Patent Application
20180286520
The disclosure generally relates to genomics and microbiology.
A microbiome can include an ecological community of commensal, symbiotic, and pathogenic microorganisms that are associated with an organism. Characterization of the human microbiome is a complex process. The human microbiome includes over 10 times more microbial cells than human cells, but characterization of the human microbiome is still in nascent stages such as due to limitations in sample processing techniques, genetic analysis techniques, and resources for processing large amounts of data. Present knowledge has clearly established the role of microbiome associations with multiple health conditions, and has become an increasingly appreciated mediator of host genetic and environmental factors on human disease development. The microbiome is suspected to play at least a partial role in a number of health/disease-related states. Further, the microbiome may mediate effects of environmental factors on human, plant, and/or animal health. Given the profound implications of the microbiome in affecting a user's health, efforts related to the characterization of the microbiome, the generation of insights from the characterization, and the generation of therapeutics configured to rectify states of dysbiosis should be pursued. Methods and systems for analyzing the microbiomes of humans and/or providing therapeutic measures based on gained insights have, however, left many questions unanswered.
As such, there is a need in the field of microbiology for a new and useful method and/or system for characterizing, monitoring, diagnosing, and/or intervening in one or more microorganism-related health conditions, such as for individualized and/or population-wide use.
The following description of the embodiments is not intended to limit the embodiments, but rather to enable any person skilled in the art to make and use.
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Embodiments of the method 100 can additionally or alternatively include one or more of: processing supplementary data associated with (e.g., informative of; describing; indicative of; correlated with, etc.) one or more sleep-related conditions S120; processing one or more biological samples associated with a user (e.g., subject, human, animal, patient; etc.) S150; determining, with one or more characterization processes, a sleep-related characterization for the user for one or more sleep-related conditions, based on a user microorganism dataset (e.g., user microorganism sequence dataset; user microbiome composition dataset; user microbiome function dataset; user microbiome features derived from the user microorganism dataset, where the user microbiome features can correspond to feature values for the microbiome features determined from one or more characterization processes; etc.) associated with a biological sample of the user S160; facilitating therapeutic intervention for the one or more sleep-related conditions for the user (e.g., based upon the sleep-related characterization and/or a therapy model; etc.) S170; monitoring effectiveness of one or more therapies and/or monitoring other suitable components (e.g., microbiome characteristics, etc.) for the user (e.g., based upon processing a series of biological samples from the user), over time (e.g., such as to assess user microbiome characteristics such as user microbiome composition features and/or functional features associated with the therapy, for the user over time, etc.) S180; and/or any other suitable processes.
Embodiments of the method 100 and/or system 200 can function to characterize (e.g., assess, evaluate, diagnose, describe, etc.) one or more sleep-related conditions (e.g., characterizing the sleep-related conditions themselves, such as determining microbiome features correlated with and/or otherwise associated with the sleep-related conditions; characterizing one or more sleep-related conditions for one or more users, such as determining propensity metrics for the one or more sleep-related conditions for the one or more users; etc.). In an example, the method 100 can include: determining a microorganism dataset associated with a set of subjects (e.g., including subjects with one or more sleep-related conditions, subjects without the one or more sleep-related conditions, etc.), based on microorganism nucleic acids from biological samples associated with the set of subjects, where the microorganism nucleic acids are associated with one or more sleep-related conditions; processing (e.g., collecting, etc.), for the set of subjects, supplementary data associated with the one or more sleep-related conditions; determining microbiome features (e.g., at least one of a set of microbiome composition features and a set of microbiome functional features, etc.) associated with the set of subjects, based on the microorganism dataset (and/or the supplementary data and/or other suitable data); generating a sleep-related characterization model (e.g., for determining sleep-related characterizations; a therapy model; etc.) based on the supplementary data and the microbiome features, where the sleep-related characterization model is associated with the one or more sleep-related conditions; determining a sleep-related characterization for a user for the one or more sleep-related conditions based on the sleep-related characterization model; and facilitating therapeutic intervention (e.g., providing a therapy to the user, etc.) for facilitating improvement of the one or more sleep-related conditions, based on the sleep-related characterization. In another example, the method 100 can include: collecting a biological sample from a user (e.g., via sample kit provision and collection, etc.), where the biological sample includes microorganism nucleic acids associated with one or more sleep-related conditions; determining a microorganism dataset associated with the user based on the microorganism nucleic acids of the biological sample (e.g., based on sample preparation and/or sequencing with the biological sample, etc.); determining user microbiome features (e.g., including at least one of user microbiome composition features and user microbiome functional features, based on the microorganism dataset, etc.), where the user microbiome features are associated with the one or more sleep-related conditions; determining a sleep-related characterization for a user for the one or more sleep-related conditions based on the user microbiome features; and facilitating therapeutic intervention in relation to a therapy for the user for facilitating improvement of the one or more sleep-related conditions, based on the sleep-related characterization.
Additionally or alternatively, embodiments of the method 100 and/or system 200 can identify microbiome features and/or other suitable data associated with (e.g., positive correlated with, negatively correlated with, etc.) one or more sleep-related conditions, such as for use as biomarkers (e.g., for diagnostic processes, for treatment processes, etc.). In examples, sleep-related characterization can be associated with at least one or more of user microbiome composition (e.g., microbiome composition diversity, etc.), microbiome function (e.g., microbiome functional diversity, etc.), and/or other suitable microbiome-related aspects. In an example, microorganism features (e.g., describing composition, function, and/or diversity of recognizable patterns, such as in relation to relative abundance of microorganisms that are present in a subject's microbiome, such as for subjects exhibiting one or more sleep-related conditions; etc.) and/or microorganism datasets (e.g., from which microbiome features can be derived, etc.) can be used for diagnostics, characterizations, therapeutic intervention facilitation, monitoring, and/or other suitable purposes, such as by using bioinformatics pipelines, analytical techniques, and/or other suitable approaches described herein. Additionally or alternatively, embodiments of the method 100 and/or system 200 can function to perform cross-condition analyses for a plurality of sleep-related conditions (e.g., performing characterization processes for a plurality of sleep-related conditions, such as determining correlation, covariance, comorbidity, and/or other suitable relationships between different sleep-related conditions, etc.), such as in the context of characterizing, diagnosing, and/or treating a user.
Additionally or alternatively, embodiments can function to facilitate therapeutic intervention (e.g., therapy selection; therapy promotion and/or provision; therapy monitoring; therapy evaluation; etc.) for one or more sleep-related conditions, such as through promotion of associated therapies (e.g., in relation to specific physiological sites gut, skin, nose, mouth, genitals, other suitable physiological sites, other collection sites; therapies determined by therapy models; etc.). Additionally or alternatively, embodiments can function to generate models (e.g., sleep-related characterization models such as for phenotypic prediction; therapy models such as for therapy determination; machine learning models such as for feature processing, etc.), such as models that can be used to characterize and/or diagnose users based on their microbiome (e.g., user microbiome features; as a clinical diagnostic; as a companion diagnostic, etc.), and/or that can be used to select and/or provide therapies for subjects in relation to one or more sleep-related conditions. Additionally or alternatively, embodiments can perform any suitable functionality described herein.
As such, data from populations of subjects (e.g., associated with one or more sleep-related conditions; positively or negatively correlated with one or more sleep-related conditions; etc.) can be used to characterize subsequent users, such as for indicating microorganism-related states of health and/or areas of improvement, and/or to facilitate therapeutic intervention (e.g., promoting one or more therapies; facilitating modulation of the composition and/or functional diversity of a user's microbiome toward one or more of a set of desired equilibrium states, such as states correlated with improved health states associated with one or more sleep-related conditions; etc.), such as in relation to one or more sleep-related conditions. Variations of the method 100 can further facilitate selection, monitoring (e.g., efficacy monitoring, etc.) and/or adjusting of therapies provided to a user, such as through collection and analysis (e.g., with sleep-related characterization models) of additional samples from a subject over time (e.g., throughout the course of a therapy regimen, through the extent of a user's experiences with sleep-related conditions; etc.), across collection sites, in addition or alternative to processing supplementary data over time (e.g., sleep-tracking data, etc.), such as for one or more sleep-related conditions. However, data from populations, subgroups, individuals, and/or other suitable entities can be used by any suitable portions of the method 100 and/or system 200 for any suitable purpose.
Embodiments of the method 100 and/or system 200 can preferably determine and/or promote (e.g., provide; present; notify regarding; etc.) characterizations and/or therapies for one or more sleep-related conditions, and/or any suitable portions of the method 100 and/or system 200 can be performed in relation to sleep-related conditions. Sleep-related conditions can include any one or more of: insomnias (e.g., short sleeping, child insomnia, etc.), hypersomnias (e.g., narcolepsy, idiopathic hypersomnia, Kleine-Levin syndrome, insufficient sleep syndrome, long sleeping, idiopathic hypersomnia, etc.), sleep-related breathing disorders (e.g., sleep apnea, obstructive sleep apnea, snoring, central sleep apnea, child sleep apnea, infant sleep apnea, sleep-related groaning, catathrenia, hypopnea syndrome, etc.), circadian rhythm-related sleep disorders (e.g., delayed sleep-wake phase, advanced sleep-wake phase, irregular sleep-wake rhythm, non-24-hour sleep-wake rhythm, shift work sleep disorders, jet lag, etc.), parasomnias (e.g., sleepwalking, confusional arousals, sleep terrors, sleep eating disorders, REM sleep behavior disorders, sleep paralysis, nightmares, bedwetting, hallucinations, exploding head syndrome, sleep talking, etc.), sleep-related movement disorders (e.g., periodic limb movements, sleep leg cramps, sleep rhythmic movement, bruxism, restless legs syndrome, etc.), dyssomnias, sleeping sickness, nocturia, somniphobia, abnormal sleep behavior disorders, daytime sleepiness disorders, comorbid conditions, and/or any other suitable conditions associated with sleep.
Additionally or alternatively, sleep-related conditions can include one or more of: diseases, symptoms, causes (e.g., triggers, etc.), disorders, associated risk (e.g., propensity scores, etc.), associated severity, behaviors (e.g., physical activity behavior; alcohol consumption; smoking behaviors; stress-related characteristics; other psychological characteristics; sickness; social behaviors; caffeine consumption; alcohol consumption; sleep habits such as sleep time, wake time, naps, length, quality, sleep phases, consistence, variance and/or other sleep behaviors; other habits; diet-related behaviors; meditation and/or other relaxation behaviors; lifestyle conditions associated with sleep-related conditions; lifestyle conditions affecting sleep quality; lifestyle conditions informative of, correlated with, indicative of, facilitative of, and/or otherwise associated with diagnosis and/or therapeutic intervention for sleep-related conditions; behaviors affecting and/or otherwise associated with sleep and/or sleep-related conditions; etc.), environmental factors (e.g., location of sleep; bed, mattress, pillow, blanket, and/or other bedding and/or sleeping environment factors; lighting; other visual factors; noise; other audio factors; touch factors; etc.), demographic-related characteristics (e.g., age, weight, race, gender, etc.), phenotypes (e.g., phenotypes measurable for a human, animal, plant, fungi body; phenotypes associated with sleep and/or other related aspects, etc.), and/or any other suitable aspects associated with sleep-related conditions. In examples, one or more sleep-related conditions can include a medical disorder affecting the sleep patterns of a human, animal, and/or other suitable entity. In an example, one or more sleep-related conditions can interfere with normal physical, mental, social and/or emotional function. In an example, one or more sleep-related conditions can be characterized by and/or diagnosed by medical interview, medical history, survey, sensor data, medical exams, data activities including and/or requiring monitoring individuals as they sleep, other supplementary data, and/or through any suitable techniques (e.g., techniques available for diagnosis for sleep-related conditions, etc.).
Embodiments of the method 100 and/or system 200 can be implemented for a single user, such as in relation to applying one or more sample handling processes and/or characterization processes for processing one or more biological samples (e.g., collected across one or more collection sites, etc.) from the user, for sleep-related characterization, facilitating therapeutic intervention, and/or for any other suitable purpose. Additionally or alternatively, embodiments can be implemented for a population of subjects (e.g., including the user, excluding the user), where the population of subjects can include subjects similar to and/or dissimilar to any other subjects for any suitable type of characteristics (e.g., in relation to sleep-related conditions, demographic characteristics, behaviors, microbiome composition and/or function, etc.); implemented for a subgroup of users (e.g., sharing characteristics, such as characteristics affecting sleep-related characterization and/or therapy determination; etc.); implemented for plants, animals, microorganisms, and/or any other suitable entities. Thus, information derived from a set of subjects (e.g., population of subjects, set of subjects, subgroup of users, etc.) can be used to provide additional insight for subsequent users. In a variation, an aggregate set of biological samples is preferably associated with and processed for a wide variety of subjects, such as including subjects of one or more of: different demographic characteristics (e.g., genders, ages, marital statuses, ethnicities, nationalities, socioeconomic statuses, sexual orientations, etc.), different sleep-related conditions (e.g., health and disease states; different genetic dispositions; etc.), different living situations (e.g., living alone, living with pets, living with a significant other, living with children, etc.), different dietary habits (e.g., omnivorous, vegetarian, vegan, sugar consumption, acid consumption, caffeine consumption, etc.), different behavioral tendencies (e.g., levels of physical activity, drug use, alcohol use, etc.), different levels of mobility (e.g., related to distance traveled within a given time period), and/or any other suitable characteristic (e.g., characteristics influencing, correlated with, and/or otherwise associated with microbiome composition and/or function, etc.). In examples, as the number of subjects increases, the predictive power of processes implemented in portions of the method 100 and/or system 200 can increase, such as in relation to characterizing subsequent users (e.g., with varying characteristics, etc.) based upon their microbiomes (e.g., in relation to different collection sites for samples for the users, etc.). However, portions of the method 100 and/or system 200 can be performed and/or configured in any suitable manner for any suitable entity or entities.
Data described herein (e.g., microbiome features, microorganism datasets, models, sleep-related characterizations, supplementary data, notifications, etc.) can be associated with any suitable temporal indicators (e.g., seconds, minutes, hours, days, weeks, etc.) including one or more: temporal indicators indicating when the data was collected (e.g., temporal indicators indicating when a sample was collected; etc.), determined, transmitted, received, and/or otherwise processed; temporal indicators providing context to content described by the data (e.g., temporal indicators associated with sleep-related characterizations, such as where the sleep-related characterization describes the sleep-related conditions and/or user microbiome status at a particular time; etc.); changes in temporal indicators (e.g., changes in sleep-related characterizations over time, such as in response to receiving a therapy; latency between sample collection, sample analysis, provision of a sleep-related characterization or therapy to a user, and/or other suitable portions of the method 100; etc.); and/or any other suitable indicators related to time.
Additionally or alternatively, parameters, metrics, inputs, outputs, and/or other suitable data can be associated with value types including: scores (e.g., sleep-related condition propensity scores; feature relevance scores; correlation scores, covariance scores, microbiome diversity scores, severity scores; etc.), individual values (e.g., individual sleep-related condition scores, such as condition propensity scores, for different collection sites, etc.), aggregate values, (e.g., overall scores based on individual microorganism-related scores for different collection sites, etc.), binary values (e.g., presence or absence of a microbiome feature; presence or absence of a sleep-related condition; etc.), relative values (e.g., relative taxonomic group abundance, relative microbiome function abundance, relative feature abundance, etc.), classifications (e.g., sleep-related condition classifications and/or diagnoses for users; feature classifications; behavior classifications; demographic characteristic classifications; etc.), confidence levels (e.g., associated with microorganism sequence datasets; with microbiome diversity scores; with other sleep-related characterizations; with other outputs; etc.), identifiers, values along a spectrum, and/or any other suitable types of values. Any suitable types of data described herein can be used as inputs (e.g., for different analytical techniques, models, and/or other suitable components described herein), generated as outputs (e.g., of different analytical techniques, models, etc.), and/or manipulated in any suitable manner for any suitable components associated with the method 100 and/or system 200.
One or more instances and/or portions of the method 100 and/or processes described herein can be performed asynchronously (e.g., sequentially), concurrently (e.g., parallel data processing; concurrent cross-condition analysis; multiplex sample processing, such as multiplex amplification of microorganism nucleic acid fragments corresponding to target sequences associated with sleep-related conditions; performing sample processing and analysis for substantially concurrently evaluating a panel of sleep-related conditions; computationally determining microorganism datasets, microbiome features, and/or characterizing sleep-related conditions in parallel for a plurality of users; such as concurrently on different threads for parallel computing to improve system processing ability; etc.), in temporal relation (e.g., substantially concurrently with, in response to, serially, prior to, subsequent to, etc.) to a trigger event (e.g., performance of a portion of the method 100), and/or in any other suitable order at any suitable time and frequency by and/or using one or more instances of the system 200, components, and/or entities described herein. In an example, the method 100 can include generating a microorganism dataset based on processing microorganism nucleic acids of one or more biological samples with a bridge amplification substrate of a next generation sequencing platform (and/or other suitable sequencing system) of a sample handling system, and determining microbiome features and microbiome functional diversity features at computing devices operable to communicate with the next generation sequencing platform. However, the method 100 and/or system 200 can be configured in any suitable manner.
Microbiome analysis can enable accurate and/or efficient characterization and/or therapy provision (e.g., according to portions of the method 100, etc.) for sleep-related conditions caused by, correlated with, and/or otherwise associated with microorganisms. Specific examples of the technology can overcome several challenges faced by conventional approaches in characterizing a sleep-related conditions and/or facilitating therapeutic intervention. First, conventional approaches can require patients to visit one or more care providers to receive a characterization and/or a therapy recommendation for a sleep-related condition (e.g., through diagnostic medical procedures such as in-hospital sleep-tracking; etc.), which can amount to inefficiencies and/or health-risks associated with the amount of time elapsed before diagnosis and/or treatment, with inconsistency in healthcare quality, and/or with other aspects of care provider visitation. Second, conventional genetic sequencing and analysis technologies for human genome sequencing can be incompatible and/or inefficient when applied to the microbiome (e.g., where the human microbiome can include over 10 times more microbial cells than human cells; where viable analytical techniques and the means of leveraging the analytical techniques can differ; where optimal sample processing techniques can differ, such as for reducing amplification bias; where different approaches to sleep-related characterizations can be employed; where the types of conditions and correlations can differ; where causes of the associated conditions and/or viable therapies for the associated conditions can differ; where sequence reference databases can differ; where the microbiome can vary across different body regions of the user such as at different collection sites; etc.). Third, the onset of sequencing technologies (e.g., next-generation sequencing, associated technologies, etc.) has given rise to technological issues (e.g., data processing and analysis issues for the plethora of generated sequence data; issues with processing a plurality of biological samples in a multiplex manner; information display issues; therapy prediction issues; therapy provision issues, etc.) that would not exist but for the unprecedented advances in speed and data generation associated with sequencing genetic material. Specific examples of the method 100 and/or system 200 can confer technologically-rooted solutions to at least the challenges described above.
First, specific examples of the technology can transform entities (e.g., users, biological samples, therapy facilitation systems including medical devices, etc.) into different states or things. For example, the technology can transform a biological sample into components able to be sequenced and analyzed to generate microorganism dataset and/or microbiome features usable for characterizing users in relation to one or more sleep-related conditions (e.g., such as through use of next-generation sequencing systems, multiplex amplification operations; etc.). In another example, the technology can identify, promote (e.g., present, recommend, etc.), discourage, and/or provide therapies (e.g., personalized therapies based on a sleep-related characterization; etc.) and/or otherwise facilitate therapeutic intervention (e.g., facilitating modification of a user's microbiome composition, microbiome functionality, etc.), which can prevent and/or ameliorate one or more sleep-related conditions, thereby transforming the microbiome and/or health of the patient (e.g., improving a health state associated with a sleep-related condition; etc.). In another example, the technology can transform microbiome composition and/or function at one or more different physiological sites of a user (e.g., one or more different collection sites, etc.), such as targeting and/or transforming microorganisms associated with a gut, nose, skin, mouth, and/or genitals microbiome. In another example, the technology can control therapy facilitation systems (e.g., dietary systems; automated medication dispensers; behavior modification systems; diagnostic systems; disease therapy facilitation systems; etc.) to promote therapies (e.g., by generating control instructions for the therapy facilitation system to execute; etc.), thereby transforming the therapy facilitation system.
Second, specific examples of the technology can confer improvements in computer-related technology (e.g., improving computational efficiency in storing, retrieving, and/or processing microorganism-related data for sleep-related conditions; computational processing associated with biological sample processing, etc.) such as by facilitating computer performance of functions not previously performable. For example, the technology can apply a set of analytical techniques in a non-generic manner to non-generic microorganism datasets and/or microbiome features (e.g., that are recently able to be generated and/or are viable due to advances in sample processing techniques and/or sequencing technology, etc.) for improving sleep-related characterizations and/or facilitating therapeutic intervention for sleep-related conditions.
Third, specific examples of the technology can confer improvements in processing speed, sleep-related characterization, accuracy, microbiome-related therapy determination and promotion, and/or other suitable aspects in relation to sleep-related conditions. For example, the technology can leverage non-generic microorganism datasets to determine, select, and/or otherwise process microbiome features of particular relevance to one or more sleep-related conditions (e.g., processed microbiome features relevant to a sleep-related condition; cross-condition microbiome features with relevance to a plurality of sleep-related conditions, etc.), which can facilitate improvements in accuracy (e.g., by using the most relevant microbiome features; by leveraging tailored analytical techniques; etc.), processing speed (e.g., by selecting a subset of relevant microbiome features; by performing dimensionality reduction techniques; by leveraging tailored analytical techniques; etc.), and/or other computational improvements in relation to phenotypic prediction (e.g., indications of the sleep-related conditions, etc.), other suitable characterizations, therapeutic intervention facilitation, and/or other suitable purposes. In a specific example, the technology can apply feature-selection rules (e.g., microbiome feature-selection rules for composition, function; for supplemental features extracted from supplementary datasets; etc.) to select an optimized subset of features (e.g., microbiome functional features relevant to one or more sleep-related conditions; microbiome composition diversity features such as reference relative abundance features indicative of healthy, presence, absence, and/or other suitable ranges of taxonomic groups associated with sleep-related conditions; user relative abundance features that can be compared to reference relative abundance features correlated with sleep-related conditions and/or therapy responses; etc.) out of a vast potential pool of features (e.g., extractable from the plethora of microbiome data such as sequence data; identifiable by univariate statistical tests; etc.) for generating, applying, and/or otherwise facilitating characterization and/or therapies (e.g., through models, etc.). The potential size of microbiomes (e.g., human microbiomes, animal microbiomes, etc.) can translate into a plethora of data, giving rise to questions of how to process and analyze the vast array of data to generate actionable microbiome insights in relation to sleep-related conditions. However, the feature-selection rules and/or other suitable computer-implementable rules can enable one or more of: shorter generation and execution times (e.g., for generating and/or applying models; for determining sleep-related characterizations and/or associated therapies; etc.); optimized sample processing techniques (e.g., improving transformation of microorganism nucleic acids from biological samples through using primer types, other biomolecules, and/or other sample processing components identified through computational analysis of taxonomic groups, sequences, and/or other suitable data associated with sleep-related conditions, such as while optimizing for improving specificity, reducing amplification bias, and/or other suitable parameters; etc.); model simplification facilitating efficient interpretation of results; reduction in overfitting; network effects associated with generating, storing, and applying sleep-related characterizations for a plurality of users over time in relation to sleep-related conditions (e.g., through collecting and processing an increasing amount of microbiome-related data associated with an increasing number of users to improve predictive power of the sleep-related characterizations and/or therapy determinations; etc.); improvements in data storage and retrieval (e.g., storing and/or retrieving sleep-related characterization models; storing specific models such as in association with different users and/or sets of users, with different sleep-related conditions; storing microorganism datasets in association with user accounts; storing therapy monitoring data in association with one or more therapies and/or users receiving the therapies; storing features, sleep-related characterizations, and/or other suitable data in association with a user, set of users, and/or other entities to improve delivery of personalized characterizations and/or treatments for the sleep-related conditions, etc.), and/or other suitable improvements to technological areas.
Fourth, specific examples of the technology can amount to an inventive distribution of functionality across a network including a sample handling system, a sleep-related characterization system, and a plurality of users, where the sample handling system can handle substantially concurrent processing of biological samples (e.g., in a multiplex manner) from the plurality of users, which can be leveraged by the sleep-related characterization system in generating personalized characterizations and/or therapies (e.g., customized to the user's microbiome such as in relation to the user's dietary behavior, probiotics-associated behavior, medical history, demographic characteristics, other behaviors, preferences, etc.) for sleep-related conditions.
Fifth, specific examples of the technology can improve the technical fields of at least genomics, microbiology, microbiome-related computation, diagnostics, therapeutics, microbiome-related digital medicine, digital medicine generally, modeling, and/or other relevant fields. In an example, the technology can model and/or characterize different sleep-related conditions, such as through computational identification of relevant microorganism features (e.g., which can act as biomarkers to be used in diagnoses, facilitating therapeutic intervention, etc.) for sleep-related conditions. In another example, the technology can perform cross-condition analysis to identify and evaluate cross-condition microbiome features associated with (e.g., shared across, correlated across, etc.) a plurality of a sleep-related conditions (e.g., diseases, phenotypes, etc.). Such identification and characterization of microbiome features can facilitate improved health care practices (e.g., at the population and individual level, such as by facilitating diagnosis and therapeutic intervention, etc.), by reducing risk and prevalence of comorbid and/or multi-morbid sleep-related conditions (e.g., which can be associated with environmental factors, and thereby associated with the microbiome, etc.).
Sixth, the technology can leverage specialized computing devices (e.g., devices associated with the sample handling system, such as next-generation sequencing systems; sleep-related characterization systems; therapy facilitation systems; etc.) in performing suitable portions associated with the method 100 and/or system 200.
Specific examples of the technology can, however, provide any other suitable benefit(s) in the context of using non-generalized computer systems for sleep-related characterization, microbiome modulation, and/or for performing other suitable portions of the method 100.
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The handling system 210 of the system 200 can function to receive and/or process (e.g., fragment, amplify, sequence, generate associated datasets, etc.) biological samples to transform microorganism nucleic acids and/or other components of the biological samples into data (e.g., genetic sequences that can be subsequently aligned and analyzed; microorganism datasets; etc.) for facilitating generation of sleep-related characterizations and/or therapeutic intervention. The handling system 210 can additionally or alternatively function to provide sample kits 250 (e.g., including sample containers, instructions for collecting samples from one or more collection sites, etc.) to a plurality of users (e.g., in response to a purchase order for a sample kit 250), such as through a mail delivery system. The handling system 210 can include one or more sequencing systems 215 (e.g., a next-generation sequencing systems, sequencing systems for targeted amplicon sequencing, metatranscriptomic sequencing, metagenomic sequencing, sequencing-by-synthesis techniques, capillary sequencing technique, Sanger sequencing, pyrosequencing techniques, nanopore sequencing techniques, etc.) for sequencing one or more biological samples (e.g., sequencing microorganism nucleic acids from the biological samples, etc.), such as in generating microorganism data (e.g., microorganism sequence data, other data for microorganism datasets, etc.). The handling system 210 can additionally or alternatively include a library preparation system operable to automatically prepare biological samples (e.g., fragment and amplify using primers compatible with genetic targets associated with the sleep-related condition) in a multiplex manner to be sequenced by a sequencing system; and/or any suitable components. The handling system can perform any suitable sample processing techniques described herein. However, the handling system 210 and associated components can be configured in any suitable manner.
The sleep-related characterization system 220 of the system 200 can function to determine, analyze, characterize, and/or otherwise process microorganism datasets (e.g., based on processed biological samples leading to microorganism genetic sequences; alignments to reference sequences; etc.), microbiome features (e.g., individual variables; groups of variables; features relevant for phenotypic prediction, for statistical description; variables associated with a sample obtained from an individual; variables associated with sleep-related conditions; variables describing fully or partially, in relative or absolute quantities the sample's microbiome composition and/or functionality; etc.), models, and/or other suitable data for facilitating sleep-related characterization and/or therapeutic intervention. In examples, the sleep-related characterization system 220 can identify data associated with the information of the features that statistically describe the differences between samples associated with one or more sleep-related conditions (e.g., samples associated with presence, absence, risk of, propensity for, and/or other aspects related to sleep-related conditions etc.), such as where the differing analyses can provide complementing views into the features differentiating the different samples (e.g., differentiating the subgroups associated with presence or absence of a condition, etc.). In a specific example, individual predictors, a specific biological process, and/or statistically inferred latent variables can provide complementary information at different levels of data complexity to facilitate varied downstream opportunities in relation to characterization, diagnosis, and/or treatment. In another specific example, the sleep-related characterization system 220 process supplementary data for performing one or more characterization processes.
The sleep-related characterization system 220 can include, generate, apply, and/or otherwise process sleep-related characterization models, which can include any one or more of sleep-related condition models for characterizing one or more sleep-related conditions (e.g., determining propensity of one or more sleep-related conditions for one or more users, etc.), therapy models for determining therapies, and/or any other suitable models for any suitable purposes associated with the system 200 and/or method 100. In a specific example, the sleep-related characterization system 220 can generate and/or apply a therapy model (e.g., based on cross-condition analyses, etc.) for identifying and/or characterizing a therapy used to treat one or more sleep-related conditions. Different sleep-related characterization models (e.g., different combinations of sleep-related characterization models; different models applying different analytical techniques; different inputs and/or output types; applied in different manners such as in relation to time and/or frequency; etc.) can be applied (e.g., executed, selected, retrieves, stored, etc.) based on one or more of: sleep-related conditions (e.g., using different sleep-related characterization models depending on the sleep-related condition or conditions being characterized, such as where different sleep-related characterization models possess differing levels of suitability for processing data in relation to different sleep-related conditions and/or combinations of conditions, etc.), users (e.g., different sleep-related characterization models based on different user data and/or characteristics, demographic characteristics, genetics, environmental factors, etc.), sleep-related characterizations (e.g., different sleep-related characterization models for different types of characterizations, such as a therapy-related characterization versus a diagnosis-related characterization, such as for identifying relevant microbiome composition versus determining a propensity score for a sleep-related condition; etc.), therapies (e.g., different sleep-related characterization models for monitoring efficacy of different therapies, etc.), collection sites (e.g., different sleep-related characterization models for processing microorganism datasets corresponding to biological samples from different collection sites; etc.), supplementary data, and/or any other suitable components. In examples, different sleep-related characterization models can be tailored to different types of inputs, outputs, sleep-related characterizations, sleep-related conditions (e.g., different phenotypic measures that need to be characterized), and/or any other suitable entities. However, sleep-related characterization models can be tailored and/or used in any suitable manner for facilitating sleep-related characterization and/or therapeutic intervention.
Sleep-related characterization models, other models, other components of the system 200, and/or suitable portions of the method 100 (e.g., characterization processes, determining microbiome features, determining sleep-related characterizations, etc.) can employ analytical techniques including any one or more of: univariate statistical tests, multivariate statistical tests, dimensionality reduction techniques, artificial intelligence approaches (e.g., machine learning approaches, etc.), performing pattern recognition on data (e.g., identifying correlations between sleep-related conditions and microbiome features; etc.), fusing data from multiple sources (e.g., generating characterization models based on microbiome data and/or supplementary data from a plurality of users associated with one or more sleep-related conditions, such as based on microbiome features extracted from the data; etc.), combination of values (e.g., averaging values, etc.), compression, conversion (e.g., digital-to-analog conversion, analog-to-digital conversion), performing statistical estimation on data (e.g. ordinary least squares regression, non-negative least squares regression, principal components analysis, ridge regression, etc.), wave modulation, normalization, updating (e.g., of characterization models and/or therapy models based on processed biological samples over time; etc.), ranking (e.g., microbiome features; therapies; etc.), weighting (e.g., microbiome features; etc.), validating, filtering (e.g., for baseline correction, data cropping, etc.), noise reduction, smoothing, filling (e.g., gap filling), aligning, model fitting, binning, windowing, clipping, transformations, mathematical operations (e.g., derivatives, moving averages, summing, subtracting, multiplying, dividing, etc.), data association, multiplexing, demultiplexing, interpolating, extrapolating, clustering, image processing techniques, other signal processing operations, other image processing operations, visualizing, and/or any other suitable processing operations. Artificial intelligence approaches can include any one or more of: supervised learning (e.g., using logistic regression, using back propagation neural networks, using random forests, decision trees, etc.), unsupervised learning (e.g., using an Apriori algorithm, using K-means clustering), semi-supervised learning, a deep learning algorithm (e.g., neural networks, a restricted Boltzmann machine, a deep belief network method, a convolutional neural network method, a recurrent neural network method, stacked auto-encoder method, etc.) reinforcement learning (e.g., using a Q-learning algorithm, using temporal difference learning), a regression algorithm (e.g., ordinary least squares, logistic regression, stepwise regression, multivariate adaptive regression splines, locally estimated scatterplot smoothing, etc.), an instance-based method (e.g., k-nearest neighbor, learning vector quantization, self-organizing map, etc.), a regularization method (e.g., ridge regression, least absolute shrinkage and selection operator, elastic net, etc.), a decision tree learning method (e.g., classification and regression tree, iterative dichotomiser 3, C4.5, chi-squared automatic interaction detection, decision stump, random forest, multivariate adaptive regression splines, gradient boosting machines, etc.), a Bayesian method (e.g., naïve Bayes, averaged one-dependence estimators, Bayesian belief network, etc.), a kernel method (e.g., a support vector machine, a radial basis function, a linear discriminate analysis, etc.), a clustering method (e.g., k-means clustering, expectation maximization, etc.), an associated rule learning algorithm (e.g., an Apriori algorithm, an Eclat algorithm, etc.), an artificial neural network model (e.g., a Perceptron method, a back-propagation method, a Hopfield network method, a self-organizing map method, a learning vector quantization method, etc.), an ensemble method (e.g., boosting, boostrapped aggregation, AdaBoost, stacked generalization, gradient boosting machine method, random forest method, etc.), and/or any suitable artificial intelligence approach. However, data processing can be employed in any suitable manner.
The sleep-related characterization system 220 can preferably perform cross-condition analyses for a plurality of sleep-related conditions (e.g., generating multi-condition characterizations based on outputs of different sleep-related characterization models, such as multi-condition microbiome features; etc.). For example, the sleep-related characterization system can characterize relationships between sleep-related conditions based on microorganism data, microbiome features, and/or other suitable microbiome characteristics of users associated with (e.g., diagnosed with, characterized by, etc.) a plurality of sleep-related conditions. In a specific example, cross-condition analyses can be performed based on characterizations for individual sleep-related conditions (e.g., outputs from sleep-related characterization models for individual sleep-related conditions, etc.). Cross-condition analyses can include identification of condition-specific features (e.g., associated exclusively with a single sleep-related condition, etc.), multi-condition features (e.g., associated with two or more sleep-related conditions, etc.), and/or any other suitable types of features. Cross-condition analyses can include determination of parameters informing correlation, concordance, and/or other similar parameters describing relationships between two or more sleep-related conditions, such as by evaluating different pairs of sleep-related conditions. However, the sleep-related characterization system and/or other suitable components can be configured in any suitable manner to facilitate cross-condition analyses (e.g., applying analytical techniques for cross-condition analysis purposes; generating cross-condition characterizations, etc.).
The sleep-related characterization system 220 preferably includes a remote computing system (e.g., for applying sleep-related characterization models, etc.), but can additionally or alternatively include any suitable computing systems (e.g., local computing systems, user devices, handling system components, etc.). However, the sleep-related characterization system 220 can be configured in any suitable manner.
The therapy facilitation system 230 of the system 200 can function to facilitate therapeutic intervention (e.g., promote one or more therapies, etc.) for one or more sleep-related conditions (e.g., facilitating modulation of a user microbiome composition and functional diversity for improving a state of the user in relation to one or more sleep-related conditions, etc.). The therapy facilitation system 230 can facilitate therapeutic intervention for any number of sleep-related conditions associated with any number of collection sites, such as based on multi-site characterizations, multi-condition characterizations, other characterizations, and/or any other suitable data. The therapy facilitation system 230 can include any one or more of: a communications system (e.g., to communicate therapy recommendations, selections, discouragements, and/or other suitable therapy-related information to a computing device (e.g., user device and/or care provider device; mobile device; smart phone; desktop computer; at a website, web application, and/or mobile application accessed by the computing device; etc.); to enable telemedicine between a care provider and a subject in relation to a sleep-related condition; etc.), an application executable on a user device (e.g., indicating microbiome composition and/or functionality for a user; etc.), a medical device (e.g., a biological sampling device, such as for collecting samples from different collection sites; medication provision devices; surgical systems; etc.), a user device (e.g., biometric sensors), and/or any other suitable component. One or more therapy facilitation systems 230 can be controllable, communicable with, and/or otherwise associated with the sleep-related characterization system 220. For example, the sleep-related characterization system 220 can generate characterizations of one or more sleep-related conditions for the therapy facilitation system 230 to present (e.g., transmit, communicate, etc.) to a corresponding user (e.g., at an interface 240, etc.). In another example, the therapy facilitation system 230 can update and/or otherwise modify an application and/or other software of a device (e.g., user smartphone) to promote a therapy (e.g., promoting, at a to-do list application, lifestyle changes for improving a user state associated with one or more sleep-related conditions, etc.). However, the therapy facilitation system 230 can be configured in any other manner.
As shown in
While the components of the system 200 are generally described as distinct components, they can be physically and/or logically integrated in any manner. For example, a computing system (e.g., a remote computing system, a user device, etc.) can implement portions and/or all of the sleep-related characterization system 220 (e.g., apply a microbiome-related condition model to generate a characterization of sleep-related conditions for a user, etc.) and the therapy facilitation system 230 (e.g., facilitate therapeutic intervention through presenting insights associated with microbiome composition and/or function; presenting therapy recommendations and/or information; scheduling daily events at a calendar application of the smartphone to notify the user in relation to therapies for improving sleep-related, etc.). However, the functionality of the system 200 can be distributed in any suitable manner amongst any suitable system components. However, the components of the system 200 can be configured in any suitable manner
Block S110 can include determining a microorganism dataset (e.g., microorganism sequence dataset, microbiome composition diversity dataset such as based upon a microorganism sequence dataset, microbiome functional diversity dataset such as based upon a microorganism sequence dataset, etc.) associated with a set of subjects S110. Block S110 can function to process biological samples (e.g., an aggregate set of biological samples associated with a population of subjects, a subpopulation of subjects, a subgroup of subjects sharing a demographic characteristic and/or other suitable characteristics; a user biological sample; etc.), in order to determine compositional, functional, pharmacogenomics, and/or other suitable aspects associated with the corresponding microbiomes, such as in relation to one or more sleep-related conditions. Compositional and/or functional aspects can include one or more of aspects at the microorganism level (and/or other suitable granularity), including parameters related to distribution of microorganisms across different groups of kingdoms, phyla, classes, orders, families, genera, species, subspecies, strains, and/or any other suitable infraspecies taxon (e.g., as measured in total abundance of each group, relative abundance of each group, total number of groups represented, etc.). Compositional and/or functional aspects can also be represented in terms of operational taxonomic units (OTUs). Compositional and/or functional aspects can additionally or alternatively include compositional aspects at the genetic level (e.g., regions determined by multilocus sequence typing, 16S sequences, 18S sequences, ITS sequences, other genetic markers, other phylogenetic markers, etc.). Compositional and functional aspects can include the presence or absence or the quantity of genes associated with specific functions (e.g. enzyme activities, transport functions, immune activities, etc.). Outputs of Block 110 can thus be used to facilitate determination of microbiome features (e.g., generation of a microorganism sequence dataset usable for identifying microbiome features; etc.) for the characterization process of Block S130 and/or other suitable portions of the method 100 (e.g., where Block S110 can lead to outputs of microbiome composition datasets, microbiome functional datasets, and/or other suitable microorganism datasets from which microbiome features can be extracted, etc.), where the features can be microorganism-based (e.g., presence of a genus of bacteria), genetic-based (e.g., based upon representation of specific genetic regions and/or sequences), functional-based (e.g., presence of a specific catalytic activity), and/or any other suitable microbiome features.
In a variation, Block S110 can include assessment and/or processing based upon phylogenetic markers (e.g., for generating microorganism datasets, etc.) derived from bacteria and/or archaea in relation to gene families associated with one or more of: ribosomal protein S2, ribosomal protein S3, ribosomal protein S5, ribosomal protein S7, ribosomal protein S8, ribosomal protein S9, ribosomal protein S10, ribosomal protein S11, ribosomal protein S12/S23, ribosomal protein S13, ribosomal protein S15P/S13e, ribosomal protein S17, ribosomal protein S19, ribosomal protein L1, ribosomal protein L2, ribosomal protein L3, ribosomal protein L4/L1e, ribosomal protein L5, ribosomal protein L6, ribosomal protein L10, ribosomal protein L11, ribosomal protein L14b/L23e, ribosomal protein L15, ribosomal protein L16/L10E, ribosomal protein L18P/L5E, ribosomal protein L22, ribosomal protein L24, ribosomal protein L25/L23, ribosomal protein L29, translation elongation factor EF-2, translation initiation factor IF-2, metalloendopeptidase, ffh signal recognition particle protein, phenylalanyl-tRNA synthetase beta subunit, phenylalanyl-tRNA synthetase alpha subunit, tRNA pseudouridine synthase B, Porphobilinogen deaminase, ribosomal protein L13, phosphoribosylformylglycinamidine cyclo-ligase, and ribonuclease HII. Additionally or alternatively, markers can include target sequences (e.g., sequences associated with a microorganism taxonomic group; sequences associated with functional aspects; sequences correlated with sleep-related conditions; sequences indicative of user responsiveness to different therapies; sequences that are invariant across a population and/or any suitable set of subjects, such as to facilitate multiplex amplification using a primer type sharing a primer sequence; conserved sequences; sequences including mutations, polymorphisms; nucleotide sequences; amino acid sequences; etc.), proteins (e.g., serum proteins, antibodies, etc.), peptides, carbohydrates, lipids, other nucleic acids, whole cells, metabolites, natural products, genetic predisposition biomarkers, diagnostic biomarkers, prognostic biomarkers, predictive biomarkers, other molecular biomarkers, gene expression markers, imaging biomarkers, and/or other suitable markers. However, markers can include any other suitable marker(s) associated with microbiome composition, microbiome functionality, and/or sleep-related conditions.
Characterizing the microbiome composition and/or functional aspects for each of the aggregate set of biological samples thus preferably includes a combination of sample processing techniques (e.g., wet laboratory techniques; as shown in
In variations, sample processing in Block 110 can include any one or more of: lysing a biological sample, disrupting membranes in cells of a biological sample, separation of undesired elements (e.g., RNA, proteins) from the biological sample, purification of nucleic acids (e.g., DNA) in a biological sample, amplification of nucleic acids from the biological sample, further purification of amplified nucleic acids of the biological sample, and sequencing of amplified nucleic acids of the biological sample. In an example, Block 110 can include: collecting biological samples from a set of users (e.g., biological samples collected by the user with a sampling kit including a sample container, etc.), where the biological samples include microorganism nucleic acids associated with the sleep-related condition (e.g., microorganism nucleic acids including target sequences correlated with a sleep-related condition; etc.). In another example, Block S110 can include providing a set of sampling kits to a set of users, each sampling kit of the set of sampling kits including a sample container (e.g., including pre-processing reagents, such as lysing reagents; etc.) operable to receive a biological sample from a user of the set of users.
In variations, lysing a biological sample and/or disrupting membranes in cells of a biological sample preferably includes physical methods (e.g., bead beating, nitrogen decompression, homogenization, sonication), which omit certain reagents that produce bias in representation of certain bacterial groups upon sequencing. Additionally or alternatively, lysing or disrupting in Block 110 can involve chemical methods (e.g., using a detergent, using a solvent, using a surfactant, etc.). Additionally or alternatively, lysing or disrupting in Block 110 can involve biological methods. In variations, separation of undesired elements can include removal of RNA using RNases and/or removal of proteins using proteases. In variations, purification of nucleic acids can include one or more of: precipitation of nucleic acids from the biological samples (e.g., using alcohol-based precipitation methods), liquid-liquid based purification techniques (e.g., phenol-chloroform extraction), chromatography-based purification techniques (e.g., column adsorption), purification techniques involving use of binding moiety-bound particles (e.g., magnetic beads, buoyant beads, beads with size distributions, ultrasonically responsive beads, etc.) configured to bind nucleic acids and configured to release nucleic acids in the presence of an elution environment (e.g., having an elution solution, providing a pH shift, providing a temperature shift, etc.), and any other suitable purification techniques.
In variations, amplification of purified nucleic acids can include one or more of: polymerase chain reaction (PCR)-based techniques (e.g., solid-phase PCR, RT-PCR, qPCR, multiplex PCR, touchdown PCR, nanoPCR, nested PCR, hot start PCR, etc.), helicase-dependent amplification (HDA), loop mediated isothermal amplification (LAMP), self-sustained sequence replication (3SR), nucleic acid sequence based amplification (NASBA), strand displacement amplification (SDA), rolling circle amplification (RCA), ligase chain reaction (LCR), and any other suitable amplification technique. In amplification of purified nucleic acids, the primers used are preferably selected to prevent or minimize amplification bias, as well as configured to amplify nucleic acid regions/sequences (e.g., of the 16S region, the 18S region, the ITS region, etc.) that are informative taxonomically, phylogenetically, for diagnostics, for formulations (e.g., for probiotic formulations), and/or for any other suitable purpose. Thus, universal primers (e.g., a F27-R338 primer set for 16S RNA, a F515-R806 primer set for 16S RNA, etc.) configured to avoid amplification bias can be used in amplification. Additionally or alternatively include incorporated barcode sequences and/or UMIs specific to biological samples, to users, to sleep-related conditions, to taxa, to target sequences, and/or to any other suitable components, which can facilitate a post-sequencing identification process (e.g., for mapping sequence reads to microbiome composition and/or microbiome function aspects; etc.). Primers used in variations of Block 110 can additionally or alternatively include adaptor regions configured to cooperate with sequencing techniques involving complementary adaptors (e.g., Illumina Sequencing). Additionally or alternatively, Block S110 can implement any other step configured to facilitate processing (e.g., using a Nextera kit). In a specific example, performing amplification and/or sample processing operations can be in a multiplex manner (e.g., for a single biological sample, for a plurality of biological samples across multiple users; etc.). In another specific example, performing amplification can include normalization steps to balance libraries and detect all amplicons in a mixture independent of the amount of starting material, such as 3 step PCR, bead based normalization, and/or other suitable techniques.
In variations, sequencing of purified nucleic acids can include methods involving targeted amplicon sequencing, metatranscriptomic sequencing, and/or metagenomic sequencing, implementing techniques including one or more of: sequencing-by-synthesis techniques (e.g., Illumina sequencing), capillary sequencing techniques (e.g., Sanger sequencing), pyrosequencing techniques, and nanopore sequencing techniques (e.g., using an Oxford Nanopore technique).
In a specific example, amplification and sequencing of nucleic acids from biological samples of the set of biological samples includes: solid-phase PCR involving bridge amplification of DNA fragments of the biological samples on a substrate with oligo adapters, where amplification involves primers having a forward index sequence (e.g., corresponding to an Illumina forward index for MiSeq/NextSeq/HiSeq platforms), a forward barcode sequence, a transposase sequence (e.g., corresponding to a transposase binding site for MiSeq/NextSeq/HiSeq platforms), a linker (e.g., a zero, one, or two-base fragment configured to reduce homogeneity and improve sequence results), an additional random base, UMIs, a sequence for targeting a specific target region (e.g., 16S region, 18S region, ITS region), a reverse index sequence (e.g., corresponding to an Illumina reverse index for MiSeq/HiSeq platforms), and a reverse barcode sequence. In the specific example, sequencing can include Illumina sequencing (e.g., with a HiSeq platform, with a MiSeq platform, with a NextSeq platform, etc.) using a sequencing-by-synthesis technique. In another specific example, the method 100 can include: identifying one or more primer types compatible with one or more genetic targets associated with one or more sleep-related conditions (e.g., a biomarker of the one or more sleep-related conditions; positively correlated with; negatively correlated with; causative of; etc.); determining a microorganism dataset (e.g., microorganism sequence dataset; such as with a next-generation sequencing system; etc.) for one or more users (e.g., set of subjects) based on the one or more primer types (e.g., based on primers corresponding to the one or more primer types, and on the microorganism nucleic acids included in collected biological samples, etc.), such as through fragmenting the microorganism nucleic acids, and/or performing a singleplex amplification process and/or a multiplex amplification process for the fragmented microorganism nucleic acids based on the one or more identified primer types (e.g., primers corresponding to the primer types, etc.) compatible with the one or more genetic targets associated with the sleep-related condition; and/or promoting (e.g., providing), based on a sleep-related characterization derived from the a microorganism dataset a therapy for the user condition (e.g., enabling selective modulation of a microbiome of the user in relation to at least one of a population size of a desired taxon and a desired microbiome function, etc.). In a specific example, the biological samples can correspond to a set of collection sites including at least one of a gut site, a skin site, a nose site, a mouth site, and a genitals site, and where determining a microorganism dataset (e.g., microorganism sequence dataset, etc.) can include identifying a first primer type compatible with a first genetic target associated with one or more sleep-related conditions and a first collection site of the set of collection sites; identifying a second primer type compatible with a second genetic target associated with the one or more sleep-related conditions and a second collection site of the set of collection sites; and generating the microorganism dataset for the set of subjects based on the microorganism nucleic acids, the first primers corresponding to the first primer type, and second primers corresponding to the second primer type. In the specific example, the first collection site type can include the gut site (e.g., which can be evaluated through stool samples, etc.), where determining the microorganism dataset can include determining at least one of a metagenomic library and a metatranscriptomic library based on a subset of the microorganism nucleic acids and the first primers, and where determining the at least one of the set of microbiome composition features and the set of microbiome functional features can include determining the at least one of the set of microbiome composition features and the set of microbiome functional features based on the at least one of the metagenomic library and the metatranscriptomic library. However, processing metagenomic libraries and/or metatranscriptomic libraries (e.g., for any suitable portions of the method 100 and/or system 200) can be performed in any suitable manner.
In variations, primers (e.g., of a primer type corresponding to a primer sequence; etc.) used in Block S110 and/or other suitable portions of the method 100 can include primers associated with protein genes (e.g., coding for conserved protein gene sequences across a plurality of taxa, such as to enable multiplex amplification for a plurality of targets and/or taxa; etc.). Primers can additionally or alternatively be associated with sleep-related conditions (e.g., primers compatible with genetic targets including microorganism sequence biomarkers for microorganisms correlated with sleep-related conditions; etc.), microbiome composition features (e.g., identified primers compatible with a genetic target corresponding to microbiome composition features associated with a group of taxa correlated with a sleep-related condition; genetic sequences from which relative abundance features are derived etc.), functional diversity features, supplementary features, and/or other suitable features and/or data. Primers (and/or other suitable molecules, markers, and/or biological material described herein) can possess any suitable size (e.g., sequence length, number of base pairs, conserved sequence length, variable region length, etc.). Additionally or alternatively, any suitable number of primers can be used in sample processing for performing characterizations (e.g., sleep-related characterizations; etc.), improving sample processing (e.g., through reducing amplification bias, etc.), and/or for any suitable purposes. The primers can be associated with any suitable number of targets, sequences, taxa, conditions, and/or other suitable aspects. Primers used in Block 110 and/or other suitable portions of the method 100 can be selected through processes described in Block 110 (e.g., primer selection based on parameters used in generating the taxonomic database) and/or any other suitable portions of the method 100. Additionally or alternatively, primers (and/or processes associated with primers) can include and/or be analogous to that described in U.S. application Ser. No. 14/919,614, filed 21 Oct. 2015, which is herein incorporated in its entirety by this reference. However, identification and/or usage of primers can be configured in any suitable manner.
Some variations of sample processing can include further purification of amplified nucleic acids (e.g., PCR products) prior to sequencing, which functions to remove excess amplification elements (e.g., primers, dNTPs, enzymes, salts, etc.). In examples, additional purification can be facilitated using any one or more of: purification kits, buffers, alcohols, pH indicators, chaotropic salts, nucleic acid binding filters, centrifugation, and/or any other suitable purification technique.
In variations, computational processing in Block 110 can include any one or more of: identification of microbiome-derived sequences (e.g., as opposed to subject sequences and contaminants), alignment and mapping of microbiome-derived sequences (e.g., alignment of fragmented sequences using one or more of single-ended alignment, ungapped alignment, gapped alignment, pairing), and generating features associated with (e.g., derived from) compositional and/or functional aspects of the microbiome associated with a biological sample.
Identification of microbiome-derived sequences can include mapping of sequence data from sample processing to a subject reference genome (e.g., provided by the Genome Reference Consortium), in order to remove subject genome-derived sequences. Unidentified sequences remaining after mapping of sequence data to the subject reference genome can then be further clustered into operational taxonomic units (OTUs) based upon sequence similarity and/or reference-based approaches (e.g., using VAMPS, using MG-RAST, using QIIME databases), aligned (e.g., using a genome hashing approach, using a Needleman-Wunsch algorithm, using a Smith-Waterman algorithm), and mapped to reference bacterial genomes (e.g., provided by the National Center for Biotechnology Information), using an alignment algorithm (e.g., Basic Local Alignment Search Tool, FPGA accelerated alignment tool, BWT-indexing with BWA, BWT-indexing with SOAP, BWT-indexing with Bowtie, etc.). Mapping of unidentified sequences can additionally or alternatively include mapping to reference archaeal genomes, viral genomes and/or eukaryotic genomes. Furthermore, mapping of taxa can be performed in relation to existing databases, and/or in relation to custom-generated databases.
However, processing biological samples, generating a microorganism dataset, and/or other aspects associated with Block 110 can be performed in any suitable manner.
The method 100 can additionally or alternatively include Block S120, which can include processing (e.g., receiving, collecting, transforming, determining supplementary features, ranking supplementary features, identifying correlations, etc.) supplementary data (e.g., one or more supplementary datasets, etc.) associated with (e.g., informative of; describing; indicative of; correlated with; etc.) one or more sleep-related conditions, one or more users, and/or other suitable entities. Block S120 can function to process data for supplementing microorganism datasets, microbiome features (e.g., in relation to determining sleep-related characterizations and/or facilitating therapeutic intervention, etc.), and/or can function to supplement any suitable portion of the method 100 and/or system 200 (e.g., processing supplementary data for facilitating one or more characterization processes, such as in Block S130; such as for facilitating training, validating, generating, determining, applying and/or otherwise processing sleep-related characterization models, etc.). In an example, supplementary data can include at least one of survey-derived data, user data, site-specific data, and device data (and/or other suitable supplementary data), where the method 100 can include determining a set of supplementary features based on the at least one of the survey-derived data, the user data, the site-specific data, and the device data (and/or other suitable supplementary data); and generating one or more sleep-related characterization models based on the supplementary features, microbiome features, and/or other suitable data.
Supplementary data can include any one or more of: survey-derived data (e.g., data from responses to one or more surveys surveying for one or more sleep-related conditions, for any suitable types of data described herein; etc.); site-specific data (e.g., data informative of different collection sites, such as prior biological knowledge indicating correlations between microbiomes at specific collection sites and one or more sleep-related conditions; etc.); sleep-related condition data (e.g., data informative of different sleep-related conditions, such as in relation to microbiome characteristics, therapies, users, etc.); device data (e.g., sensor data; contextual sensor data associated with sleep; wearable device data; medical device data; user device data such as mobile phone application data; web application data; etc.); user data (e.g., user medical data current and historical medical data such as historical therapies, historical medical examination data; medical device-derived data; physiological data; data associated with medical tests; social media data; demographic data; family history data; behavior data describing behaviors; environmental factor data describing environmental factors; diet-related data such as data from food establishment check-ins, data from spectrophotometric analysis, user-inputted data, nutrition data associated with probiotic and/or prebiotic food items, types of food consumed, amount of food consumed, caloric data, diet regimen data, and/or other suitable diet-related data; etc.); prior biological knowledge (e.g., informative of sleep-related conditions, microbiome characteristics, associations between microbiome characteristics and sleep-related conditions, etc.); and/or any other suitable type of supplementary data.
In variations, processing supplementary data can include processing survey-derived data, where the survey-derived data can provide physiological data, demographic data, behavior data, environmental factor data (e.g., describing environmental factors, etc.), other types of supplementary data, and/or any other suitable data. Physiological data can include information related to physiological features (e.g., height, weight, body mass index, body fat percent, body hair level, medical history, etc.). Demographic data can include information related to demographic characteristics (e.g., gender, age, ethnicity, marital status, number of siblings, socioeconomic status, sexual orientation, etc.). Behavioral data can describe behaviors including one or more: health-associated states (e.g., health and disease states), dietary habits (e.g., alcohol consumption, caffeine consumption, omnivorous, vegetarian, vegan, sugar consumption, acid consumption, consumption of wheat, egg, soy, treenut, peanut, shellfish, food preferences, allergy characteristics, consumption and/or avoidance of other food items, etc.), behavioral tendencies (e.g., levels of physical activity, drug use, alcohol use, habit development, etc.), different levels of mobility (e.g., amount of exercise such as low, moderate, and/or extreme physical exercise activity; related to distance traveled within a given time period; indicated by mobility sensors such as motion and/or location sensors; etc.), different levels of sexual activity (e.g., related to numbers of partners and sexual orientation), and any other suitable behavioral data. Survey-derived data can include quantitative data, qualitative data, and/or other suitable types of survey-derived data, such as where qualitative data can be converted to quantitative data (e.g., using scales of severity, mapping of qualitative responses to quantified scores, etc.). Processing survey-derived data can include facilitating collection of survey-derived data, such as including providing one or more surveys to one or more users, subjects, and/or other suitable entities. Surveys can be provided in-person (e.g., in coordination with sample kit provision and/or reception of samples; etc.), electronically (e.g., during account setup; at an application executing at an electronic device of a subject, at a web application and/or website accessible through an internet connection; etc.), and/or in any other suitable manner.
Additionally or alternatively, processing supplementary data can include processing sensor data (e.g., sensors of sleep-related devices, wearable computing devices, mobile devices; biometric sensors associated with the user, such as biometric sensors of a user smart phone; etc.). Sensor data can include any one or more of: physical activity- and/or physical action-related data (e.g., accelerometer data, gyroscope data, location sensor data such as GPS data, and/or other mobility sensor data from one or more devices such as a mobile device and/or wearable electronic device, etc.), sensor data describing environmental factors (e.g., temperature data, elevation data, climate data, light parameter data, pressure data, air quality data, etc.), biometric sensor data (e.g., heart rate sensor data; fingerprint sensor data; optical sensor data such as facial images and/or video; data recorded through sensors of a mobile device; data recorded through a wearable or other peripheral device; etc.), and/or any other suitable data associated with sensors. Additionally or alternatively, sensor data can include data sampled at one or more: optical sensors (e.g., image sensors, light sensors, cameras, etc.), audio sensors (e.g., microphones, etc.), temperature sensors, volatile compound sensors, air quality sensors, weight sensors, humidity sensors, depth sensors, location sensors (GPS receivers; beacons; indoor positioning systems; compasses; etc.), motion sensors (e.g., accelerators, gyroscope, magnetometer, remote motion detection systems such as for monitoring motion of a user when sleeping, motion sensors integrated with a device worn by a user during sleep, etc.), biometric sensors (e.g., heart rate sensors such as for monitoring heart rate during a time period associated with user sleep; fingerprint sensors; facial recognition sensors; bio-impedance sensors, etc.), pressure sensors (e.g., integrated with a bedding-related component, such as for detecting user motion when sleeping on a bed, etc.), proximity sensors (e.g., for monitoring motion and/or other aspects of third-party objects associated with user sleep periods; etc.), flow sensors, power sensors (e.g., Hall effect sensors), virtual reality-related sensors, augmented reality-related sensors, and/or or any other suitable types of sensors.
Additionally or alternatively, supplementary data can include medical record data and/or clinical data. As such, portions of the supplementary dataset can be derived from one or more electronic health records (EHRs). Additionally or alternatively, supplementary data can include any other suitable diagnostic information (e.g., clinical diagnosis information). Any suitable supplementary data (e.g., in the form of extracted supplementary features, etc.) can be combined with and/or used with microbiome features and/or other suitable data for performing portions of the method 100 (e.g., performing characterization processes, etc.) and/or system 200. For example, supplementary data associated with (e.g., derived from, etc.) a colonoscopy, biopsy, blood test, diagnostic imaging, other suitable diagnostic procedures, survey-related information, and/or any other suitable test can be used to supplement (e.g., for any suitable portions of the method 100 and/or system 200).
Additionally or alternatively, supplementary data can include therapy-related data including one or more of: therapy regimens, types of therapies, recommended therapies, therapies used by the user, therapy adherence, and/or other suitable data related to therapies. For example, supplementary data can include user adherence metrics (e.g., medication adherence, probiotic adherence, physical exercise adherence, dietary adherence, etc.) in relation one or more therapies (e.g., a recommended therapy, etc.). However, processing supplementary data can be performed in any suitable manner.
Block S130 can include, performing a characterization process (e.g., pre-processing, feature generation, feature processing, multi-site characterization for a plurality of collection sites, cross-condition analysis for a plurality of sleep-related conditions, model generation, etc.) associated with one or more sleep-related conditions, such as based on a microorganism dataset (e.g., derived in Block 110, etc.) and/or other suitable data (e.g., supplementary dataset; etc.) S130. Block S130 can function to identify, determine, extract, and/or otherwise process features and/or feature combinations that can be used to determine sleep-related characterizations for users or and sets of users, based upon their microbiome composition (e.g., microbiome composition diversity features, etc.), function (e.g., microbiome functional diversity features, etc.), and/or other suitable microbiome features (e.g., such as through the generation and application of a characterization model for determining sleep-related characterizations, etc.). As such, the characterization process can be used as a diagnostic tool that can characterize a subject (e.g., in terms of behavioral traits, in terms of medical conditions, in terms of demographic characteristics, etc.) based upon their microbiome composition and/or functional features, in relation to one or more of their health condition states (e.g., sleep-related condition states), behavioral traits, medical conditions, demographic characteristics, and/or any other suitable traits. Such characterizations can be used to determine, recommend, and/or provide therapies (e.g., personalized therapies, such as determined by way of a therapy model, etc.), and/or otherwise facilitate therapeutic intervention.
Performing a characterization process S130 can include pre-processing microorganism datasets, microbiome features, and/or other suitable data for facilitation of downstream processing (e.g., determining sleep-related characterizations, etc.). In an example, performing a characterization process can include, filtering a microorganism dataset (e.g., filtering a microorganism sequence dataset, such as prior to applying a set of analytical techniques to determine the microbiome features, etc.), by at least one of: a) removing first sample data corresponding to first sample outliers of a set of biological samples (e.g., associated with one or more sleep-related conditions, etc.), such as where the first sample outliers are determined by at least one of principal component analysis, a dimensionality reduction technique, and a multivariate methodology; b) removing second sample data corresponding to second sample outliers of the set of biological samples, where the second sample outliers can determined based on corresponding data quality for the set of microbiome features (e.g., removing samples corresponding to a number of microbiome features with high quality data below a threshold condition, etc.); and c) removing one or more microbiome features from the set of microbiome features based on a sample number for the microbiome feature failing to satisfy a threshold sample number condition, where the sample number corresponds to a number of samples associated with high quality data for the microbiome feature. However, pre-processing can be performed with any suitable analytical techniques in any suitable manner.
In performing the characterization process, Block S130 can use computational methods (e.g., statistical methods, machine learning methods, artificial intelligence methods, bioinformatics methods, etc.) to characterize a subject as exhibiting features (e.g., where determining user microbiome features can include determining feature values for microbiome features identified by characterization processes as correlated with and/or otherwise associated with one or more sleep-related conditions, etc.) associated with one or more sleep-related conditions (e.g., features characteristic of a set of users with the one or more sleep-related conditions, etc.).
As shown in
In variations, upon identification of represented groups of microorganisms of the microbiome associated with a biological sample, generating features associated with (e.g., derived from) compositional and functional aspects of the microbiome associated with a biological sample can be performed. In a variation, generating features can include generating features based upon multilocus sequence typing (MSLT), in order to identify markers useful for characterization in subsequent blocks of the method 100. Additionally or alternatively, generated features can include generating features that describe the presence or absence of certain taxonomic groups of microorganisms, and/or ratios between exhibited taxonomic groups of microorganisms. Additionally or alternatively, generating features can include generating features describing one or more of: quantities of represented taxonomic groups, networks of represented taxonomic groups, correlations in representation of different taxonomic groups, interactions between different taxonomic groups, products produced by different taxonomic groups, interactions between products produced by different taxonomic groups, ratios between dead and alive microorganisms (e.g., for different represented taxonomic groups, based upon analysis of RNAs), phylogenetic distance (e.g., in terms of Kantorovich-Rubinstein distances, Wasserstein distances etc.), any other suitable taxonomic group-related feature(s), any other suitable genetic or functional aspect(s).
Additionally or alternatively, generating features can include generating features describing relative abundance of different microorganism groups, for instance, using a sparCC approach, using Genome Relative Abundance and Average size (GAAS) approach and/or using a Genome Relative Abundance using Mixture Model theory (GRAMMy) approach that uses sequence-similarity data to perform a maximum likelihood estimation of the relative abundance of one or more groups of microorganisms. Additionally or alternatively, generating features can include generating statistical measures of taxonomic variation, as derived from abundance metrics. Additionally or alternatively, generating features can include generating features associated with (e.g., derived from) relative abundance factors (e.g., in relation to changes in abundance of a taxon, which affects abundance of other taxa). Additionally or alternatively, generating features can include generation of qualitative features describing presence of one or more taxonomic groups, in isolation and/or in combination. Additionally or alternatively, generating features can include generation of features related to genetic markers (e.g., representative 16S, 18S, and/or ITS sequences) characterizing microorganisms of the microbiome associated with a biological sample. Additionally or alternatively, generating features can include generation of features related to functional associations of specific genes and/or organisms having the specific genes. Additionally or alternatively, generating features can include generation of features related to pathogenicity of a taxon and/or products attributed to a taxon. Block S130 can, however, include determination of any other suitable feature(s) derived from sequencing and mapping of nucleic acids of a biological sample. For instance, the feature(s) can be combinatory (e.g. involving pairs, triplets), correlative (e.g., related to correlations between different features), and/or related to changes in features (e.g., temporal changes, changes across sample sites, etc., spatial changes, etc.). However, determining microbiome features can be performed in any suitable manner.
In variations, performing a characterization process can include performing one or more multi-site analyses (e.g., with sleep-related characterization models; generating a multi-site characterization, etc.) associated with a plurality of collection sites. However, multi-site analyses can be performed in any suitable manner.
In variations, performing a characterization process can include performing one or more cross-condition analyses (e.g., using sleep-related characterization models, etc.) for a plurality of sleep-related conditions. In an example, performing cross-condition analyses can include determining a set of cross-condition features (e.g., as part of determining microbiome features, etc.) associated with a plurality of sleep-related conditions (e.g., a first sleep-related condition and a second sleep-related condition, etc.) based on one or more analytical techniques, where determining a sleep-related characterization can include determining the sleep-related characterization for a user for the plurality of sleep-related conditions (e.g., first and the second sleep-related conditions, etc.) based on one or more sleep-related characterization models, and where the set of cross-condition features is configured to improve the computing system-related functionality associated with the determining of the sleep-related characterization for the user for the plurality of sleep-related conditions. Performing cross-condition analyses can include determining cross-condition correlation metrics (e.g., correlation and/or covariance between data corresponding to different sleep-related conditions, etc.) and/or other suitable metrics associated with cross-condition analyses. However, performing cross-condition analyses can be performed in any suitable manner.
In a variation, characterization can be based upon features associated with (e.g., derived from) a statistical analysis (e.g., an analysis of probability distributions) of similarities and/or differences between a first group of subjects exhibiting a target state (e.g., a sleep-related condition state) and a second group of subjects not exhibiting the target state (e.g., a “normal” state). In implementing this variation, one or more of a Kolmogorov-Smirnov (KS) test, a permutation test, a Cramer-von Mises test, any other statistical test (e.g., t-test, z-test, chi-squared test, test associated with distributions, etc.), and/or other suitable analytical techniques can be used. In particular, one or more such statistical hypothesis tests can be used to assess a set of features having varying degrees of abundance in a first group of subjects exhibiting a target state (e.g., a sick state) and a second group of subjects not exhibiting the target state (e.g., having a normal state). In more detail, the set of features assessed can be constrained based upon percent abundance and/or any other suitable parameter pertaining to diversity in association with the first group of subjects and the second group of subjects, in order to increase or decrease confidence in the characterization. In a specific implementation of this example, a feature can be derived from a taxon of bacteria that is abundant in a certain percentage of subjects of the first group and subjects of the second group, where a relative abundance of the taxon between the first group of subjects and the second group of subjects can be determined from the KS test, with an indication of significance (e.g., in terms of p-value). Thus, an output of Block S130 can include a normalized relative abundance value (e.g., 25% greater abundance of a taxon in subjects with a sleep-related condition vs. subjects without the sleep-related condition; in sick subjects vs. healthy subjects) with an indication of significance (e.g., a p-value of 0.0013). Variations of feature generation can additionally or alternatively implement or be derived from functional features or metadata features (e.g., non-bacterial markers). Additionally or alternatively, any suitable microbiome features can be derived based on statistical analyses (e.g., applied to a microorganism sequence dataset and/or other suitable microorganism dataset, etc.) including any one or more of: a prediction analysis, multi hypothesis testing, a random forest test, principal component analysis, and/or other suitable analytical techniques.
In performing the characterization process, Block S130 can additionally or alternatively transform input data from at least one of the microbiome composition diversity dataset and microbiome functional diversity dataset into feature vectors that can be tested for efficacy in predicting characterizations of the population of subjects. Data from the supplementary dataset can be used to provide indication of one or more characterizations of a set of characterizations, where the characterization process is trained with a training dataset of candidate features and candidate classifications to identify features and/or feature combinations that have high degrees (or low degrees) of predictive power in accurately predicting a classification. As such, refinement of the characterization process with the training dataset identifies feature sets (e.g., of subject features, of combinations of features) having high correlation with specific classifications of subjects.
In variations, feature vectors (and/or any suitable set of features) effective in predicting classifications of the characterization process can include features related to one or more of: microbiome diversity metrics (e.g., in relation to distribution across taxonomic groups, in relation to distribution across archaeal, bacterial, viral, and/or eukaryotic groups), presence of taxonomic groups in one's microbiome, representation of specific genetic sequences (e.g., 16S sequences) in one's microbiome, relative abundance of taxonomic groups in one's microbiome, microbiome resilience metrics (e.g., in response to a perturbation determined from the supplementary dataset), abundance of genes that encode proteins or RNAs with given functions (enzymes, transporters, proteins from the immune system, hormones, interference RNAs, etc.) and any other suitable features associated with (e.g., derived from) the microbiome diversity dataset and/or the supplementary dataset. In variations, microbiome features can be associated with (e.g., include, correspond to, typify, etc.) at least one of: presence of a microbiome feature from the microbiome features (e.g., user microbiome features, etc.), absence of the microbiome features from the microbiome features, relative abundance of different taxonomic groups associated with the sleep-related condition; a ratio between at least two microbiome features associated with the different taxonomic groups, interactions between the different taxonomic groups, and phylogenetic distance between the different taxonomic groups. In a specific example, microbiome features can include one or more relative abundance characteristics associated with at least one of the microbiome composition diversity features (e.g., relative abundance associated with different taxa, etc.) and the microbiome functional diversity features (e.g., relative abundance of sequences corresponding to different functional features; etc.). Relative abundance characteristics and/or other suitable microbiome features (and/or other suitable data described herein) can be extracted and/or otherwise determined based on: a normalization, a feature vector derived from at least one of linear latent variable analysis and non-linear latent variable analysis, linear regression, non-linear regression, a kernel method, a feature embedding method, a machine learning method, a statistical inference method, and/or other suitable analytical techniques. Additionally or alternatively, combinations of features can be used in a feature vector, where features can be grouped and/or weighted in providing a combined feature as part of a feature set. For example, one feature or feature set can include a weighted composite of the number of represented classes of bacteria in one's microbiome, presence of a specific genus of bacteria in one's microbiome, representation of a specific 16S sequence in one's microbiome, and relative abundance of a first phylum over a second phylum of bacteria. However, the feature vectors can additionally or alternatively be determined in any other suitable manner.
In a variation, the characterization process can be generated and trained according to a random forest predictor (RFP) algorithm that combines bagging (e.g., bootstrap aggregation) and selection of random sets of features from a training dataset to construct a set of decision trees, T, associated with the random sets of features. In using a random forest algorithm, N cases from the set of decision trees are sampled at random with replacement to create a subset of decision trees, and for each node, m prediction features are selected from all of the prediction features for assessment. The prediction feature that provides the best split at the node (e.g., according to an objective function) is used to perform the split (e.g., as a bifurcation at the node, as a trifurcation at the node). By sampling many times from a large dataset, the strength of the characterization process, in identifying features that are strong in predicting classifications can be increased substantially. In this variation, measures to prevent bias (e.g., sampling bias) and/or account for an amount of bias can be included during processing, such as to increase robustness of the model.
In a variation, Block S130 and/or other portions of the method 100 can include applying computer-implemented rules (e.g., models, feature selection rules, etc.) to process population-level data, but can additionally or alternatively include applying computer-implemented rules to process microbiome-related data on a demographic characteristic-specific basis (e.g., subgroups sharing one or more demographic characteristics such as therapy regimens, dietary regimens, physical activity regimens, ethnicity, age, gender, weight, sleeping behaviors, etc.), condition-specific basis (e.g., subgroups exhibiting a specific sleep-related condition, a combination of sleep-related conditions, triggers for the sleep-related conditions, associated symptoms, etc.), a sample type-specific basis (e.g., applying different computer-implemented rules to process microbiome data derived from different collection sites; etc.), a user basis (e.g., different computer-implemented rules for different users; etc.) and/or any other suitable basis. As such, Block S130 can include assigning users from the population of users to one or more subgroups; and applying different computer-implemented rules for determining features (e.g., the set of feature types used; the types of characterization models generated from the features; etc.) for the different subgroups. However, applying computer-implemented rules can be performed in any suitable manner.
In another variation, Block S130 can include processing (e.g., generating, training, updating, executing, storing, etc.) one or more sleep-related characterization models (e.g., sleep-related condition models, therapy models, etc.) for one or more sleep-related conditions (e.g., for outputting characterizations for users describing user microbiome characteristics in relation to sleep-related conditions; therapy models for outputting therapy determinations for one or more sleep-related conditions; etc.). The characterization models preferably leverage microbiome features as inputs, and preferably output sleep-related characterizations and/or any suitable components thereof; but characterization models can use any suitable inputs to generate any suitable outputs. In an example, Block S130 can include transforming the supplementary data, the microbiome composition diversity features, and the microbiome functional diversity features, other microbiome features, outputs of sleep-related characterization models, and/or other suitable data into one or more characterization models (e.g., training a sleep-related characterization model based on the supplementary data and microbiome features; etc.) for one or more sleep-related conditions. In another example, the method 100 can include: determining a population microorganism sequence dataset (e.g., including microorganism sequence outputs for different users of the population; etc.) for a population of users associated with one or more sleep-related conditions, based on a set of samples from the population of users (e.g., and/or based on one or more primer types associated with the sleep-related condition; etc.); collecting a supplementary dataset associated with diagnosis of the one or more sleep-related conditions for the population of subjects; and generating the sleep-related characterization model based on the population microorganism sequence dataset and the supplementary dataset. In an example, the method 100 can include determining a set of user microbiome features for the user based on a sample from the user, where the set of user microbiome features is associated with microbiome features associated with a set of subjects (e.g., microbiome features determined to be correlated with one or more sleep-related conditions, based on processing biological samples corresponding to a set of subjects associated with the one or more sleep-related conditions; a set microbiome composition features and the set of microbiome functional features; etc.); determining a sleep-related characterization, including determining a therapy for the user for the one or more sleep-related conditions based on a therapy model and the set of user microbiome features; providing the therapy (e.g., providing a recommendation for the therapy to the user at a computing device associated with the user, etc.) and/or otherwise facilitating therapeutic intervention.
In another variation, as shown in
In variations, determining sleep-related characterizations and/or any other suitable characterizations can include determining sleep-related characterizations in relation to specific physiological sites (e.g., gut, healthy gut, skin, nose, mouth, genitals, other suitable physiological sites, other sample collection sites, etc.), such as through any one or more of: determining a sleep-related characterization based on a sleep-related characterization model derived based on site-specific data (e.g., defining correlations between a sleep-related condition and microbiome features associated with one or more physiological sites); determining a sleep-related characterization based on a user biological sample collected at one or more physiological sites, and/or any other suitable site-related processes. In examples, machine learning approaches (e.g., classifiers, deep learning algorithms, SVM, random forest), parameter optimization approaches (e.g., Bayesian Parameter Optimization), validation approaches (e.g., cross validation approaches), statistical tests (e.g., univariate statistical techniques, multivariate statistical techniques, correlation analysis such as canonical correlation analysis, etc.), dimension reduction techniques (e.g. PCA), and/or other suitable analytical techniques (e.g., described herein) can be applied in determining site-related (e.g., physiological site-related, etc.) characterizations (e.g., using a one or more approaches for one or more sample collection sites, such as for each type of sample collection site, etc.), other suitable characterizations, therapies, and/or any other suitable outputs. In a specific example, performing a characterization process (e.g., determining a sleep-related characterization; determining microbiome features; based on a sleep-related characterization model; etc.) can include applying at least one of: machine learning approaches, parameter optimization approaches, statistical tests, dimension reduction approaches, and/or other suitable approaches (e.g., where microbiome features such as a set of microbiome composition diversity features and/or a set of microbiome functional diversity features can be associated with microorganisms collected at least at one of a gut site, a skin site, a nose site, a mouth site, a genitals site, etc.). In another specific example, characterization processes performed for a plurality of sample collection sites can be used to generate individual characterizations that can be combined to determine an aggregate characterization (e.g., an aggregate microbiome score, such as for one or more conditions described herein, etc.). However, the method 100 can include determining any suitable site-related (e.g., site-specific) outputs, and/or performing any suitable portions of the method 100 (e.g., collecting samples, processing samples, determining therapies) with site-specificity and/or other site-relatedness in any suitable manner.
Characterization of the subject(s) can additionally or alternatively implement use of a high false positive test and/or a high false negative test to further analyze sensitivity of the characterization process in supporting analyses generated according to embodiments of the method 100. However, performing one or more characterization processes S130 can be performed in any suitable manner.
4.3.A Sleep-related characterization process.
Performing a characterization process S130 can include performing a sleep-related characterization process (e.g., determining a characterization for one or more sleep-related conditions; determining and/or applying one or more sleep-related characterization model; etc.) S135, such as for one or more users (e.g., for data corresponding to samples from a set of subjects for generating one or more sleep-related characterization models; for a single user for generating a sleep-related characterization for the user, such as through using one or more sleep-related characterization models; etc.) and/or for one or more sleep-related conditions.
In a variation, performing a sleep-related characterization process can include determining microbiome features associated with one or more sleep-related conditions (e.g., a sleep order condition. In an example, performing a sleep-related characterization process can include applying one or more analytical techniques (e.g., statistical analyses) to identify the sets of microbiome features (e.g., microbiome composition features, microbiome composition diversity features, microbiome functional features, microbiome functional diversity features, etc.) that have the highest correlations with one or more sleep-related conditions (e.g., features associated with a single sleep-related condition, cross-condition features associated with multiple sleep-related conditions and/or other suitable sleep-related conditions, etc.). In a specific example, performing a sleep-related characterization process can facilitate therapeutic intervention for one or more sleep-related conditions, such as through facilitating intervention associated with therapies having a positive effect on a state of one or more users in relation to the one or more sleep-related conditions. In another specific example, performing a sleep-related characterization process (e.g., determining features highest correlations to one or more sleep-related conditions, etc.) can be based upon a random forest predictor algorithm trained with a training dataset derived from a subset of the population of subjects (e.g., subjects having the one or more sleep-related conditions; subjects not having the one or more sleep-related conditions; etc.), and validated with a validation dataset derived from a subset of the population of subjects. However, determining microbiome features and/or other suitable aspects associated with one or more sleep-related conditions can be performed in any suitable manner.
Microbiome features associated with one or more sleep-related conditions (e.g., positively correlated with; negatively correlated with; useful for diagnosis; etc.) can include features associated with any combination of one or more of the following taxa (e.g., features describing abundance of; features describing relative abundance of; features describing functional aspects associated with; features derived from; features describing presence and/or absence of; etc.) described in Table 1 (e.g., in relation to a sleep-related condition of bad sleep quality, etc.) and/or Table 2 (e.g., in relation to a sleep-related condition of shift work, such as night time shift work with day time sleeping periods, etc.) and/or: Acetitomaculum (genus), Acidaminococcaceae (family), Acidaminococcus (genus), Acidaminococcus sp. D21 (species), Actinobacteria (class), Actinobacteria (phylum), Actinomyces (genus), Actinomyces sp. ICM47 (species), Actinomyces sp. ICM54 (species), Actinomyces sp. S9 PR-21 (species), Akkermansia muciniphila (species), Alcaligenaceae (family), Alistipes indistinctus (species), Alistipes sp. 627 (species), Anaerococcus (genus), Anaerococcus hydrogenalis (species), Anaerococcus octavius (species), Anaerococcus sp. 8404299 (species), Anaerococcus sp. 8405254 (species), Anaerococcus tetradius (species), Anaerofustis (genus), Anaerofustis stercorihominis (species), Anaeroplasma (genus), Anaerosporobacter (genus), Anaerostipes sp. 1y-2 (species), Anaerostipes sp. 3_2_56FAA (species), Anaerotruncus colihominis (species), Anaerotruncus sp. NML 070203 (species), Atopobium (genus), Atopobium vaginae (species), Bacteroides clarus (species), Bacteroides coprocola (species), Bacteroides nordii (species), Bacteroides plebeius (species), Bacteroides sp. 2_2_4 (species), Bacteroides sp. CB57 (species), Bacteroides sp. DJF_B097 (species), Bacteroides sp. EBA5-17 (species), Bacteroides sp. SLC1-38 (species), Bacteroides sp. XB12B (species), Bacteroides stercorirosoris (species), Bifidobacteriaceae (family), Bifidobacteriales (order), Bifidobacterium (genus), Bifidobacterium biavatii (species), Bifidobacterium bifidum (species), Bifidobacterium choerinum (species), Bifidobacterium longum (species), Bifidobacterium merycicum (species), Bifidobacterium sp. MSX5B (species), Bifidobacterium stercoris (species), Blautia glucerasea (species), Blautia hydrogenotrophica (species), Blautia sp. Ser8 (species), Blautia sp. YHC-4 (species), Brevibacterium massiliense (species), Butyricicoccus (genus), Butyricicoccus pullicaecorum (species), Butyricimonas synergistica (species), Butyrivibrio (genus), Butyrivibrio crossotus (species), Campylobacter (genus), Campylobacter hominis (species), Campylobacter ureolyticus (species), Campylobacteraceae (family), Campylobacterales (order), Candidatus Soleaferrea (genus), Candidatus Stoquefichus (genus), Catabacter hongkongensis (species), Catenibacterium mitsuokai (species), Cellulosilyticum (genus), Collinsella aerofaciens (species), Collinsella intestinalis (species), Coprobacillus (genus), Coprobacillus sp. D6 (species), Coprobacter (genus), Coprobacter fastidiosus (species), Corynebacterium sp. (species), Corynebacterium ulcerans (species), Cyanobacteria (phylum), Dermabacter (genus), Dermabacter hominis (species), Dermabacteraceae (family), Desulfovibrio desulfuricans (species), Desulfovibrio piger (species), Desulfovibrio sp. (species), Dialister (genus), Dialister invisus (species), Dialister propionicifaciens (species), Dielma (genus), Dielma fastidiosa (species), Eggerthella (genus), Eggerthella sp. HGA1 (species), Eisenbergiella tayi (species), Enterobacter (genus), Enterobacter sp. BS2-1 (species), Enterococcus sp. C6I11 (species), Epsilonproteobacteria (class), Erysipelatoclostridium ramosum (species), Eubacteriaceae (family), Eubacterium (genus), Eubacterium callanderi (species), Eubacterium sp. SA11 (species), Facklamia sp. 1440-97 (species), Fibrobacter (genus), Flavobacterium (genus), Flavonifractor plautii (species), Fusobacteria (phylum), Fusobacteriaceae (family), Fusobacteriales (order), Fusobacteriia (class), Fusobacterium (genus), Fusobacterium equinum (species), Fusobacterium ulcerans (species), Gardnerella (genus), Gardnerella vaginalis (species), Gelria (genus), Gordonibacter (genus), Gordonibacter pamelaeae (species), Granulicatella (genus), Granulicatella adiacens (species), Haemophilus (genus), Haemophilus parainfluenzae (species), Herbaspirillum (genus), Herbaspirillum seropedicae (species), Holdemania (genus), Holdemania filiformis (species), Howardella (genus), Hydrogenoanaerobacterium (genus), Intestinibacter (genus), Klebsiella (genus), Kluyvera georgiana (species), Lachnospira (genus), Lactobacillus crispatus (species), Lactobacillus rhamnosus (species), Lactobacillus sp. 66c (species), Lactobacillus sp. Akhmrol (species), Lactobacillus sp. BL302 (species), Lactobacillus sp. TAB-30 (species), Lactonifactor (genus), Lactonifactor longoviformis (species), Leptotrichiaceae (family), Leuconostoc (genus), Leuconostocaceae (family), Megamonas (genus), Megamonas funiformis (species), Megasphaera (genus), Megasphaera genomosp. C1 (species), Megasphaera sp. S6-MB2 (species), Megasphaera sp. UPII 199-6 (species), Mobiluncus (genus), Mobiluncus mulieris (species), Moryella (genus), Negativicoccus (genus), Negativicoccus succinicivorans (species), Negativicutes (class), Oligella (genus), Oligella urethralis (species), Olsenella sp. 1183 (species), Oscillospiraceae (family), Pantoea (genus), Papillibacter (genus), Parabacteroides goldsteinii (species), Parabacteroides sp. 157 (species), Paraprevotella clara (species), Parvimonas micra (species), Pasteurellaceae (family), Pasteurellales (order), Peptoniphilus (genus), Peptoniphilus coxii (species), Peptoniphilus sp. 2002-2300004 (species), Peptoniphilus sp. 7-2 (species), Peptoniphilus sp. gpac018A (species), Phascolarctobacterium (genus), Phascolarctobacterium succinatutens (species), Phyllobacteriaceae (family), Phyllobacterium (genus), Porphyromonas uenonis (species), Prevotella bivia (species), Prevotella disiens (species), Propionibacteriaceae (family), Propionibacterium (genus), Proteobacteria (phylum), Pseudobutyrivibrio (genus), Pseudoclavibacter sp. Timone(species), Rhizobiales (order), Roseburia (genus), Ruminococcaceae (family), Sarcina ventriculi (species), Selenomonadales order Shuttleworthia (genus), Sphingomonadaceae (family), Sphingomonadales (order), Stenotrophomonas (genus), Stenotrophomonas sp. C-S-TSA3 (species), Streptococcus agalactiae (species), Streptococcus gordonii (species), Streptococcus pasteurianus (species), Streptococcus peroris (species), Streptococcus sp. BS35a (species), Streptococcus sp. oral taxon G59 (species), Sutterella (genus), Sutterella sp. YIT 12072 (species), Sutterella stercoricanis (species), Sutterella wadsworthensis (species), Terrisporobacter glycolicus (species), Thermoanaerobacteraceae (family), Thermoanaerobacterales (order), Turicibacter (genus), Turicibacter sanguinis (species), Varibaculum (genus), Varibaculum cambriense (species), Veillonella sp. AS16 (species), Veillonellaceae (family), Weissella hellenica (species), Xanthomonadaceae (family), Xanthomonadales (order), Alistipes massiliensis (species), Butyricimonas virosa (species), Alistipes putredinis (species), Actinobacillus porcinus (species), Actinobacillus (genus), Butyricimonas (genus), Howardella ureilytica (species), Firmicutes (phylum), Clostridium (genus), Lentisphaeria (class), Anaeroplasmataceae (family), Pseudomonadaceae (family), Victivallaceae (family), Blautia (genus), Asteroleplasma (genus), Delftia (genus), Victivallis (genus), Peptostreptococcus (genus), Pseudomonas (genus), Alloprevotella (genus), Catenibacterium (genus), Anaeroplasmatales (order), Pseudomonadales (order), Lentisphaerae (phylum), Veillonella sp. CM60 (species), Porphyromonas sp. 2026 (species), Delftia sp. BN-SKY3 (species), Peptostreptococcus anaerobius (species), Citrobacter sp. BW4 (species), Alistipes sp. RMA 9912 (species), Bacteroides vulgatus (species), Lactobacillus sp. BL302 (species), Lactobacillus sp. TAB-26 (species), Bifidobacterium kashiwanohense (species), Butyricimonas sp. JCM 18677 (species) and/or any other suitable taxa (e.g., described herein, etc.).
In a first variation, microbiome features associated with one or more sleep-related conditions can include features associated with one or more of the following taxa: Acetitomaculum (genus), Acidaminococcaceae (family), Acidaminococcus (genus), Acidaminococcus sp. D21 (species), Actinobacteria (class), Actinobacteria (phylum), Actinomyces (genus), Actinomyces sp. ICM47 (species), Actinomyces sp. ICM54 (species), Actinomyces sp. S9 PR-21 (species), Akkermansia muciniphila (species), Alcaligenaceae (family), Alistipes indistinctus (species), Alistipes sp. 627 (species), Anaerococcus (genus), Anaerococcus hydrogenalis (species), Anaerococcus octavius (species), Anaerococcus sp. 8404299 (species), Anaerococcus sp. 8405254 (species), Anaerococcus tetradius (species), Anaerofustis (genus), Anaerofustis stercorihominis (species), Anaeroplasma (genus), Anaerosporobacter (genus), Anaerostipes sp. 1y-2 (species), Anaerostipes sp. 3—2_56FAA (species), Anaerotruncus colihominis (species), Anaerotruncus sp. NML 070203 (species), Atopobium (genus), Atopobium vaginae (species), Bacteroides clarus (species), Bacteroides coprocola (species), Bacteroides nordii (species), Bacteroides plebeius (species), Bacteroides sp. 2_2_4 (species), Bacteroides sp. CB57 (species), Bacteroides sp. DJF_B097 (species), Bacteroides sp. EBA5-17 (species), Bacteroides sp. SLC1-38 (species), Bacteroides sp. XB12B (species), Bacteroides stercorirosoris (species), Bifidobacteriaceae (family), Bifidobacteriales (order), Bifidobacterium (genus), Bifidobacterium biavatii (species), Bifidobacterium bifidum (species), Bifidobacterium choerinum (species), Bifidobacterium longum (species), Bifidobacterium merycicum (species), Bifidobacterium sp. MSX5B (species), Bifidobacterium stercoris (species), Blautia glucerasea (species), Blautia hydrogenotrophica (species), Blautia sp. Ser8 (species), Blautia sp. YHC-4 (species), Brevibacterium massiliense (species), Butyricicoccus (genus), Butyricicoccus pullicaecorum (species), Butyricimonas synergistica (species), Butyrivibrio (genus), Butyrivibrio crossotus (species), Campylobacter (genus), Campylobacter hominis (species), Campylobacter ureolyticus (species), Campylobacteraceae (family), Campylobacterales (order), Candidatus Soleaferrea (genus), Candidatus Stoquefichus (genus), Catabacter hongkongensis (species), Catenibacterium mitsuokai (species), Cellulosilyticum (genus), Collinsella aerofaciens (species), Collinsella intestinalis (species), Coprobacillus (genus), Coprobacillus sp. D6 (species), Coprobacter (genus), Coprobacter fastidiosus (species), Corynebacterium sp. (species), Corynebacterium ulcerans (species), Cyanobacteria (phylum), Dermabacter (genus), Dermabacter hominis (species), Dermabacteraceae (family), Desulfovibrio desulfuricans (species), Desulfovibrio piger (species), Desulfovibrio sp. (species), Dialister (genus), Dialister invisus (species), Dialister propionicifaciens (species), Dielma (genus), Dielma fastidiosa (species), Eggerthella (genus), Eggerthella sp. HGA1 (species), Eisenbergiella tayi (species), Enterobacter (genus), Enterobacter sp. BS2-1 (species), Enterococcus sp. C6I11 (species), Epsilonproteobacteria (class), Erysipelatoclostridium ramosum (species), Eubacteriaceae (family), Eubacterium (genus), Eubacterium callanderi (species), Eubacterium sp. SA11 (species), Facklamia sp. 1440-97 (species), Fibrobacter (genus), Flavobacterium (genus), Flavonifractor plautii (species), Fusobacteria (phylum), Fusobacteriaceae (family), Fusobacteriales (order), Fusobacteriia (class), Fusobacterium (genus), Fusobacterium equinum (species), Fusobacterium ulcerans (species), Gardnerella (genus), Gardnerella vaginalis (species), Gelria (genus), Gordonibacter (genus), Gordonibacter pamelaeae (species), Granulicatella (genus), Granulicatella adiacens (species), Haemophilus (genus), Haemophilus parainfluenzae (species), Herbaspirillum (genus), Herbaspirillum seropedicae (species), Holdemania (genus), Holdemania filiformis (species), Howardella (genus), Hydrogenoanaerobacterium (genus), Intestinibacter (genus), Klebsiella (genus), Kluyvera georgiana (species), Lachnospira (genus), Lactobacillus crispatus (species), Lactobacillus rhamnosus (species), Lactobacillus sp. 66c (species), Lactobacillus sp. Akhmrol (species), Lactobacillus sp. BL302 (species), Lactobacillus sp. TAB-30 (species), Lactonifactor (genus), Lactonifactor longoviformis (species), Leptotrichiaceae (family), Leuconostoc (genus), Leuconostocaceae (family), Megamonas (genus), Megamonas funiformis (species), Megasphaera (genus), Megasphaera genomosp. C1 (species), Megasphaera sp. S6-MB2 (species), Megasphaera sp. UPII 199-6 (species), Mobiluncus (genus), Mobiluncus mulieris (species), Moryella (genus), Negativicoccus (genus), Negativicoccus succinicivorans (species), Negativicutes (class), Oligella (genus), Oligella urethralis (species), Olsenella sp. 1183 (species), Oscillospiraceae (family), Pantoea (genus), Papillibacter (genus), Parabacteroides goldsteinii (species), Parabacteroides sp. 157 (species), Paraprevotella clara (species), Parvimonas micra (species), Pasteurellaceae (family), Pasteurellales (order), Peptoniphilus (genus), Peptoniphilus coxii (species), Peptoniphilus sp. 2002-2300004 (species), Peptoniphilus sp. 7-2 (species), Peptoniphilus sp. gpac018A (species), Phascolarctobacterium (genus), Phascolarctobacterium succinatutens (species), Phyllobacteriaceae (family), Phyllobacterium (genus), Porphyromonas uenonis (species), Prevotella bivia (species), Prevotella disiens (species), Propionibacteriaceae (family), Propionibacterium (genus), Proteobacteria (phylum), Pseudobutyrivibrio (genus), Pseudoclavibacter sp. Timone (species), Rhizobiales (order), Roseburia (genus), Ruminococcaceae (family), Sarcina ventriculi (species), Selenomonadales order Shuttleworthia (genus), Sphingomonadaceae (family), Sphingomonadales (order), Stenotrophomonas (genus), Stenotrophomonas sp. C-S-TSA3 (species), Streptococcus agalactiae (species), Streptococcus gordonii (species), Streptococcus pasteurianus (species), Streptococcus peroris (species), Streptococcus sp. BS35a (species), Streptococcus sp. oral taxon G59 (species), Sutterella (genus), Sutterella sp. YIT 12072 (species), Sutterella stercoricanis (species), Sutterella wadsworthensis (species), (species), Thermoanaerobacteraceae (family), Thermoanaerobacterales (order), Turicibacter (genus), Turicibacter sanguinis (species), Varibaculum (genus), Varibaculum cambriense (species), Veillonella sp. AS16 (species), Veillonellaceae (family), Weissella hellenica (species), Xanthomonadaceae (family), Xanthomonadales (order), and/or any other suitable taxa.
In a second variation, microbiome features associated with one or more sleep-related conditions (e.g., and/or behaviors and/or lifestyle conditions such as shift work, etc.) can include features associated with one or more of the following taxa: Alistipes massiliensis (species), Butyricimonas virosa (species), Leuconostocaceae (family), Lactobacillus sp. TAB-30 (species), Alistipes putredinis (species), Actinobacillus porcinus (species), Bifidobacterium stercoris (species), Actinobacillus (genus), Butyricimonas (genus), Howardella (genus), Catenibacterium mitsuokai (species), Howardella ureilytica (species), Firmicutes (phylum), Clostridium (genus), Lentisphaeria (class), Anaeroplasmataceae (family), Pseudomonadaceae (family), Victivallaceae (family), Blautia (genus), Asteroleplasma (genus), Delftia (genus), Victivallis (genus), Peptostreptococcus (genus), Pseudomonas (genus), Bifidobacterium (genus), Alloprevotella (genus), Catenibacterium (genus), Anaeroplasmatales (order), Pseudomonadales (order), Lentisphaerae (phylum), Veillonella sp. CM60 (species), Lactobacillus sp. Akhmrol (species), Porphyromonas sp. 2026 (species), Weissella hellenica (species), Delftia sp. BN-SKY3 (species), Peptostreptococcus anaerobius (species), Citrobacter sp. BW4 (species), Collinsella intestinalis (species), Alistipes sp. RMA 9912 (species), Bacteroides vulgatus (species), Lactobacillus sp. BL302 (species), Lactobacillus sp. TAB-26 (species), Bifidobacterium sp. (species), Prevotella bivia (species), Bifidobacterium kashiwanohense (species), Butyricimonas sp. JCM 18677 (species), Bifidobacterium stercoris (species), and/or any other suitable taxa.
In an example, the method 100 can include determining a sleep-related characterization for the user for a first sleep-related condition and a second sleep-related condition based on a first set of composition features (e.g., including at least one or more of the microbiome features described above in relation to the first variation; including any suitable combination of microbiome features; etc.), a first sleep-related characterization model, a second set of composition features (e.g., including at least one or more of the microbiome features described above in relation to the second variation; including any suitable combination of microbiome features; etc.), and a second sleep-related characterization model, where the first sleep-related characterization model is associated with the first sleep-related condition (e.g., where the first sleep-related characterization model determines characterizations for the first sleep-related condition, etc.), and where the second sleep-related characterization model is associated with the second sleep-related condition (e.g., where the second sleep-related characterization model determines characterizations for the second sleep-related condition, etc.). In the example, determining user microbiome features can include determining first user microbiome functional features associated with first functions from at least one of Cluster of Orthologous Groups (COG) database and Kyoto Encyclopedia of Genes and Genomes (KEGG) database, where the first user microbiome functional features are associated with the first sleep-related condition; and determining second user microbiome functional features associated with second functions from at least one of the COG database and the KEGG database, where the second user microbiome functional features are associated with the second sleep-related condition, where determining the sleep-related characterization can include determining the sleep-related characterization for the user for the first sleep-related condition and the second sleep-related condition based on the first set of composition features, the first user microbiome functional features, the first sleep-related characterization model, the second set of composition features, the second user microbiome functional features, and the second sleep-related characterization model. Additionally or alternatively, any combinations of microbiome features can be used with any suitable number and types of sleep-related characterization models to determine sleep-related characterization for one or more sleep-related conditions, in any suitable manner.
Additionally or alternatively, microbiome features associated with one or more sleep-related conditions can include microbiome functional features (e.g., features describing functions associated with one or more microorganisms, such as microorganisms classified under taxa described herein; features describing functional diversity; features describing presence, absence, abundance, and/or relative abundance; etc.) corresponding to functions from and/or otherwise associated with the Clusters of Orthologous Groups (COG) database, Kyoto Encyclopedia of Genes and Genomes (KEGG) database, and/or any other suitable database available (e.g., databases with microorganism function data, etc.). However, microbiome features can include any suitable microbiome functional features associated with any suitable microorganism function, human function, and/or other suitable functionality. In examples, the method 100 can include generating one or more sleep-related characterization models based on any suitable combination of microbiome features described above and/or herein (e.g., based on a set of microbiome composition features including features associated with at least one of the taxa described herein; and/or based on microbiome functional features described herein, such as corresponding to functions from databases described herein; etc.) In an example, performing a characterization process for a user can include characterizing a user as having one or more sleep-related conditions, such as based upon detection of, values corresponding to, and/or other aspects related to microbiome features described herein (e.g., microbiome features described above, etc.), and such as in a manner that is an additional (e.g., supplemental to, complementary to, etc.) or alternative to typical approaches of diagnosis, other characterizations (e.g., treatment-related characterizations, etc.), treatment, monitoring, and/or other suitable approaches associated with sleep-related conditions. In variations, the microbiome features can be used for diagnostics, other characterizations, treatment, monitoring, and/or any other suitable purposes and/or approaches associated with sleep-related conditions. However, determining one or more sleep-related characterizations can be performed in any suitable manner.
4.3.B Determining a therapy.
Performing a characterization process S130 (e.g., performing a sleep-related therapy) can include Block S140, which can include determining one or more therapies (e.g., therapies configured to modulate microbiome composition, function, diversity, and/or other suitable aspects, such as for improving one or more aspects associated with sleep-related conditions, such as in users characterized based on one or more characterization processes; etc.). Block S140 can function to identify, select, rank, prioritize, predict, discourage, and/or otherwise determine therapies (e.g., facilitate therapy determination, etc.). For example, Block S140 can include determining one or more of probiotic-based therapies, bacteriophage-based therapies, small molecule-based therapies, and/or other suitable therapies, such as therapies that can shift a subject's microbiome composition, function, diversity, and/or other characteristics (e.g., microbiomes at any suitable sites, etc.) toward a desired state (e.g., equilibrium state, etc.) in promotion of a user's health, for modifying a state of one or more sleep-related conditions, and/or for other suitable purposes.
Therapies (e.g., sleep-related therapies, etc.) can include any one or more of: consumables (e.g., probiotic therapies, prebiotic therapies, medication, sleeping pills, melatonin supplements, allergy or cold medication, bacteriophage-based therapies, consumables for underlying conditions, small molecule therapies, etc.); device-related therapies (e.g., sleep-monitoring devices, such as a user device executing a sleep-monitoring application, sensor-based devices; dental guards; breathing devices; medical devices; implantable medical devices; sleep apnea devices such as continuous positive airway pressure devices, mandibular advancement devices, tongue retaining devices; stimulation devices such as electrostimulation devices, nerve stimulation devices; snoring prevention devices; catheters such as transtracheal catheters; nasal air filters; air quality devices such as air filtration devices; audio-based devices such as white noise machines; etc.); surgical operations (e.g., sleep apnea surgery; tonsillectomy; adenoidectomy; supraglottoplasty; turbinoplasty; septoplasty; septorhinoplasty; nasal surgeries; soft palate surgeries; oropharyngeal surgeries; hypopharyngeal surgeries; tracheostomies; maxillomandibular advancement; uvulopalatopharyngoplasty; hyoid suspension; genioglossus advancement; etc.); psychological-associated therapies (e.g., cognitive behavioral therapy, anxiety therapy, talking therapy, psychodynamic therapy, action-oriented therapy, rational emotive behavior therapy, interpersonal psychotherapy, relaxation training, deep breathing techniques, progressive muscle relaxation, sleep restriction therapy, meditation, etc.); behavior modification therapies (e.g., physical activity recommendations such as increased exercise; dietary recommendations such as reducing sugar intake, increased vegetable intake, increased fish intake, decreased caffeine consumption, decreased alcohol consumption, decreased carbohydrate intake; smoking recommendations such as decreasing tobacco intake; weight-related recommendations; sleep habit recommendations; device recommendations such as decreased electronic device usage before bedtime; recommendations in relation to other behaviors; etc.); topical administration therapies (e.g., bacteriophage-based therapies); environmental factor modification therapies; (e.g., adjusting lighting for sleep time and/or wake time; adjusting bedding-related factors; modification of other environmental factors; etc.); modification of any other suitable aspects associated with one or more sleep-related conditions; and/or any other suitable therapies (e.g., for improving a health state associated with one or more sleep-related conditions, such as therapies for improving one or more sleep-related conditions, therapies for reducing the risk of one or more sleep-related conditions, etc.). In examples, types of therapies can include any one or more of: probiotic therapies, bacteriophage-based therapies, small molecule-based therapies, cognitive/behavioral therapies, physical rehabilitation therapies, clinical therapies, medication-based therapies, diet-related therapies, and/or any other suitable therapy designed to operate in any other suitable manner in promoting a user's health.
In a variation, therapies can include one or more bacteriophage-based therapies (e.g., in the form of a consumable, in the form of a topical administration therapy, etc.), where one or more populations (e.g., in terms of colony forming units) of bacteriophages specific to a certain bacteria (or other microorganism) represented in the subject can be used to down-regulate or otherwise eliminate populations of the certain bacteria. As such, bacteriophage-based therapies can be used to reduce the size(s) of the undesired population(s) of bacteria represented in the subject. Additionally or alternatively, bacteriophage-based therapies can be used to increase the relative abundances of bacterial populations not targeted by the bacteriophage(s) used. However, bacteriophage-based therapies can be used to modulate characteristics of microbiomes (e.g., microbiome composition, microbiome function, etc.) in any suitable manner, and/or can be used for any suitable purpose.
In a variation, therapies can include one or more probiotic therapies and/or prebiotic therapies associated with any combination of at least one or more of (e.g., including any combination of one or more of, etc.) any suitable taxa described in Table 1 (e.g., in relation to therapies for a sleep-related condition of bad sleep quality, etc.) and/or Table 2 (e.g., in relation to therapies for a sleep-related condition for shift work, etc.) and/or: Anaerococcus sp. 8405254, Bacteroides nordii, Bacteroides sp. SLC1-38, Bifidobacterium merycicum, Blautia glucerasea, Blautia sp. YHC-4, Butyrivibrio crossotus, Catabacter hongkongensis, Catenibacterium mitsuokai, Collinsella aerofaciens, Collinsella intestinalis, Desulfovibrio piger, Eubacterium sp. SA11, Fusobacterium ulcerans, Lactobacillus sp. TAB-30, Megamonas funiformis, Megasphaera sp. S6-MB2, Olsenella sp. 1183, Phascolarctobacterium succinatutens, Streptococcus gordonii, Sutterella sp. YIT 12072, Sutterella wadsworthensis, Veillonella sp. AS16, Fusobacterium equinum, Facklamia sp. 1440-97, Anaerostipes sp. 3_2_56FAA, Pseudoclavibacter sp. Timone, Parvimonas micra, Lactobacillus sp. 66c, Bacteroides coprocola, Corynebacterium ulcerans, Anaerostipes sp. 1y-2, Sarcina ventriculi, Lactonifactor longoviformis, Enterococcus sp. C6I11, Eubacterium callanderi, Dialister invisus, Blautia sp. Ser8, Bacteroides plebeius, Bacteroides sp. 2_2_4, Anaerotruncus colihominis, Varibaculum cambriense, Actinomyces sp. S9 PR-21, Desulfovibrio sp., Prevotella disiens, Mobiluncus mulieris, Lactobacillus rhamnosus, Bifidobacterium sp. MSX5B, Acidaminococcus sp. D21, Bifidobacterium bifidum, Bacteroides sp. EBA5-17, Anaerococcus hydrogenalis, Alistipes sp. 627, Negativicoccus succinicivorans, Anaerococcus sp. 8404299, Butyricimonas synergistica, Actinomyces sp. ICM54, Turicibacter sanguinis, Blautia hydrogenotrophica, Parabacteroides goldsteinii, Bifidobacterium biavatii, Erysipelatoclostridium ramosum, Anaerofustis stercorihominis, Gardnerella vaginalis, Gordonibacter pamelaeae, Campylobacter hominis, Lactobacillus sp. BL302, Megasphaera sp. UPII 199-6, Peptoniphilus sp. gpac018A, Bifidobacterium stercoris, Butyricicoccus pullicaecorum, Megasphaera sp. S6-MB2, Corynebacterium sp., Dialister propionicifaciens, Anaerococcus tetradius, Eggerthella sp. HGA1, Peptoniphilus sp. 7-2, Terrisporobacter glycolicus, Peptoniphilus sp. 2002-2300004, Bacteroides sp. CB57, Streptococcus pasteurianus, Megasphaera genomosp. C1, Holdemania filiformis, Coprobacillus sp. D6, Dielma fastidiosa, Sutterella stercoricanis, Brevibacterium massiliense, Bacteroides stercorirosoris, Lactobacillus sp. Akhmrol, Actinomyces sp. ICM47, Lactobacillus crispatus, Prevotella bivia, Enterobacter sp. BS2-1, Streptococcus sp. BS35a, Anaerotruncus sp. NML 070203, Haemophilus parainfluenzae, Peptoniphilus coxii, Granulicatella adiacens, Campylobacter ureolyticus, Bifidobacterium longum, Bacteroides clarus, Bacteroides sp. XB12B, Streptococcus agalactiae, Kluyvera georgiana, Flavonifractor plautii, Paraprevotella clara, Stenotrophomonas sp. C-S-TSA3, Bacteroides sp. DJF_B097, Herbaspirillum seropedicae, Streptococcus sp. oral taxon G59, Eisenbergiella tayi, Coprobacter fastidiosus, Oligella urethralis, Akkermansia muciniphila, Desulfovibrio desulfuricans, Streptococcus peroris, Anaerococcus octavius, Atopobium vaginae, Parabacteroides sp. 157, Bifidobacterium choerinum, Porphyromonas uenonis, Dermabacter hominis, Alistipes indistinctus, Weissella hellenica, Alistipes massiliensis, Butyricimonas virosa, Alistipes putredinis, Actinobacillus porcinus, Howardella ureilytica, Veillonella sp. CM60, Porphyromonas sp. 2026, Delftia sp. BN-SKY3, Peptostreptococcus anaerobius, Citrobacter sp. BW4, Alistipes sp. RMA 9912, Bacteroides vulgatus, Lactobacillus sp. TAB-26, Bifidobacterium sp., Bifidobacterium kashiwanohense, Butyricimonas sp. JCM 18677, and/or any other suitable microorganisms associated with any suitable taxonomic groups (e.g., microorganisms from taxa described herein, such as in relation to microbiome features, etc.). For one or more probiotic therapies and/or other suitable therapies, microorganisms associated with a given taxonomic group, and/or any suitable combination of microorganisms can be provided at dosages of 0.1 million to 10 billion CFU, and/or at any suitable amount (e.g., as determined from a therapy model that predicts positive adjustment of a patient's microbiome in response to the therapy, etc.). In an example, a subject can be instructed to ingest capsules including the probiotic formulation according to a regimen tailored to one or more of his/her: physiology (e.g., body mass index, weight, height), demographic characteristics (e.g., gender, age), severity of dysbiosis, sensitivity to medications, and any other suitable factor.
In a specific example of probiotic therapies, as shown in
In variations, the therapy model is preferably based upon data from a large population of subjects, which can include the population of subjects from which the microbiome diversity datasets are derived in Block S110, where microbiome composition and/or functional features or states of health, prior exposure to and post exposure to a variety of therapeutic measures, are well characterized. Such data can be used to train and validate the therapy provision model, in identifying therapeutic measures that provide desired outcomes for subjects based upon different sleep-related characterizations. In variations, support vector machines, as a supervised machine learning algorithm, can be used to generate the therapy provision model. However, any other suitable machine learning algorithm described above can facilitate generation of the therapy provision model.
Additionally or alternatively, the therapy model can be derived in relation to identification of a “normal” or baseline microbiome composition and/or functional features, as assessed from subjects of a population of subjects who are identified to be in good health. Upon identification of a subset of subjects of the population of subjects who are characterized to be in good health (e.g., using features of the characterization process), therapies that modulate microbiome compositions and/or functional features toward those of subjects in good health can be generated in Block S140. Block S140 can thus include identification of one or more baseline microbiome compositions and/or functional features (e.g., one baseline microbiome for each of a set of demographic characteristics), and potential therapy formulations and therapy regimens that can shift microbiomes of subjects who are in a state of dysbiosis toward one of the identified baseline microbiome compositions and/or functional features. The therapy model can, however, be generated and/or refined in any other suitable manner.
Microorganism compositions associated with probiotic therapies and/or prebiotic therapies (e.g., associated with probiotic therapies determined by a therapy model applied by a therapy facilitation system, etc.) can include microorganisms that are culturable (e.g., able to be expanded to provide a scalable therapy) and/or non-lethal (e.g., non-lethal in their desired therapeutic dosages). Furthermore, microorganism compositions can include a single type of microorganism that has an acute or moderated effect upon a subject's microbiome. Additionally or alternatively, microorganism compositions can include balanced combinations of multiple types of microorganisms that are configured to cooperate with each other in driving a subject's microbiome toward a desired state. For instance, a combination of multiple types of bacteria in a probiotic therapy can include a first bacteria type that generates products that are used by a second bacteria type that has a strong effect in positively affecting a subject's microbiome. Additionally or alternatively, a combination of multiple types of bacteria in a probiotic therapy can include several bacteria types that produce proteins with the same functions that positively affect a subject's microbiome.
Probiotic and/or prebiotic compositions can be naturally or synthetically derived. For instance, in one application, a probiotic composition can be naturally derived from fecal matter or other biological matter (e.g., of one or more subjects having a baseline microbiome composition and/or functional features, as identified using the characterization process and the therapy model). Additionally or alternatively, probiotic compositions can be synthetically derived (e.g., derived using a benchtop method) based upon a baseline microbiome composition and/or functional features, as identified using the characterization process and the therapy model. In variations, microorganism agents that can be used in probiotic therapies can include one or more of: yeast (e.g., Saccharomyces boulardii), gram-negative bacteria (e.g., E. coli Nissle), gram-positive bacteria (e.g., Bifidobacteria bifidum, Bifidobacteria infantis, Lactobacillus rhamnosus, Lactococcus lactis, Lactobacillus plantarum, Lactobacillus acidophilus, Lactobacillus casei, Bacillus polyfermenticus, etc.), and any other suitable type of microorganism agent. However, probiotic therapies, prebiotic therapies and/or other suitable therapies can include any suitable combination of microorganisms associated with any suitable taxa described herein, and/or therapies can be configured in any suitable manner.
Block S140 can include executing, storing, retrieving, and/or otherwise processing one or more therapy models for determining one or more therapies. Processing one or more therapy models is preferably based on microbiome features. For example, generating a therapy model can based on microbiome features associated with one or more sleep-related conditions, therapy-related aspects such as therapy efficacy in relation to microbiome characteristics, and/or other suitable data. Additionally or alternatively, processing therapy models can be based on any suitable data. In an example, processing a therapy model can include determining one or more therapies for a user based on one or more therapy models, user microbiome features (e.g., inputting user microbiome feature values into the one or more therapy models, etc.), supplementary data (e.g., prior knowledge associated with therapies such as in relation to microorganism-related metabolization; user medical history; user demographic data, such as describing demographic characteristics; etc.), and/or any other suitable data. However, processing therapy models can be based on any suitable data in any suitable manner.
Sleep-related characterization models can include one or more therapy models. In an example, determining one or more sleep-related characterizations (e.g., for one or more users, for one or more sleep-related conditions, etc.), can include determining one or more therapies, such as based on one or more therapy models (e.g., applying one or more therapy models, etc.) and/or other suitable data (e.g., microbiome features such as user microbiome features, microorganism dataset such as user microorganism datasets, etc.). In a specific example, determining one or more sleep-related characterizations can include determining a first sleep-related characterization for a user (e.g., describing propensity for one or more sleep-related conditions; etc.); and determining a second sleep-related characterization for the user based on the first sleep-related characterization (e.g., determining one or more therapies, such as for recommendation to a user, based on the propensity for one or more sleep-related conditions; etc.). In a specific example, a sleep-related characterization can include both propensity-related data (e.g., diagnostic data; associated microbiome composition, function, diversity, and/or other characteristics; etc.) and therapy-related data (e.g., recommended therapies; potential therapies; etc.). However, sleep-related characterizations can include any suitable data (e.g., any combination of data described herein, etc.).
Processing therapy models can include processing a plurality of therapy models. For example, different therapy models can be processed for different therapies (e.g., different models for different individual therapies; different models for different combinations and/or categories of therapies, such as a first therapy model for determining consumable therapies and a second therapy model for determining psychological-associated therapies; etc.). In an example, different therapy models can be processed for different sleep-related conditions, (e.g., different models for different individual sleep-related conditions; different models for different combinations and/or categories of sleep-related conditions, such as a first therapy model for determining therapies for insomnias, and a second therapy model for determining therapies for hypersomnias, etc.). Additionally or alternatively, processing a plurality of therapy models can be performed for (e.g., based on; processing different therapy models for; etc.) any suitable types of data and/or entities. However, processing a plurality of therapy models can be performed in any suitable manner, and determining and/or applying one or more therapy models can be performed in any suitable manner.
The method 100 can additionally or alternatively include Block S150, which can include processing one or more biological samples from a user (e.g., biological samples from different collection sites of the user, etc.). Block S150 can function to facilitate generation of a microorganism dataset for a user, such as for use in deriving inputs for the characterization process (e.g., for generating a sleep-related characterization for the user, such as through applying one or more sleep-related characterization models, etc.). As such, Block S150 can include receiving, processing, and/or analyzing one or more biological samples from one or more users (e.g., multiple biological samples for the same user over time, different biological samples for different users, etc.). In Block S150, the biological sample is preferably generated from the user and/or an environment of the user in a non-invasive manner. In variations, non-invasive manners of sample reception can use any one or more of: a permeable substrate (e.g., a swab configured to wipe a region of a user's body, toilet paper, a sponge, etc.), a non-permeable substrate (e.g., a slide, tape, etc.) a container (e.g., vial, tube, bag, etc.) configured to receive a sample from a region of a user's body, and any other suitable sample-reception element. In a specific example, the biological sample can be collected from one or more of the user's nose, skin, genitals, mouth, and gut (e.g., through stool samples, etc.) in a non-invasive manner (e.g., using a swab and a vial). However, the biological sample can additionally or alternatively be received in a semi-invasive manner or an invasive manner. In variations, invasive manners of sample reception can use any one or more of: a needle, a syringe, a biopsy element, a lance, and any other suitable instrument for collection of a sample in a semi-invasive or invasive manner. In specific examples, samples can include blood samples, plasma/serum samples (e.g., to enable extraction of cell-free DNA), and tissue samples.
In the above variations and examples, the biological sample can be taken from the body of the user without facilitation by another entity (e.g., a caretaker associated with a user, a health care professional, an automated or semi-automated sample collection apparatus, etc.), or can alternatively be taken from the body of the user with the assistance of another entity. In one example, where the biological sample is taken from the user without facilitation by another entity in the sample extraction process, a sample-provision kit can be provided to the user. In the example, the kit can include one or more swabs for sample acquisition, one or more containers configured to receive the swab(s) for storage, instructions for sample provision and setup of a user account, elements configured to associate the sample(s) with the user (e.g., barcode identifiers, tags, etc.), and a receptacle that allows the sample(s) from the user to be delivered to a sample processing operation (e.g., by a mail delivery system). In another example, where the biological sample is extracted from the user with the help of another entity, one or more samples can be collected in a clinical or research setting from the user (e.g., during a clinical appointment). The biological sample can, however, be received from the user in any other suitable manner.
Furthermore, processing and analyzing biological samples (e.g., to generate a user microorganism dataset; etc.) from the user is preferably performed in a manner similar to that of one of the embodiments, variations, and/or examples of sample reception described in relation to Block 110 above, and/or any other suitable portions of the method 100 and/or system 200. As such, reception and processing of the biological sample in Block S150 can be performed for the user using similar processes as those for receiving and processing biological samples used to perform the characterization processes of the method 100, such as in order to provide consistency of process. However, biological sample reception and processing in Block S150 can additionally or alternatively be performed in any other suitable manner.
The method 100 can additionally or alternatively include Block S160, which can include determining, with one or more characterization processes (e.g., one or more characterization processes described in relation to Block S130, etc.), a sleep-related characterization for the user, such as based upon processing one or more microorganism dataset (e.g., user microorganism sequence dataset, microbiome composition dataset, microbiome functional diversity dataset; processing of the microorganism dataset to extract user microbiome features that can be used to determine the one or more sleep-related characterizations; etc.) derived from the biological sample of the user. Block S160 can function to characterize one or more sleep-related conditions for a user, such as through extracting features from microbiome-derived data of the user, and using the features as inputs into an embodiment, variation, or example of the characterization process described in Block S130 above (e.g., using the user microbiome feature values as inputs into a microbiome-related condition characterization model, etc.). In an example, Block S160 can include generating a sleep-related characterization for the user based on user microbiome features and a sleep-related condition model (e.g., generated in Block S130). Sleep-related characterizations can be for any number and/or combination of sleep-related conditions (e.g., a combination of sleep-related conditions, a single sleep-related condition, and/or other suitable sleep-related conditions; etc.), users, collection sites, and/or other suitable entities. Sleep-related characterizations can include one or more of: diagnoses (e.g., presence or absence of a sleep-related condition; etc.); risk (e.g., risk scores for developing and/or the presence of a sleep-related condition; information regarding sleep-related characterizations (e.g., symptoms, signs, triggers, associated conditions, etc.); comparisons (e.g., comparisons with other subgroups, populations, users, historic health statuses of the user such as historic microbiome compositions and/or functional diversities; comparisons associated with sleep-related conditions; etc.); therapy determinations; other suitable outputs associated with characterization processes; and/or any other suitable data.
In another variation, a sleep-related characterization can include a microbiome diversity score (e.g., in relation to microbiome composition, function, etc.) associated with (e.g., correlated with; negatively correlated with; positively correlated with; etc.) a microbiome diversity score correlated with one or more sleep-related conditions. In examples, the sleep-related characterization can include microbiome diversity scores over time (e.g., calculated for a plurality of biological samples of the user collected over time), comparisons to microbiome diversity scores for other users, and/or any other suitable type of microbiome diversity score. However, processing microbiome diversity scores (e.g., determining microbiome diversity scores; using microbiome diversity scores to determine and/or provide therapies; etc.) can be performed in any suitable manner.
Determining a sleep-related characterization in Block S160 preferably includes determining features and/or combinations of features associated with the microbiome composition and/or functional features of the user (e.g., determining feature values associated with the user, the feature values corresponding to microbiome features determined in Block S130, etc.), inputting the features into the characterization process, and receiving an output that characterizes the user as belonging to one or more of: a behavioral group, a gender group, a dietary group, a disease-state group, and any other suitable group capable of being identified by the characterization process. Block S160 can additionally or alternatively include generation of and/or output of a confidence metric associated with the characterization of the user. For instance, a confidence metric can be derived from the number of features used to generate the characterization, relative weights or rankings of features used to generate the characterization, measures of bias in the characterization process, and/or any other suitable parameter associated with aspects of the characterization process. However, leveraging user microbiome features can be performed in any suitable manner to generate any suitable sleep-related characterizations.
In some variations, features extracted from the microorganism dataset of the user can be supplemented with supplementary features (e.g., extracted from supplementary data collected for the user; such as survey-derived features, medical history-derived features, sensor data, etc.), where such data, the user microbiome data, and/or other suitable data can be used to further refine the characterization process of Block S130, Block S160, and/or other suitable portions of the method 100.
Determining a sleep-related characterization preferably includes extracting and applying user microbiome features (e.g., user microbiome composition diversity features; user microbiome functional diversity features; etc.) for the user (e.g., based on a user microorganism dataset), characterization models, and/or other suitable components, such as by employing processes described in Block S130, and/or by employing any suitable approaches described herein.
In variations, as shown in
As shown in
For example, a combination of commercially available probiotic supplements can include a suitable probiotic therapy for the user according to an output of the therapy model. In another example, the method 100 can include determining a sleep-related condition risk for the user for the sleep-related condition based on a sleep-related condition model (e.g., and/or user microbiome features); and promoting a therapy to the user based on the sleep-related condition risk.
In a variation, facilitating therapeutic intervention can include promoting a diagnostic procedure (e.g., for facilitating detection of sleep-related conditions, which can motivate subsequent promotion of other therapies, such as for modulation of a user microbiome for improving a user health state associated with one or more sleep-related conditions; etc.). Diagnostic procedures can include any one or more of: medical history analyses, imaging examinations, cell culture tests, antibody tests, skin prick testing, patch testing, blood testing, challenge testing, performing portions of the method 100, and/or any other suitable procedures for facilitating the detecting (e.g., observing, predicting, etc.) of sleep-related conditions. Additionally or alternatively, diagnostic device-related information and/or other suitable diagnostic information can be processed as part of a supplementary dataset (e.g., in relation to Block S120, where such data can be used in determining and/or applying characterization models, therapy models, and/or other suitable models; etc.), and/or collected, used, and/or otherwise processed in relation to any suitable portions of the method 100 (e.g., administering diagnostic procedures for users for monitoring therapy efficacy in relation to Block S180; etc.)
In another variation, Block S170 can include promoting a bacteriophage-based therapy. In more detail, one or more populations (e.g., in terms of colony forming units) of bacteriophages specific to a certain bacteria (or other microorganism) represented in the user can be used to down-regulate or otherwise eliminate populations of the certain bacteria. As such, bacteriophage-based therapies can be used to reduce the size(s) of the undesired population(s) of bacteria represented in the user. Complementarily, bacteriophage-based therapies can be used to increase the relative abundances of bacterial populations not targeted by the bacteriophage(s) used.
In another variation, facilitating therapeutic intervention (e.g., providing therapies, etc.) can include provision of notifications to a user regarding the recommended therapy, other forms of therapy, sleep-related characterizations, and/or other suitable data. In a specific example, providing a therapy to a user can include providing therapy recommendations (e.g., substantially concurrently with providing information derived from a sleep-related characterization for a user; etc.) and/or other suitable therapy-related information (e.g., therapy efficacy; comparisons to other individual users, subgroups of users, and/or populations of users; therapy comparisons; historic therapies and/or associated therapy-related information; psychological therapy guides such as for cognitive behavioral therapy; etc.), such as through presenting notifications at a web interface (e.g., through a user account associated with and identifying a user; etc.). Notifications can be provided to a user by way of an electronic device (e.g., personal computer, mobile device, tablet, wearable, head-mounted wearable computing device, wrist-mounted wearable computing device, etc.) that executes an application, web interface, and/or messaging client configured for notification provision. In one example, a web interface of a personal computer or laptop associated with a user can provide access, by the user, to a user account of the user, where the user account includes information regarding the user's sleep-related characterization, detailed characterization of aspects of the user's microbiome (e.g., in relation to correlations with sleep-related conditions; etc.), and/or notifications regarding suggested therapeutic measures (e.g., generated in Blocks S140 and/or S170, etc.). In another example, an application executing at a personal electronic device (e.g., smart phone, smart watch, head-mounted smart device) can be configured to provide notifications (e.g., at a display, haptically, in an auditory manner, etc.) regarding therapy suggestions generated by the therapy model of Block S170. Notifications and/or probiotic therapies can additionally or alternatively be provided directly through an entity associated with a user (e.g., a caretaker, a spouse, a significant other, a healthcare professional, etc.). In some further variations, notifications can additionally or alternatively be provided to an entity (e.g., healthcare professional) associated with a user, such as where the entity is able to facilitate provision of the therapy (e.g., by way of prescription, by way of conducting a therapeutic session, through a digital telemedicine session using optical and/or audio sensors of a computing device, etc.). Providing notifications and/or otherwise facilitating therapeutic, however, be performed in any suitable manner.
As shown in
Monitoring of a user during the course of a therapy promoted by the therapy model (e.g., by receiving and analyzing biological samples from the user throughout therapy, by receiving survey-derived data from the user throughout therapy) can thus be used to generate a therapy-effectiveness model for each characterization provided by the characterization process of Block S130, and each recommended therapy measure provided in Blocks S140 and S170.
In Block S180, the user can be prompted to provide additional biological samples, supplementary data, and/or other suitable data at one or more key time points of a therapy regimen that incorporates the therapy, and the additional biological sample(s) can be processed and analyzed (e.g., in a manner similar to that described in relation to Block S120) to generate metrics characterizing modulation of the user's microbiome composition and/or functional features. For instance, metrics related to one or more of: a change in relative abundance of one or more taxonomic groups represented in the user's microbiome at an earlier time point, a change in representation of a specific taxonomic group of the user's microbiome, a ratio between abundance of a first taxonomic group of bacteria and abundance of a second taxonomic group of bacteria of the user's microbiome, a change in relative abundance of one or more functional families in a user's microbiome, and any other suitable metrics can be used to assess therapy effectiveness from changes in microbiome composition and/or functional features. Additionally or alternatively, survey-derived data from the user, pertaining to experiences of the user while on the therapy (e.g., experienced side effects, personal assessment of improvement, behavioral modifications, symptom improvement, etc.) can be used to determine effectiveness of the therapy in Block S180. For example, the method 100 can include receiving a post-therapy biological sample from the user; collecting a supplementary dataset from the user, where the supplementary dataset describes user adherence to a therapy (e.g., a determined and promoted therapy) and/or other suitable user characteristics (e.g., behaviors, conditions, etc.); generating a post-therapy sleep-related characterization of the first user in relation to the sleep-related condition based on the sleep-related characterization model and the post-therapy biological sample; and promoting an updated therapy to the user for the sleep-related condition based on the post-therapy sleep-related characterization (e.g., based on a comparison between the post-therapy sleep-related characterization and a pre-therapy sleep-related characterization; etc.) and/or the user adherence to the therapy (e.g., modifying the therapy based on positive or negative results for the user microbiome in relation to the sleep-related condition; etc.). Additionally or alternatively, other suitable data (e.g., supplementary data describing user behavior associated with one or more sleep-related conditions; supplementary data describing a sleep-related condition such as observed symptoms; etc.) can be used in determining a post-therapy characterization (e.g., degree of change from pre- to post-therapy in relation to the sleep-related condition; etc.), updated therapies (e.g., determining an updated therapy based on effectiveness and/or adherence to the promoted therapy, etc.).
In an example, the method 100 can include collecting first sleep-tracking data (e.g., at least one of first survey-derived data and first device data) and/or other suitable supplementary, where the first sleep-tracking data is associated with sleep quality of the user; determining the sleep-related characterization for the user based on the user microbiome features and the first sleep-tracking data; facilitating therapeutic intervention based on the sleep-related characterization; collecting a post-therapy biological sample from the user; collecting second sleep-tracking data (e.g., including at least one of second survey-derived data and second device data; etc.) and/or other suitable supplementary data, where the second sleep-tracking data is associated with the sleep quality of the user; and determining a post-therapy sleep-related characterization for the user for the sleep-related condition based on the second sleep-tracking data and post-therapy microbiome features associated with the post-therapy biological sample. In the example, the method 100 can include facilitating therapeutic intervention in relation to an updated therapy (e.g., a modification of the therapy; a different therapy; etc.) for the user for improving the sleep-related condition, based on the post-therapy sleep-related characterization, such as where the updated therapy can include at least one of a consumable, a device-related therapy, a surgical operation, a psychological-associated therapy, a behavior modification therapy, and an environmental factor modification therapy. In the example determining the post-therapy sleep-related characterization can include determining a comparison between microbiome characteristics of the user and reference microbiome characteristics corresponding to a user subgroup sharing at least one of a behavior and an environmental factor (and/or other suitable characteristic) associated with the sleep-related condition, based on the post-therapy microbiome features, and where facilitating therapeutic intervention in relation to the updated therapy can include presenting the comparison to the user for facilitating at least one of the behavior modification therapy and the environmental factor modification therapy and/or other suitable therapies. However, Block S180 can be performed in relation to additional biological samples, additional supplementary data, and/or other suitable additional data in any suitable manner.
Therapy effectiveness, processing of additional biological samples (e.g., to determine additional sleep-related characterizations, therapies, etc.), and/or other suitable aspects associated with continued biological sample collection, processing, and analysis in relation to sleep-related conditions can be performed at any suitable time and frequency for generating, updating, and/or otherwise processing models (e.g., characterization models, therapy models, etc.), and/or for any other suitable purpose (e.g., as inputs associated with other portions of the method 100). However, Block S180 can be performed in any suitable manner.
The method 100 can, however, include any other suitable blocks or steps configured to facilitate reception of biological samples from subjects, processing of biological samples from subjects, analyzing data derived from biological samples, and generating models that can be used to provide customized diagnostics and/or probiotic-based therapeutics according to specific microbiome compositions and/or functional features of subjects.
Embodiments of the system 200 and/or method 100 can include every combination and permutation of the various system components and the various method processes, including any variants (e.g., embodiments, variations, examples, specific examples, figures, etc.), where portions of the method 100 and/or processes described herein can be performed asynchronously (e.g., sequentially), concurrently (e.g., in parallel), or in any other suitable order by and/or using one or more instances, elements, components of, and/or other aspects of the system 200 and/or other entities described herein.
Any of the variants described herein (e.g., embodiments, variations, examples, specific examples, figures, etc.) and/or any portion of the variants described herein can be additionally or alternatively combined, aggregated, excluded, used, and/or otherwise applied.
The system 200 and/or method 100 and/or variants thereof can be embodied and/or implemented at least in part as a machine configured to receive a computer-readable medium storing computer-readable instructions. The instructions are preferably executed by computer-executable components preferably integrated with the system. The computer-readable medium can be stored on any suitable computer-readable media such as RAMs, ROMs, flash memory, EEPROMs, optical devices (CD or DVD), hard drives, floppy drives, or any suitable device. The computer-executable component is preferably a general or application specific processor, but any suitable dedicated hardware or hardware/firmware combination device can alternatively or additionally execute the instructions.
As a person skilled in the art will recognize from the previous detailed description and from the figures and claims, modifications and changes can be made to the system 200, method 100, and/or variants without departing from the scope defined in the following claims.
Anaerobacter
Sphingomonas
Bacteroides
Cryobacterium
Corynebacterium
epidermidicanis
Bacteroides
Enterococcus
Mesorhizobium
thetaiotaomicron
raffinosus
Bacteroides
Actinomyces sp.
uniformis
Bacteroides
Megasphaera sp.
vulgatus
Roseburia
Desulfovibrio sp.
Sphingobium sp.
Faecalibacterium
Pseudomonas
Anaerococcus sp.
prausnitzii
monteilii
Desulfovibrio
Bifidobacterium
merycicum
Desulfovibrio sp.
Bifidobacterium
Faecalibacterium
pullorum
Murdochiella
Anaerovibrio
Lachnoanaerobaculum
Anaerosinus
Streptococcus sp.
glycerini
Sarcina
Varibaculum sp.
Papillibacter
Dermabacter sp.
Streptococcus
Coprobacillus
Propionibacterium
Clostridium
Anaerostipes
Stomatobaculum
caccae
Parasporobacterium
Actinomyces sp.
paucivorans
Lachnospira
Bifidobacterium
Atopobium sp.
Lachnospira
Campylobacter
Atopobium sp.
pectinoschiza
faecalis
Oscillospira
Gardnerella sp.
guilliermondii
Fusobacterium
Prevotella sp. S4-
equinum
Asaccharospora
Corynebacterium
irregularis
Bacillus sp. HC15
Streptococcus sp.
Phascolarctobacterium
Weissella cibaria
Veillonella sp.
Phascolarctobacterium
Alloprevotella
faecium
Collinsella
Dialister sp.
intestinalis
Dorea
Anaerosinus
Stenotrophomonas
formicigenerans
Sutterella
Dysgonomonas
Bradyrhizobium
Pseudobutyrivibrio
Bifidobacterium
Anaerococcus sp.
scardovii
Bacteroides
Enterococcus
Anaerococcus sp.
caccae
pallens
Leuconostoc inhae
Finegoldia sp. S9
Holdemania
Blautia schinkii
Murdochiella sp.
Holdemania
Peptoniphilus sp.
filiformis
Corynebacterium
Ralstonia sp. A52
Staphylococcus
Gelria
Senegalimassilia
Eggerthella
Sedimentibacter
Peptoniphilus sp.
Anaerofustis
Romboutsia
stercorihominis
Corynebacterium
Veillonella
ciconiae
seminalis
Bacteroides
Eggerthella
Terrisporobacter
acidifaciens
sinensis
Blautia luti
Slackia faecicanis
Acetanaerobacterium
Bacteroides sp.
Anaerosporobacter
mobilis
Bacteroides sp.
Anaerofustis
Collinsella
Lactobacillus
kefiranofaciens
Oscillospira
Veillonella sp. ADV
Catabacter
Deinococcus
Roseburia
Catabacter
Lactococcus
intestinalis
hongkongensis
Pseudoclavibacter
bifida
Bacteroides sp.
Smarlab 3301643
Shuttleworthia
Campylobacter
showae
Proteiniphilum
Comamonas
Streptococcus sp.
Neisseria
flavescens
Bacteroides sp.
Neisseria canis
Bacteroides sp.
Eikenella
corrodens
Dorea
Alistipes
Aggregatibacter
onderdonkii
aphrophilus
Mitsuokella sp.
Aggregatibacter
segnis
Oscillibacter
Rodentibacter
valericigenes
pneumotropicus
Bifidobacterium
tsurumiense
Megasphaera sp.
Selenomonas
Anaerococcus sp.
Capnocytophaga
Lactococcus sp. D2
Capnocytophaga
gingivalis
Weissella sp. H1a
Capnocytophaga
ochracea
Anaerostipes
Barnesiella
Capnocytophaga
viscericola
sputigena
Pediococcus sp.
Cronobacter
Streptococcus
dublinensis
mutans
Faecalibacterium
Cronobacter
Atopobium rimae
turicensis
Elusimicrobium
Lactobacillus
paracasei
Alistipes
Actinomyces
viscosus
Akkermansia
Cellulosilyticum
Bifidobacterium
ruminicola
adolescentis
Akkermansia
Bacteroides sp.
Pseudopropionibacterium
muciniphila
propionicum
Hespellia
Paraprevotella
Cardiobacterium
xylaniphila
Anaerotruncus
Bacteroides sp.
Cardiobacterium
hominis
Marvinbryantia
Parabacteroides sp.
Tannerella
forsythia
Subdoligranulum
Nosocomiicoccus
Porphyromonas
endodontalis
Flavonifractor
Nosocomiicoccus
Prevotella
plautii
ampullae
intermedia
Bacteroides
Megamonas
Prevotella
finegoldii
rupellensis
nigrescens
Roseburia
Butyricicoccus
Prevotella oris
inulinivorans
pullicaecorum
Blautia wexlerae
Cloacibacterium
Prevotella
rupense
oulorum
Lactonifactor
Fusobacterium sp.
Dolosigranulum
Moryella
Mitsuokella sp.
Dolosigranulum
pigrum
Adlercreutzia
Roseburia sp.
Acetitomaculum
equolifaciens
Adlercreutzia
Terrisporobacter
glycolicus
Pantoea sp.
Leptotrichia
buccalis
Butyricimonas
Porphyromonas
synergistica
catoniae
Selenomonas sp.
Corynebacterium
matruchotii
Asaccharobacter
Catonella
Blautia
Coprobacillus sp.
Catonella morbi
Roseburia sp.
Bifidobacterium
Filifactor
Bacteroides sp.
Bacteroides sp. S-
Actinomyces
georgiae
Alistipes sp.
Butyricicoccus
Actinomyces
gerencseriae
Blautia faecis
Bacteroides sp.
Hydrogenoanaerobacterium
Prevotella pallens
Bacteroides fluxus
Corynebacterium
durum
Bacteroides
Alloprevotella
oleiciplenus
tannerae
Eggerthella sp.
Slackia piriformis
Centipeda
Flavonifractor
Collinsella
Centipeda
tanakaei
periodontii
Christensenella
Eggerthella lenta
minuta
Anaerostipes sp.
Succinatimonas
Cryptobacterium
hippei
Fusicatenibacter
Desulfovibrio sp.
Cryptobacterium
saccharivorans
curtum
Intestinimonas
Pyramidobacter
Rothia sp. CCUG
piscolens
Fusicatenibacter
Lactobacillus sp.
Mogibacterium
pumilum
Candidatus
Pseudoflavonifractor
Soleaferrea
capillosus
Peptoclostridium
Asaccharospora
Erysipelatoclostridium
Bacteroides sp. TP-5
Filifactor alocis
Flavobacterium
Anaerostipes
Turicibacter
butyraticus
sanguinis
Aeromonas sp. B11
Leptotrichia
wadei
Kluyvera
Parabacteroides sp.
Leptotrichia
shahii
Desulfovibrio sp.
Rothia aeria
Haemophilus
Enterorhabdus
Victivallis
caecimuris
Haemophilus
Bacteroides faecis
Turicibacter
parainfluenzae
Bacteroides
Succinatimonas
Tannerella
fragilis
Parabacteroides
Blautia sp. Ser5
distasonis
Butyrivibrio
Eubacterium sp.
Porphyromonas
Bacteroides
rodentium
Prevotella
Bacteroides
chinchillae
Cellulosilyticum
Selenomonas
Streptococcus
Caldicoprobacter
thermophilus
Erysipelatoclostridium
Enterobacter sp.
ramosum
Lactobacillus
Lactobacillus sp.
Bifidobacterium
Capnocytophaga
biavatii
Bifidobacterium
Megasphaera sp.
Capnocytophaga
Corynebacterium
Pseudomonas sp.
Capnocytophaga
Corynebacterium
Rothia sp. RV13
Aggregatibacter
Klebsiella sp.
Prevotella
nanceiensis
Lactococcus sp.
Actinomyces
massiliensis
Campylobacter sp.
Lachnoanaerobaculum
saburreum
Methanobrevibacter
Lactobacillus sp.
Bacteroides sp.
Methanobrevibacter
Leuconostoc sp.
Olsenella sp.
smithii
Gardnerella
Leptotrichia
hongkongensis
Gardnerella
Christensenella
vaginalis
Peptococcus
Bacteroides sp.
Bifidobacterium
stercoris
Alistipes
Parabacteroides sp.
Neisseria
putredinis
shayeganii
Odoribacter
Bacteroides sp.
Rhizobium sp.
splanchnicus
Anaerovibrio sp.
Fretibacterium
fastidiosum
Anaerovibrio sp.
Oribacterium sp.
Acidaminococcus
Leptotrichia sp.
Mollicutes
Finegoldia sp.
Alloprevotella
rava
Bilophila
Lactococcus sp.
Prevotella sp.
Sutterella
Herbaspirillum sp.
Neisseria
wadsworthensis
skkuensis
Phascolarctobacterium
Capnocytophaga
Butyrivibrio
Peptococcus sp.
Capnocytophaga
crossotus
Parabacteroides
Proteiniclasticum
Actinomyces sp.
merdae
Bacteroides
Bacteroides sp.
Streptococcus sp.
stercoris
Turicibacter sp.
Tannerella sp.
Kluyvera
Cloacibacillus
Capnocytophaga
georgiana
porcorum
Collinsella
Propionibacterium
Fusobacterium
aerofaciens
Slackia
Butyricimonas sp.
Oribacterium sp.
Butyricimonas sp.
Brevundimonas
Olsenella sp. 1183
Moraxella sp.
Dorea
Anaerostipes
Pseudomonas sp.
longicatena
rhamnosivorans
Butyricimonas sp.
Lysinibacillus sp.
Streptococcus sp.
Pseudoflavonifractor
Streptococcus sp.
Neisseria oralis
Peptoniphilus
Veillonella sp.
Actinomyces sp.
Anaerococcus
Sutterella sp. 252
Rothia sp. THG-
Thalassospira
Roseburia sp. 499
Anaerostipes sp.
Tessaracoccus
lapidicaptus
Rahnella sp.
Fretibacterium
Citrobacter sp.
Dielma
Megasphaera sp.
Alistipes inops
Megasphaera sp.
Pseudomonas
aeruginosa
Megasphaera sp.
Moraxella
catarrhalis
Subdoligranulum
Candidatus
Enterobacter
variabile
Methanomethylophilus
cloacae
Bifidobacterium
Rahnella sp. BSP18
Klebsiella oxytoca
longum
Peptoniphilus
Bacteroides
Aeromonas
caecigallinarum
Sutterella
Bacteroides sp.
Rhodobacter
stercoricanis
Odoribacter
Pediococcus sp.
Bacteroides
Streptococcus sp.
Leuconostoc
Parabacteroides
Leuconostoc
goldsteinii
mesenteroides
Bacteroides sp.
Leuconostoc
lactis
Parabacteroides
Leuconostoc
carnosum
Barnesiella
Pediococcus
Howardella
Clostridioides
Weissella confusa
Alistipes sp.
Lactobacillus
delbrueckii
Oscillibacter
Klebsiella
Carnobacterium
pneumoniae
Alistipes sp.
Aeromonas
Lactobacillus
salmonicida
curvatus
Barnesiella
Streptococcus
Bacteroides
intestinihominis
sobrinus
ovatus
Parasutterella
Nocardioides
Rahnella
excrementihominis
Porphyromonas
Weissella
bennonis
Acidovorax
Weissella
hellenica
Variovorax
Raoultella
ornithinolytica
Butyricimonas
Pseudomonas
citronellolis
Parasutterella
Pseudomonas
monteilii
Bifidobacterium
kashiwanohense
Anaerosporobacter
Massilia
Corynebacterium
Staphylococcus
Fusobacterium
canis
equorum
equinum
Bilophila sp.
Blautia stercoris
Raoultella
Alistipes sp.
Aerococcus sp.
Bacteroides sp.
Defluviimonas
Corynebacterium
atypicum
Terrisporobacter
Yersinia
Intestinibacter
Yersinia
enterocolitica
Lactococcus
Bacillus cereus
Pseudoclavibacter
bifida
Bacteroides sp.
Planomicrobium
Paucibacter
Phascolarctobacterium
Rothia sp. BBH4
Cloacibacterium
succinatutens
rupense
Asteroleplasma
Acinetobacter sp.
Incertae Sedis
Acetitomaculum
Arthrospira
Pantoea gaviniae
Terrisporobacter
Variovorax sp.
Bacillus sp. DHT-
glycolicus
Bifidobacterium
Streptococcus
Lactobacillus sp.
australis
Turicibacter
Pseudomonas sp.
sanguinis
Victivallis
Providencia
Lactococcus sp.
Salmonella
Pseudomonas sp.
Leuconostoc
Propionibacterium
gelidum
Turicibacter
Streptococcus suis
Streptococcus sp.
Enterococcus
Rahnella sp.
faecium
Enterococcus
gallinarum
Lactococcus
Lysobacter
raffinolactis
Bacteroides
Bacillus subtilis
Caulobacter
nordii
Howardella
Salmonella
ureilytica
enterica
Bacteroides sp.
Peptoniphilus
indolicus
Gordonibacter
Lactobacillus
Brevundimonas
pamelaeae
pontis
diminuta
Bacteroides sp.
Corynebacterium
Xanthomonas
vitaeruminis
campestris
Butyricimonas
Helcococcus kunzii
virosa
Anaerotruncus
Desulfovibrio
Acinetobacter
fairfieldensis
baumannii
Robinsoniella
Brachyspira
Moraxella
pilosicoli
nonliquefaciens
Bifidobacterium
Desulfovibrio
Psychrobacter
stercoris
intestinalis
Gordonibacter
Deinococcus
Klebsiella
geothermalis
pneumoniae
Denitrobacterium
Aeromonas
salmonicida
Mannheimia
Alloiococcus
varigena
Dielma
Dysgonomonas
Alloiococcus
gadei
otitis
Alistipes inops
Allobaculum
Nocardia
Alistipes
Allobaculum
Nocardioides
massiliensis
stercoricanis
Alistipes
Cetobacterium
Pseudonocardia
indistinctus
Lactococcus sp.
Cetobacterium
Streptomyces
somerae
Sutterella sp.
Paraeggerthella
Aeromicrobium
hongkongensis
Desulfovibrio sp.
Bacteroides
Anaerostipes sp.
Bacteroides
Denitratisoma
Brochothrix
thetaiotaomicron
Bacteroides
Bacteroides
uniformis
barnesiae
Bacteroides
Bacteroides
Basidiomycota
vulgatus
salanitronis
Roseburia
Sphaerochaeta
Acidovorax
Faecalibacterium
Sphingobacterium
prausnitzii
Acidaminococcus
Pediococcus
Staphylococcus
argentinicus
saprophyticus
Herbaspirillum
Slackia
Microlunatus
equolifaciens
Herbaspirillum
Herbaspirillum sp.
Cutibacterium
seropedicae
granulosum
Vagococcus teuberi
Microbacterium
lacticum
Brachyspira sp.
Exiguobacterium
Parabacteroides sp.
Variovorax
Sarcina
Enterorhabdus
Dietzia
mucosicola
Paraeggerthella
Blastococcus
Streptococcus
Lactobacillus sp.
Blastococcus
aggregatus
Clostridium
Bacteroides
sartorii
Lactococcus sp.
Pseudomonas
citronellolis
Lachnospira
Lactococcus sp.
Malassezia
Lachnospira
Bacteroides sp. D-2
Dermacoccus
pectinoschiza
Dysgonomonas
Brevundimonas
oryzarvi
subvibrioides
Asaccharospora
Lactobacillus faecis
Malassezia
irregularis
restricta
Parabacteroides
chinchillae
Lactobacillus sp.
Sutterella
Lactobacillus sp.
Pseudobutyrivibrio
Bacillus sp. PrMC7
Fusobacterium sp.
Lactobacillus sp.
Bacillus sp. N-16
Methanosphaera
cuniculi
Eggerthella sp. E1
Anaeroglobus sp.
Murdochiella sp.
Facklamia
tabacinasalis
Bacteroides
Oscillibacter sp. 1-3
acidifaciens
Bacteroides sp.
Hymenobacter
Bacteroides sp.
Corynebacterium
Acinetobacter
ursingii
Bacteroides sp.
Dyadobacter
Collinsella
Corynebacterium
felinum
Oscillospira
Parabacteroides sp.
Enterobacter sp.
Massilia
Roseburia
Anaerobacterium
intestinalis
Ruminiclostridium
Aurantimonas
Sphingomonas
aerolata
Shuttleworthia
Dermacoccus sp.
Bacteroides
Dermacoccus sp.
Bacteroides
uniformis
Bacteroides
vulgatus
Roseburia
Faecalibacterium
prausnitzii
Desulfovibrio
Herbaspirillum
Solirubrobacter
Herbaspirillum
Brachybacterium
seropedicae
muris
Faecalibacterium
Alistipes
Kocuria marina
Akkermansia
Sarcina
Jeotgalicoccus
Akkermansia
Staphylococcus
muciniphila
equorum
Anaerotruncus
Streptococcus
Marvinbryantia
Clostridium
Rubellimicrobium
Flavonifractor
plautii
Bacteroides
Lachnospira
Sphingomonas
finegoldii
oligophenolica
Moryella
Lachnospira
pectinoschiza
Adlercreutzia
equolifaciens
Adlercreutzia
Truepera
Asaccharospora
Methylobacterium
irregularis
adhaesivum
Actinomycetospora
Phascolarctobacterium
Acinetobacter sp.
Acidaminococcus
Phascolarctobacterium
faecium
Blautia
Dermacoccus sp.
Roseburia sp.
Dorea
Methylobacterium
formicigenerans
Alistipes sp.
Sutterella
Blautia sp. Ser8
Pseudobutyrivibrio
Blautia faecis
Bacteroides caccae
Acinetobacter
kyonggiensis
Acinetobacter sp.
Eggerthella sp.
Holdemania
Flavonifractor
Holdemania
Brevundimonas
filiformis
Intestinimonas
Pseudomonas sp.
Peptoclostridium
Microbacterium
Asaccharospora
Erysipelatoclostridium
Coriobacteriia
Campylobacter
Novosphingobium
Campylobacter
Bacteroides
Staphylococcus
concisus
acidifaciens
Campylobacter
Blautia luti
Aerocoecus sp.
rectus
Achromobacter
Pseudomonas sp.
Flavobacterium
Bacteroides sp.
Acinetobacter sp.
Pseudomonas
Bacteroides sp.
Acinetobacter sp.
Ralstonia
Collinsella
Micrococcus sp.
pickettii
Oscillospira
Sphingomonas
Rhizobium
Ferruginibacter
Methylobacterium
Roseburia
Amnibacterium
intestinalis
Aureimonas
Acinetobacter
Stenotrophomonas
Moraxella
Shuttleworthia
Blastocatella
fastidiosa
Acinetobacter sp.
Neisseria
Ochrobactrum
Neisseria
Chryseobacterium
elongata
Rhizobium sp.
Ochrobactrum
Brevibacterium
Dorea
Exiguobacterium
Citrobacter
Mycobacterium
Enterobacter
Phenylobacterium
Kluyvera
Planctomyces
Proteus
Pirellula
Proteus mirabilis
Thermus sp.
Serratia
Anaerostipes
Acidiphilium
Haemophilus
Pantoea
influenzae
agglomerans
Haemophilus
Yersinia
parainfluenzae
Bacteroides
Faecalibacterium
Yersinia
fragilis
enterocolitica
Parabacteroides
Alistipes
Sphingobacterium
distasonis
mizutaii
Campylobacter
Akkermansia
Dermacoccus
gracilis
nishinomiyaensis
Campylobacter
Hespellia
Planococcus
ureolyticus
Porphyromonas
Anaerotruncus
Bacillus cereus
Prevotella
Marvinbryantia
Sporosarcina
Fusobacterium
Subdoligranulum
Cellulomonas
Fusobacterium
Flavonifractor
Sphingomonas
nucleatum
plautii
paucimobilis
Fusobacterium
Bacteroides
Alicyclobacillus
periodonticum
finegoldii
Megasphaera
Roseburia
Porphyromonas
inulinivorans
cangingivalis
Weeksella
Blautia wexlerae
Porphyromonas
canoris
Weeksella virosa
Lactonifactor
Kocuria kristinae
Moryella
Pedomicrobium
Rhodopseudomonas
Adlercreutzia
Bibersteinia
equolifaciens
trehalosi
Adlercreutzia
Peptostreptococcus
Janibacter
Peptostreptococcus
Chroococcidiopsis
anaerobius
Kytococcus
Micrococcus
Blautia
Empedobacter
Micrococcus
Roseburia sp.
Rheinheimera
luteus
Staphylococcus
Bacteroides sp.
Macrococcus
Staphylococcus
Alistipes sp. RMA
Luteimonas
aureus
Staphylococcus
Blautia faecis
Pedobacter
epidermidis
Staphylococcus
simulans
Streptococcus
gordonii
Streptococcus
Eggerthella sp.
agalactiae
Streptococcus
Streptococcus sp.
Modestobacter
parasanguinis
Streptococcus
Flavonifractor
Modestobacter
anginosus
multiseptatus
Streptococcus
Sphingomonas
dysgalactiae
aquatilis
Enterococcus
Anaerostipes sp.
Ochrobactrum
tritici
Enterococcus
Fusicatenibacter
Ornithinimicrobium
faecalis
saccharivorans
Lactococcus
Blautia sp. YHC-4
Planomicrobium
lactis
Aerococcus
Intestinimonas
Microvirga
Aerococcus
Fusicatenibacter
urinae
Gemella
Eisenbergiella
Albidovulum
inexpectatum
Atopobium
Eisenbergiella tayi
Ralstonia sp.
Atopobium
Candidatus
Albidovulum
minutum
Soleaferrea
Peptoclostridium
Bacillus
Asaccharospora
Paucibacter
toxinivorans
Lysinibacillus
Erysipelatoclostridium
Chthoniobacter
sphaericus
Lactobacillus
Campylobacter
Conchiformibius
Lactobacillus
Campylobacter
acidophilus
concisus
Lactobacillus
Campylobacter
Sphingomonas
plantarum
rectus
dokdonensis
Lactobacillus
Achromobacter
Staphylococcus
gasseri
Lactobacillus
Flavobacterium
Flavisolibacter
reuteri
Lactobacillus
Pseudomonas
Aureimonas
fermentum
Lactobacillus
Chryseobacterium
vaginalis
haifense
Bradyrhizobium
Nocardioides
mesophilus
Actinomyces
Methylobacterium
Singulisphaera
odontolyticus
Arthrobacter
Acidovorax sp.
Bifidobacterium
Acinetobacter
Mycobacterium
Bifidobacterium
Moraxella
Pseudomonas sp.
bifidum
Bifidobacterium
Nitrososphaera
breve
Bifidobacterium
Neisseria
Brevibacterium
dentium
pityocampae
Brevibacterium
Neisseria mucosa
Sphingomonas
Corynebacterium
Neisseria elongata
Chryseobacterium
Corynebacterium
Neisseria macacae
Variovorax sp.
diphtheriae
Corynebacterium
Propionibacterium
Jeotgalicoccus
aerolatus
Cutibacterium
Enterobacter
Pseudorhodoferax
acnes
Klebsiella
Burkholderia sp.
Mycobacterium
Kluyvera
Blastococcus sp.
Rhodococcus
Pseudomonas sp.
Rhodococcus
Actinobacillus
Chryseobacterium
erythropolis
Haemophilus
Brevibacterium
Haemophilus
Acinetobacter sp.
influenzae
Mobiluncus
Haemophilus
Janibacter sp.
parainfluenzae
Mobiluncus
Bacteroides fragilis
Blastococcus sp.
curtisii
Mobiluncus
Parabacteroides
Mesorhizobium
mulieris
distasonis
Mycoplasma
Campylobacter
Bacillus sp.
gracilis
Mycoplasma
Campylobacter
Shewanella sp.
hominis
ureolyticus
Ureaplasma
Butyrivibrio
Kocuria sp.
Ureaplasma
Porphyromonas
Acinetobacter sp.
urealyticum
Prevotella
Pseudomonas sp.
Fusobacterium
Methanobrevibacter
Fusobacterium
nucleatum
Methanobrevibacter
Fusobacterium
Photobacterium
smithii
periodonticum
Gardnerella
Megasphaera
Dermacoccus sp.
Gardnerella
Stenotrophomonas
vaginalis
Peptococcus
Peptostreptococcus
Deinococcus
antarcticus
Peptococcus
Finegoldia magna
Deinococcus sp.
niger
Halomonas
Peptostreptococcus
Staphylococcus
anaerobius
Globicatella
Variovorax sp.
Globicatella
Staphylococcus
Janibacter sp.
sanguinis
Sphingomonas
Staphylococcus
Rhodobacter
epidermidis
capsulatus
Phyllobacterium
Bacteroides
Streptococcus
Pedomicrobium
eggerthii
gordonii
ferrugineum
Alistipes
Streptococcus
Thermomonas
putredinis
thermophilus
Odoribacter
Streptococcus
Alkanindiges
splanchnicus
agalactiae
Porphyromonas
Streptococcus
Aureimonas
asaccharolytica
parasanguinis
altamirensis
Prevotella bivia
Enterococcus
Rhodococcus sp.
Prevotella
Lactococcus lactis
Pseudomonas
buccalis
syringae
Prevotella
Aerococcus
Acinetobacter
disiens
calcoaceticus
Cronobacter
Gemella
Neisseria
sakazakii
meningitidis
Atopobium
Pectobacterium
carotovorum
Arcanobacterium
Bacillus
Fusobacterium
russii
Arcanobacterium
Lysinibacillus
Thiobacillus
haemolyticum
sphaericus
Clostridioides
Bergeyella
difficile
zoohelcum
Gemella
Erysipelatoclostridium
Nitrosococcus
morbillorum
ramosum
Rhizobium etli
Lactobacillus
Streptococcus
pneumoniae
Veillonella
Lactobacillus
Aerococcus
plantarum
viridans
Veillonella
Lactobacillus
Actinomyces
parvula
gasseri
israelii
Lactobacillus
Mycobacterium
fermentum
chelonae
Lactobacillus
Curtobacterium
salivarius
Lactobacillus
vaginalis
Mollicutes
Eggerthia
Actinomyces
Chelatococcus
catenaformis
Helcococcus
Actinomyces
Brachymonas
odontolyticus
Leptotrichia
Bifidobacterium
Microlunatus
bifidum
phosphovorus
Rothia
Bifidobacterium
Acinetobacter
dentium
haemolyticus
Actinomyces
Corynebacterium
Arthrospira
neuii
Cutibacterium
Corynebacterium
Rubrobacter
avidum
Propionimicrobium
Propionibacterium
Johnsonella
lymphophilum
ignava
Anaerococcus
Cutibacterium
Pseudomonas
hydrogenalis
acnes
agarici
Peptoniphilus
Arthrospira
lacrimalis
fusiformis
Anaerococcus
Mycobacterium
Macrococcus
lactolyticus
caseolyticus
Anaerococcus
Aqubacterium
prevotii
commune
Anaerococcus
Rothia
Staphylococcus
tetradius
dentocariosa
vitulinus
Anaerococcus
Williamsia
vaginalis
Microbacterium
Mobiluncus
Bacillus niacini
Lactobacillus
Moraxella
johnsonii
lincolnii
Dermabacter
Dermabacter
Mycoplasma
Alkalibacterium
hominis
Veillonella
Alishewanella
atypica
Corynebacterium
Tepidimonas
glucuronolyticum
Dialister
Methanobrevibacter
Sneathia
Methanobrevibacter
sanguinegens
smithii
Sutterella
Peptococcus
wadsworthensis
Brevundimonas
Salana
Bifidobacterium
Salana
pseudocatenulatum
multivorans
Phyllobacterium
Pectobacterium
Bacteroides
eggerthii
Corynebacterium
Alistipes putredinis
argentoratense
Brachybacterium
Odoribacter
Xenophilus
splanchnicus
Rothia
Porphyromonas
Georgenia
mucilaginosa
asaccharolytica
Abiotrophia
Prevotella bivia
Pseudomonas
graminis
Granulicatella
Prevotella buccalis
Neisseria sp.
adiacens
Abiotrophia
Prevotella disiens
defectiva
Parabacteroides
Arcanobacterium
merdae
Bacteroides
Prevotella micans
stercoris
Lactobacillus
Gemella
Flavobacterium
rhamnosus
morbillorum
Lactobacillus
Veillonella
Pseudospirillum
crispatus
Veillonella parvula
Macrococcus
brunensis
Pantoea
Thermomonas
brevis
Anaerococcus
Acinetobacter sp.
octavius
Actinotignum
Effusibacillus
schaalii
pohliae
Trueperella
Corynebacterium
bernardiae
caspium
Chryseobacterium
Eggerthia
Kocuria
catenaformis
carniphila
Bergeyella
Candidatus
Protochlamydia
Corynebacterium
Burkholderia
Adhaeribacter
ulcerans
Meiothermus
Actinomyces
Leptotrichia
europaeus
Facklamia
Rothia
Facklamia sp.
Actinomyces neuii
Candidatus
Solibacter
Facklamia sp.
Peptoniphilus
Exiguobacterium
lacrimalis
sibiricum
Mesorhizobium
Parvimonas micra
Epilithonimonas
Anaerococcus
Chryseobacterium
tetradius
Anaerococcus
Cryobacterium
vaginalis
psychrotolerans
Kocuria
Bradyrhizobium
rhizophila
Bilophila
Comamonas sp.
Bilophila
Leucobacter sp.
wadsworthia
Tessaracoccus
Veillonella atypica
Kluyvera
Corynebacterium
georgiana
glucuronolyticum
Collinsella
Dialister
aerofaciens
Campylobacter
Dialister
Sphingobium sp.
hominis
pneumosintes
Actinobaculum
Stenotrophomonas
Burkholderia lata
Halomonas
Sneathia
Deinococcus sp.
pacifica
sanguinegens
Bacillus
Sutterella
Pseudonocardia
Perlucidibaca
Delftia
Microvirga
aerilata
Bacillus
nanhaiisediminis
Rothia
Planococcus sp.
mucilaginosa
Facklamia
Abiotrophia
Frigoribacterium
languida
Slackia
Granulicatella
Porphyrobacter
adiacens
Slackia exigua
Abiotrophia
Microvirga sp.
defectiva
Gemella sp. 933-
Parabacteroides
Comamonas sp.
merdae
Bacteroides
Pseudomonas sp.
stercoris
Lautropia
Brachybacterium
Lactobacillus
Methylobacterium
rhamnosus
Lactobacillus
Actinomycetospora
crispatus
Ralstonia
Arthrobacter sp.
Flavobacterium
Actinobacillus
Massilia oculi
porcinus
Meiothermus
Alishewanella sp.
silvanus
Bosea
Pantoea
Rhodopseudomonas
Achromobacter
Anaerococcus
Ornithinimicrobium
xylosoxidans
octavius
Mogibacterium
Kocuria
Brachymonas sp.
Propionibacterium
Chryseobacterium
Tepidimonas sp.
Aerococcus
Bergeyella
Microvirga sp.
christensenii
Lactobacillus
Corynebacterium
Ornithinimicrobium
fornicalis
ulcerans
Dorea
Kocuria sp. BRI
longicatena
Oligella
Kocuria sp. LW2-
Oligella
Tessaracoccus
Stenotrophomonas
urethralis
Kluyvera georgiana
Staphylococcus
Collinsella
aerofaciens
Aquabacterium
Campylobacter
hominis
Aquabacterium
Luteimonas
mephitis
Delftia
Pseudoglutamici
Leucobacter
bacter albus
aridicollis
Solobacterium
moorei
Veillonella ratti
Slackia
Bacteroides
Lactobacillus
Slackia exigua
Bacteroides
jensenii
thetaiotaomicron
Granulicatella
Gemella sp. 933-
Bacteroides
uniformis
Bacteroides
vulgatus
Bulleidia
Roseburia
Bulleidia
Faecalibacterium
extructa
prausnitzii
Desulfovibrio
Achromobacter
Acidaminococcus
Mogibacterium
Herbaspirillum
Solobacterium
Propionibacterium
Herbaspirillum
seropedicae
Pseudomonas
Aerococcus
brenneri
christensenii
Actinomyces
Lactobacillus
radingae
fornicalis
Actinomyces
Dorea longicatena
turicensis
Olsenella
Sarcina
Aquabacterium
Streptococcus
Aquabacterium sp.
Clostridium
Catenibacterium
Globicatella
Solobacterium
Lachnospira
sulfidifaciens
moorei
pectinoschiza
Granulicatella
Lactobacillus
elegans
jensenii
Lactobacillus
Granulicatella
iners
Anaeroglobus
Anaeroglobus
geminatus
Pseudoglutamici
Phascolarctobacterium
bacter
cumminsii
Megamonas
Phascolarctobacterium
faecium
Corynebacterium
Solobacterium
mastitidis
Peptoniphilus
Olsenella
Sutterella
Sphingobium
Bacteroides
caccae
Novosphingobium
Anaerococcus
Catenibacterium
Holdemania
Sneathia
Aerosphaera
Thalassospira
Aerosphaera taetra
Brevibacterium
Granulicatella
paucivorans
elegans
Finegoldia
Anoxybacillus
Eggerthella
Lactobacillus sp.
Anaeroglobus
geminatus
Actinomyces
Megamonas
hongkongensis
Lactobacillus
Peptoniphilus
Blautia luti
coleohominis
Novosphingobium
Bacilli
Varibaculum
Anaerococcus
Bacteroides sp.
Varibaculum
Sneathia
Oscillospira
cambriense
Corynebacterium
Thalassospira
spheniscorum
Shuttleworthia
Varibaculum
Megasphaera
micronuciformis
Acidaminococcus
intestini
Veillonella
montpellierensis
Megasphaera
micronuciformis
Dialister sp.
Actinotignum
Dialister sp. E2_20
urinale
Propionibacterium
Propionibacterium
Anaerostipes
Streptococcus
pasteurianus
Actinobaculum
Faecalibacterium
massiliense
Propionimicrobium
Akkermansia
Akkermansia
muciniphila
Hespellia
Marvinbryantia
Bacteroides
Subdoligranulum
massiliensis
Flavonifractor
plautii
Bacteroides
finegoldii
Subdoligranulum
Lactonifactor
variabile
longoviformis
Alistipes
Subdoligranulum
Roseburia
finegoldii
variabile
inulinivorans
Peptoniphilus
Bifidobacterium
Blautia wexlerae
longum
Peptoniphilus
Dialister invisus
Lactonifactor
Actinomyces sp.
Peptoniphilus sp.
Curvibacter
Peptoniphilus sp.
gracilis
Bacillus sp. T41
Sutterella
Blautia
stercoricanis
Sutterella
Oribacterium
Roseburia sp.
stercoricanis
Fastidiosipila
Porphyromonas
Bacteroides sp.
uenonis
Fastidiosipila
Odoribacter
Blautia faecis
sanguinis
Cloacibacterium
Bacteroides
normanense
salyersiae
Helcococcus
Roseburia hominis
sueciensis
Pseudoclavibacter
Roseburia faecis
Oribacterium
Dialister
Streptococcus sp.
propionicifaciens
Curvibacter
Bacteroides
plebeius
Porphyromonas
Bacteroides
Fusicatenibacter
uenonis
coprocola
saccharivorans
Odoribacter
Porphyromonas
Blautia sp. YHC-4
somerae
Corynebacterium
Shinella
Intestinimonas
Bacteroides
Alistipes shahii
Fusicatenibacter
salyersiae
Roseburia
Bacteroides
Eisenbergiella
hominis
intestinalis
Roseburia faecis
Peptostreptococcus
Eisenbergiella
stomatis
tayi
Dialister
Bergeyella sp. AF14
Peptoclostridium
propionicifaciens
Dialister
Bacteroides dorei
Erysipelatoclostridium
micraerophilus
Bacteroides
Peptoniphilus sp.
Campylobacter
plebeius
Bacteroides
Bacteroides sp.
Achromobacter
coprocola
Porphyromonas
Parabacteroides
Flavobacterium
somerae
Parabacteroides
Anoxybacillus sp.
Pseudomonas
goldsteinii
Alistipes shahii
Prevotella
Ralstonia
timonensis
pickettii
Bacteroides
Barnesiella
intestinalis
Pelomonas
Lysinibacillus
Bradyrhizobium
Bergeyella sp.
Howardella
Rhizobium
Bacteroides
Anaerococcus
Mesorhizobium
dorei
murdochii
loti
Peptoniphilus
Acinetobacter sp.
Methylobacterium
Peptoniphilus
Streptococcus sp.
Peptoniphilus
Prevotella amnii
Acinetobacter
Bacteroides sp.
Alloscardovia
Moraxella
Moryella
Alloscardovia
indoligenes
omnicolens
Parabacteroides
Veillonella rogosae
Neisseria
Anoxybacillus
Megamonas
Neisseria mucosa
funiformis
Prevotella
Alistipes sp. EBA6-
Neisseria
timonensis
elongata
Barnesiella
Bacteroides sp.
Neisseria
macacae
Lysinibacillus
Paraprevotella
clara
Howardella
Oscillibacter
Ochrobactrum
Citrobacter sp.
Anaerobacillus
alkalidiazotrophicus
Anaerococcus
Alistipes sp.
Citrobacter
murdochii
Cronobacter
Barnesiella
Enterobacter
intestinihominis
Acinetobacter
Parasutterella
Klebsiella
excrementihominis
Streptococcus
Porphyromonas
Kluyvera
bennonis
Methylobacterium
Cloacibacterium
Prevotella amnii
Actinobacillus
Alloscardovia
Haemophilus
parainfluenzae
Alloscardovia
Parvimonas
Bacteroides
omnicolens
fragilis
Rhizobium sp.
Campylobacter
gracilis
Veillonella
Corynebacterium
Campylobacter
rogosae
freiburgense
ureolyticus
Jonquetella
Delftia lacustris
Butyrivibrio
Jonquetella
Novosphingobium
Porphyromonas
anthropi
sediminicola
Megamonas
Butyricimonas
Prevotella
funiformis
Bacillus sp. CZb
Parasutterella
Fusobacterium
Alistipes sp.
Enterorhabdus
Fusobacterium
nucleatum
Bacteroides sp.
Phyllobacterium
Fusobacterium
periodonticum
Paraprevotella
Negativicoccus
Desulfovibrio
clara
succinicivorans
piger
Serratia
Bacteroides clarus
Megasphaera
nematodiphila
Oscillibacter
Sutterella sp. YIT
Pantoea vagans
Bifidobacterium
Rhodopseudomonas
kashiwanohense
Anaerobacillus
Porphyromonas sp.
alkalidiazotrophicus
Rhodopseudomonas
Lautropia sp. TeTO
Peptostreptococcus
boonkerdii
Chryseobacterium
Pyramidobacter
Finegoldia magna
Alistipes sp.
Anaerostipes
Peptostreptococcus
hadrus
anaerobius
Brevibacterium
ravenspurgense
Dialister
Micrococcus
succinatiphilus
Barnesiella
Micrococcus
intestinihominis
luteus
Pseudomonas
Anaerosporobacter
Staphylococcus
Porphyromonas
Lactobacillus sp.
Staphylococcus
bennonis
aureus
Cloacibacterium
Campylobacter sp.
Staphylococcus
epidermidis
Gemella
Lactobacillus sp.
Staphylococcus
asaccharolytica
simulans
Bosea sp.
Veillonella sp. oral
Deinococcus-
Thermus
Peptoniphilus
Bilophila sp.
Streptococcus
duerdenii
thermophilus
Peptoniphilus
Anaerobacillus
Streptococcus
koenoeneniae
parasanguinis
Murdochiella
Corynebacterium
Streptococcus
asaccharolytica
dysgalactiae
Actinomyces sp.
Enterococcus
Cloacibacillus
Peptococcus sp.
Aerococcus
Cloacibacillus
Streptococcus sp.
Gemella
evryensis
Stomatobaculum
Atopobium
longum
Parvimonas
Blautia stercoris
Peptoniphilus sp.
Bacillus
Corynebacterium
Peptoniphilus sp.
Lysinibacillus
freiburgense
sphaericus
Delftia lacustris
Ralstonia sp.
Clostridioides
difficile
Novosphingobium
Stenotrophomonas
Lactobacillus
sediminicola
Butyricimonas
Alistipes sp. HGB5
Lactobacillus
plantarum
Bifidobacterium
Negativicoccus
Lactobacillus
salivarius
Brevibacterium
Shinella sp. DR33
massiliense
Paraprevotella
Bacteroides sp.
Actinomyces
Parasutterella
Lactobacillus sp.
Actinomyces
odontolyticus
Enterorhabdus
Veillonella sp.
Arthrobacter
Negativicoccus
Actinomyces sp.
Bifidobacterium
succinicivorans
Mycobacterium
Bifidobacterium
Brevibacterium
Bacteroides
Campylobacter sp.
Corynebacterium
clarus
Bifidobacterium
Fusobacterium sp.
Corynebacterium
kashiwanohense
diphtheriae
Porphyromonas
Fusobacterium sp.
Propionibacterium
Porphyromonas
Fusobacterium sp.
Cutibacterium
acnes
Pyramidobacter
Veillonella sp.
Pseudoclavibacter
Veillonella sp.
Mycobacterium
Anaerostipes
Anaerococcus sp.
Rhodococcus
hadrus
Anaerococcus sp.
Rhodococcus
erythropolis
Anaerococcus sp.
Anaerococcus
Rothia
provencensis
dentocariosa
Anaerosporobacter
Bradyrhizobium
Lactobacillus sp.
Delftia sp. BN-
Mobiluncus
Ochrobactrum
Methylobacterium
Mobiluncus
curtisii
Anaerostipes sp.
Staphylococcus sp.
Mobiluncus
mulieris
Campylobacter
Enterobacter sp.
Lactobacillus sp.
Megasphaera sp.
Veillonella sp.
Sphingomonas sp.
Mycoplasma
Microbacterium
Coprobacter
yannicii
fastidiosus
Corynebacterium
Actinomyces sp.
canis
Tessaracoccus
Methanobrevibacter
Peptoniphilus
Faecalibacterium
Methanobrevibacter
smithii
Corynebacterium
Murdochiella
Gardnerella
Anaerobacillus
Lachnoanaerobaculum
Gardnerella
vaginalis
Corynebacterium
Streptococcus sp.
Peptococcus
Peptoniphilus
Varibaculum sp.
Halomonas
Brevundimonas
Stomatobaculum
Lactobacillus sp.
Prevotella sp. S4-
Sphingomonas
Peptoniphilus
Solobacterium sp.
Phyllobacterium
coxii
Stomatobaculum
Streptococcus sp.
Bacteroides
longum
eggerthii
Herbaspirillum
Veillonella sp.
Odoribacter
huttiense
splanchnicus
Bacteroides
Eggerthia
Porphyromonas
stercorirosoris
asaccharolytica
Peptoniphilus
Alloprevotella
Prevotella bivia
Peptoniphilus
Anaerococcus sp.
Prevotella
buccalis
Streptococcus
Porphyromonas sp.
Stenotrophomonas
Slackia sp. S8 F4
Alistipes sp.
Finegoldia sp. S9
Gemella
morbillorum
Negativicoccus
Murdochiella sp.
Rhizobium etli
Bacteroides sp.
Peptoniphilus sp.
Veillonella
Lactobacillus sp.
Coprobacter
Veillonella
parvula
Acinetobacter
Staphylococcus sp.
Stenotrophomonas
Senegalimassilia
Acinetobacter
Romboutsia
Veillonella sp.
Terrisporobacter
Actinomyces sp.
Intestinibacter
Bifidobacterium
Burkholderia
Campylobacter
Fusobacterium
Leptotrichia
Fusobacterium
Cutibacterium
avidum
Fusobacterium
Anaerococcus
hydrogenalis
Veillonella sp.
Peptoniphilus
lacrimalis
Veillonella sp.
Cutibacterium
Anaerococcus
lactolyticus
Anaerococcus
Parvimonas
micra
Anaerococcus
Anaerococcus
provencensis
prevotii
Bradyrhizobium
Deinococcus
Anaerococcus
tetradius
Sphingomonas
Lactococcus
Anaerococcus
vaginalis
Enterococcus sp.
Johnsonella
Microbacterium
Bosea sp. R-
Lactobacillus sp.
Bilophila
Delftia sp. BN-
Dermabacter
hominis
Moraxella sp. 26
Phascolarctobacterium
Veillonella
succinatutens
atypica
Staphylococcus
Campylobacter
Corynebacterium
showae
glucuronolyticum
Methylobacterium
Comamonas
Stenotrophomonas
Enterococcus sp.
Neisseria
Brevundimonas
flavescens
Staphylococcus
Neisseria sicca
Brachybacterium
Neisseria canis
Megasphaera sp.
Bergeriella
denitrificans
Megasphaera sp.
Kingella oralis
Corynebacterium
argentoratense
Sphingomonas
Eikenella
Brachybacterium
Corynebacterium
Eikenella
Abiotrophia
epidermidicanis
corrodens
Trueperella
Aggregatibacter
Granulicatella
aphrophilus
adiacens
Mesorhizobium
Aggregatibacter
Abiotrophia
segnis
defectiva
Coprobacter
Pasteurella
Parabacteroides
fastidiosus
merdae
Actinomyces sp.
Rodentibacter
Bacteroides
pneumotropicus
stercoris
Jonquetella sp.
Porphyromonas
Lautropia
gingivalis
Prevotella sp.
Lactobacillus
crispatus
Peptoniphilus
Desulfobulbus
Ralstonia
Megasphaera sp.
Selenomonas
Sphingobium sp.
Capnocytophaga
Actinobacillus
porcinus
Anaerococcus
Capnocytophaga
Pantoea
gingivalis
Capnocytophaga
Anaerococcus
sputigena
octavius
Faecalibacterium
Kocuria
Murdochiella
Streptococcus
Chryseobacterium
mutans
Lachnoanaerobaculum
Streptococcus
Bergeyella
intermedius
Streptococcus
Atopobium
Corynebacterium
parvulum
ulcerans
Varibaculum sp.
Atopobium rimae
Facklamia
Varibaculum sp.
Lactobacillus
Mesorhizobium
paracasei
Propionibacterium
Actinomyces
viscosus
Stomatobaculum
Bifidobacterium
Kocuria
adolescentis
rhizophila
Actinomyces sp.
Pseudopropionibacterium
propionicum
Atopobium sp.
Anaeroplasma
Atopobium sp.
Asteroleplasma
Tessaracoccus
Atopobium sp.
Methanosphaera
Kluyvera
georgiana
Atopobium sp.
Methanosphaera
Collinsella
stadtmanae
aerofaciens
Atopobium sp.
Cardiobacterium
Atopobium sp.
Vagococcus
Halomonas
pacifica
Dialister sp. S4-
Streptococcus
Bacillus
mitis
pseudofirmus
Finegoldia sp.
Tannerella
forsythia
Gardnerella sp.
Porphyromonas
Delftia
endodontalis
Peptoniphilus
Prevotella
intermedia
Prevotella sp.
Prevotella oralis
Solobacterium
Prevotella oris
Facklamia
languida
Peptoniphilus
Prevotella oulorum
Gemella sp. 933-
Finegoldia sp.
Acetitomaculum
Negativicoccus
Kingella
Corynebacterium
Terrisporobacter
frankenforstense
glycolicus
Megasphaera
Veillonella dispar
massiliensis
Corynebacterium
Leptotrichia
buccalis
Streptococcus
Porphyromonas
catoniae
Veillonella sp.
Corynebacterium
matruchotii
Eggerthia
Catonella
Bosea
Alloprevotella
Catonella morbi
Achromobacter
xylosoxidans
Dialister sp.
Filifactor
Mogibacterium
Intestinimonas
Capnocytophaga
Propionibacterium
butyriciproducens
granulosa
Lactobacillus sp.
Capnocytophaga
Lactobacillus
haemolytica
fornicalis
Bradyrhizobium
Actinomyces
georgiae
Anaerococcus
Actinomyces
meyeri
Finegoldia sp. S8
Actinomyces
Aquabacterium
graevenitzii
Porphyromonas
Aquabacterium
Slackia sp. S8 F4
Prevotella pallens
Actinomyces sp.
Corynebacterium
Solobacterium
durum
moorei
Anaerococcus
Streptococcus
Lactobacillus
peroris
jensenii
Anaerococcus
Mannheimia
Granulicatella
Finegoldia sp. S9
Alloprevotella
tannerae
Olsenella sp. S9
Centipeda
Peptococcus sp.
Centipeda
periodontii
Peptococcus sp.
Cryptobacterium
Peptoniphilus
Cryptobacterium
curtum
Coprobacter
Rothia sp. CCUG
Solobacterium
Corynebacterium
Mannheimia
Pseudomonas
granulomatis
brenneri
Atopobium
Mogibacterium
Actinomyces
deltae
pumilum
radingae
Parvibacter
Mycoplasma
falconis
Ralstonia sp.
Mycoplasma
subdolum
Helcococcus
Pseudoflavonifractor
seattlensis
capillosus
Staphylococcus
Leptotrichia
trevisanii
Senegalimassilia
Peptoniphilus
Aerosphaera
Romboutsia
Filifactor alocis
Aerosphaera
taetra
Veillonella
Turicibacter
Granulicatella
seminalis
sanguinis
elegans
Terrisporobacter
Leptotrichia wadei
Lactobacillus
iners
Intestinibacter
Leptotrichia
Finegoldia
hofstadii
Leptotrichia shahii
Anoxybacillus
Actinotignum
Leptotrichia
Megamonas
goodfellowii
Actinomyces sp.
Corynebacterium
mastitidis
Anaerotruncus
Peptoniphilus
colihominis
Cutibacterium
Rothia aeria
Gallicola
Victivallis
Sphingobium
Novosphingobium
Deinococcus
Anaerococcus
Johnsonella
Turicibacter
Thalassospira
Alysiella
Cardiobacterium
valvarum
Tannerella
Lactobacillus sp.
Bacteroides sp.
Scardovia
Lactobacillus
Varibaculum
taiwanensis
Varibaculum
cambriense
Bacteroides
Corynebacterium
spheniscorum
Bacteroides
thetaiotaomicron
Bacteroides
vulgatus
Roseburia
Leptotrichia
Faecalibacterium
Megasphaera
prausnitzii
Desulfovibrio
Scardovia wiggsiae
Desulfovibrio sp.
Selenomonas
Propionibacterium
Acidaminococcus
Herbaspirillum
Propionimicrobium
Herbaspirillum
Neisseria
seropedicae
bacilliformis
Actinomyces
dentalis
Bacteroides nordii
Sarcina
Capnocytophaga
Clostridium
Capnocytophaga
Bacteroides
massiliensis
Capnocytophaga
Lachnospira
Capnocytophaga
Lachnospira
Bergeriella
Subdoligranulum
pectinoschiza
variabile
Streptococcus
Alistipes
dentirousetti
finegoldii
Parabacteroides
Bifidobacterium
johnsonii
longum
Howardella
Dialister invisus
ureilytica
Peptoniphilus sp.
Phascolarctobacterium
Peptoniphilus sp.
Phascolarctobacterium
Aggregatibacter
Curvibacter
faecium
gracilis
Prevotella
Bacillus sp. T41
nanceiensis
Dorea
Prevotella
Sutterella
formicigenerans
maculosa
stercoricanis
Pseudobutyrivibrio
Veillonella sp.
Bacteroides sp.
Actinomyces
Pseudoclavibacter
massiliensis
Holdemania
Lachnoanaerobaculum
Oribacterium
saburreum
Holdemania
Bacteroides sp.
Curvibacter
filiformis
Bacteroides sp.
Porphyromonas
uenonis
Gordonibacter
Odoribacter
pamelaeae
Atopobium sp.
Dialister
propionicifaciens
Olsenella sp.
Dialister
micraerophilus
Blautia glucerasea
Bacteroides
plebeius
Eggerthella
Bacteroides sp.
Bacteroides
coprocola
Actinomyces oris
Alistipes shahii
Butyricimonas
Peptoniphilus sp.
virosa
Blautia luti
Leptotrichia
Peptoniphilus sp.
hongkongensis
Bacteroides sp.
Bacteroides sp.
Collinsella
Robinsoniella
Moryella
indoligenes
Oscillospira
Propionibacterium
Anoxybacillus sp.
Prevotella
Prevotella
aurantiaca
timonensis
Neisseria
Barnesiella
shayeganii
Lachnoanaerobaculum
Lysinibacillus
umeaense
Odoribacter laneus
Citrobacter sp.
Gordonibacter
Pseudomonas sp.
Fretibacterium
Anaerococcus
fastidiosum
murdochii
Oribacterium sp.
Acinetobacter sp.
Dorea
Prevotella sp. oral
Streptococcus sp.
Leptotrichia sp.
Methylobacterium
Oribacterium sp.
Megamonas
funiformis
Alloprevotella rava
Alistipes sp.
Parvimonas sp.
Bacteroides sp.
Prevotella sp. WAL
Paraprevotella
clara
Neisseria
Serratia
skkuensis
nematodiphila
Capnocytophaga
Oscillibacter
Anaerostipes
Actinomyces
Anaerobacillus
alkalidiazotrophicus
Capnocytophaga
Rhodopseudomonas
boonkerdii
Actinomyces
Alistipes sp.
Faecalibacterium
Actinomyces sp.
Dialister
succinatiphilus
Capnocytophaga
Barnesiella
intestinihominis
Alistipes
Capnocytophaga
Parasutterella
excrementihominis
Akkermansia
Desulfobulbus sp.
Pseudomonas
Akkermansia
Leptotrichia sp.
Porphyromonas
muciniphila
bennonis
Hespellia
Oribacterium sp.
Cloacibacterium
Anaerotruncus
Shuttleworthia sp.
Bosea sp.
Marvinbryantia
Streptococcus sp.
Subdoligranulum
Tannerella sp. oral
Flavonifractor
Parvimonas sp.
Parvimonas
plautii
Lactonifactor
longoviformis
Lactonifactor
Leptotrichia sp.
Corynebacterium
freiburgense
Moryella
Leptotrichia sp.
Delftia lacustris
Adlercreutzia
Methylobacterium
Novosphingobium
equolifaciens
longum
sediminicola
Adlercreutzia
Capnocytophaga
Paraprevotella
Mogibacterium sp.
Parasutterella
Mogibacterium sp.
Enterorhabdus
Selenomonas sp.
Phyllobacterium sp.
Actinomyces sp.
Negativicoccus
succinicivorans
Acidaminococcus
Actinomyces sp.
Bacteroides
clarus
Blautia
Atopobium sp.
Porphyromonas
Roseburia sp.
Fusobacterium sp.
Lautropia sp.
Bacteroides sp.
Oribacterium sp.
Pseudoclavibacter
Blautia sp. Ser8
Oribacterium sp.
Anaerostipes
Blautia faecis
Lachnoanaerobaculum
Lachnoanaerobaculum
Vagococcus sp.
Lachnoanaerobaculum
Klebsiella sp. B12
orale
Eggerthella sp.
Actinomyces sp.
Anaerosporobacter
Flavonifractor
Pseudomonas sp.
Lactobacillus sp.
Pseudoflavonifractor
Ochrobactrum
Anaerostipes sp.
Fusobacterium sp.
Lactobacillus sp.
Fusicatenibacter
Dielma fastidiosa
Veillonella sp.
saccharivorans
Blautia sp. YHC-4
Veillonella sp. JL-2
Microbacterium
yannicii
Intestinimonas
Neisseria oralis
Corynebacterium
canis
Fusicatenibacter
Veillonella
Bilophila sp.
tobetsuensis
Eisenbergiella
Actinomyces sp.
Anaerobacillus
Eisenbergiella
Neisseria sp.
Corynebacterium
tayi
Candidatus
Phascolarctobacterium
Actinomyces sp.
Soleaferrea
Campylobacter
Streptococcus sp.
Peptococcus sp.
concisus
Campylobacter
Veillonella sp.
Peptoniphilus sp.
rectus
Achromobacter
Veillonella sp.
Streptococcus sp.
Flavobacterium
Rothia sp. THG-N7
Brevundimonas
Capnocytophaga
Lactobacillus sp.
Rhizobium
Actinomyces sp.
Stomatobaculum
longum
Mesorhizobium
Herbaspirillum
loti
huttiense
Candidatus
Blautia stercoris
Saccharimonas
Acinetobacter
Bacteroides sp.
Peptoniphilus sp.
Moraxella
Tessaracoccus
Peptoniphilus sp.
lapidicaptus
Prevotella sp.
Ralstonia sp.
Neisseria
Fretibacterium
Stenotrophomonas
Neisseria
Butyricimonas
Negativicoccus
mucosa
faecihominis
Butyricimonas
Shinella sp. DR33
paravirosa
Ochrobactrum
Aeromonas
Acinetobacter sp.
Roseburia cecicola
Stenotrophomonas
Citrobacter
Fusobacterium
Veillonella sp.
ulcerans
Enterobacter
Rhodobacter
Actinomyces sp.
Klebsiella
Veillonella sp.
Kluyvera
Leuconostoc
Veillonella sp.
Proteus mirabilis
Blautia hansenii
Anaerococcus sp.
Serratia
Streptococcus
Anaerococcus sp.
equinus
Haemophilus
Anaerococcus
influenzae
provencensis
Bacteroides
Acholeplasma
Sphingomonas
fragilis
Parabacteroides
Bifidobacterium
Enterococcus sp.
distasonis
animalis
Campylobacter
Bacteroides ovatus
Bosea sp. R-
gracilis
Campylobacter
Delftia sp. BN-
ureolyticus
Porphyromonas
Moraxella sp. 26
Prevotella
Staphylococcus
Fusobacterium
Methylobacterium
Fusobacterium
Enterococcus sp.
mortiferum
Fusobacterium
Staphylococcus
nucleatum
Fusobacterium
Anaerosporobacter
Brachybacterium
periodonticum
mobilis
Megasphaera
Alistipes
Sphingomonas
massiliensis
Veillonella sp. ADV
Corynebacterium
epidermidicanis
Peptostreptococcus
Bacteroides
Mesorhizobium
coprophilus
Paraprevotella
Murdochiella
xylaniphila
Staphylococcus
Alistipes
Lachnoanaerobaculum
aureus
indistinctus
Streptococcus
Aeromonas sp. B11
Streptococcus sp.
gordonii
Streptococcus
Pseudomonas sp.
Varibaculum sp.
parasanguinis
Streptococcus
Lactococcus sp.
Varibaculum sp.
anginosus
Streptococcus
Leuconostoc sp.
Propionibacterium
dysgalactiae
Enterococcus
Butyricimonas sp.
Stomatobaculum
faecalis
Aerococcus
Sutterella sp. 252
Actinomyces sp.
Aerococcus
Atopobium sp.
urinae
Atopobium
Staphylococcus
Negativicoccus
saprophyticus
Atopobium
Cutibacterium
Corynebacterium
minutum
granulosum
Bacillus
Streptococcus sp.
Lysinibacillus
Actinomyces
Veillonella sp.
sphaericus
Erysipelatoclostridium
Alloprevotella
ramosum
Lactobacillus
Dialister sp.
acidophilus
Lactobacillus
Simonsiella
Bradyrhizobium
plantarum
Lactobacillus
Simonsiella
Anaerococcus sp.
gasseri
muelleri
Lactobacillus
Streptobacillus
Finegoldia sp. S8
fermentum
Lactobacillus
Kocuria kristinae
Finegoldia sp. S9
salivarius
Lactobacillus
Janibacter
Murdochiella sp.
vaginalis
Planomicrobium
Peptococcus sp.
Actinomyces
Neisseria
Peptoniphilus sp.
odontolyticus
wadsworthii
Arthrobacter
Burkholderia sp.
Corynebacterium
Arthrobacter sp.
Necropsobacter
Ralstonia sp. A52
Bifidobacterium
Necropsobacter
Staphylococcus
bifidum
rosorum
Bifidobacterium
Janibacter sp. M3-5
Terrisporobacter
breve
Bifidobacterium
Pseudomonas
Intestinibacter
dentium
syringae
Brevibacterium
Neisseria
meningitidis
Corynebacterium
Neisseria flava
Corynebacterium
Arthrospira
diphtheriae
Corynebacterium
Johnsonella ignava
Cutibacterium
Arthrospira
Cutibacterium
acnes
fusiformis
Neisseria sp. CCUG
Rhodococcus
Prevotella micans
Deinococcus
Campylobacter
Lactococcus
hyointestinalis
Rothia
Neisseria cinerea
Johnsonella
dentocariosa
Mobiluncus
Neisseria
polysaccharea
Mobiluncus
Aggregatibacter
curtisii
actinomycetemcomitans
Mobiluncus
Selenomonas
mulieris
sputigena
Trichococcus
Bacteroides sp.
Porphyromonas
Comamonas
gulae
Streptococcus
Neisseria sicca
australis
Olsenella uli
Kingella oralis
Methanobrevibacter
Capnocytophaga
Aggregatibacter
aphrophilus
Methanobrevibacter
Gemella sp. oral
Aggregatibacter
smithii
segnis
Gardnerella
Actinomyces sp.
Pasteurella
Globicatella
Actinomyces
Capnocytophaga
cardiffensis
Globicatella
Prevotella sp.
Capnocytophaga
sanguinis
gingivalis
Sphingomonas
Eikenella sp.
Capnocytophaga
ochracea
Bifidobacterium
Veillonella
pseudocatenulatum
rodentium
Phyllobacterium
Capnocytophaga
Streptococcus
mutans
Bacteroides
Capnocytophaga
Vagococcus
eggerthii
leadbetteri
Alistipes
Mycoplasma sp.
Tannerella
putredinis
forsythia
Porphyromonas
Propionibacterium
Porphyromonas
asaccharolytica
acidifaciens
endodontalis
Prevotella
Fusobacterium sp.
Prevotella
buccalis
intermedia
Campylobacter sp.
Prevotella
nigrescens
Arcanobacterium
Stenotrophomonas
Prevotella oris
haemolyticum
Snodgrassella
Prevotella
oulorum
Veillonella
Rothia sp. THG-T3
Dolosigranulum
parvula
Actinomyces sp.
Dolosigranulum
pigrum
Helcococcus
Acetitomaculum
Bacteroides
Kingella
Bacteroides
Terrisporobacter
thetaiotaomicron
glycolicus
Leptotrichia
Bacteroides
Leptotrichia
uniformis
buccalis
Rothia
Bacteroides
Porphyromonas
vulgatus
catoniae
Cutibacterium
Roseburia
Catonella
avidum
Anaerococcus
Faecalibacterium
Catonella morbi
hydrogenalis
prausnitzii
Peptoniphilus
Desulfovibrio
Filifactor
lacrimalis
Anaerococcus
Desulfovibrio sp.
Capnocytophaga
lactolyticus
granulosa
Parvimonas
Herbaspirillum
Actinomyces
micra
seropedicae
georgiae
Anaerococcus
Actinomyces
prevotii
gerencseriae
Anaerococcus
Actinomyces
tetradius
graevenitzii
Lactobacillus
johnsonii
Bilophila
Prevotella pallens
Bilophila
Corynebacterium
wadsworthia
durum
Dermabacter
Streptococcus
Centipeda
Dermabacter
Clostridium
Centipeda
hominis
periodontii
Corynebacterium
Rothia sp. CCUG
glucuronolyticum
Dialister
Lachnospira
Mogibacterium
pneumosintes
pumilum
Stenotrophomonas
Lachnospira
Pseudoflavonifractor
pectinoschiza
capillosus
Sneathia
Leptotrichia
sanguinegens
trevisanii
Sutterella
wadsworthensis
Leptotrichia
wadei
Leptotrichia
shahii
Phascolarctobacterium
Leptotrichia
goodfellowii
Corynebacterium
Phascolarctobacterium
Tannerella
argentoratense
faecium
Rothia
mucilaginosa
Butyrivibrio
Dorea
crossotus
formicigenerans
Abiotrophia
Sutterella
Granulicatella
Pseudobutyrivibrio
adiacens
Abiotrophia
Bacteroides caccae
Megasphaera
defectiva
Parabacteroides
Selenomonas
merdae
Bacteroides
Actinomyces
stercoris
dentalis
Lautropia
Bacteroides
nordii
Lactobacillus
Capnocytophaga
rhamnosus
Eggerthella
Aggregatibacter
Pantoea
Prevotella
nanceiensis
Actinotignum
Prevotella
schaalii
maculosa
Trueperella
Bacteroides
Actinomyces
bernardiae
acidifaciens
massiliensis
Corynebacterium
Blautia luti
Lachnoanaerobaculum
ulcerans
saburreum
Facklamia sp.
Bacteroides sp.
Bacteroides sp.
Facklamia sp.
Bacteroides sp.
Gordonibacter
pamelaeae
Mesorhizobium
Collinsella
Bacteroides sp.
Butyricimonas
virosa
Hydrogenophilus
Roseburia
Leptotrichia
intestinalis
hongkongensis
Tessaracoccus
Robinsoniella
Kluyvera
Shuttleworthia
Rhizobium sp.
georgiana
Collinsella
Oribacterium sp.
aerofaciens
Campylobacter
Leptotrichia sp.
hominis
Bifidobacterium
Alloprevotellarava
gallicum
Dorea
Capnocytophaga
Tannerella sp.
Atopobium
Leptotrichia sp.
vaginae
Facklamia
Actinomyces sp.
languida
Slackia
Oribacterium sp.
Gemella sp. 933-
Anaerostipes
Lachnoanaerobaculum
Moraxella sp.
Lysinibacillus sp.
Faecalibacterium
Pseudoflavonifractor
Alistipes
Neisseria oralis
Akkermansia
Actinomyces sp.
muciniphila
Hespellia
Rothia sp. THG-
Bosea
Marvinbryantia
Candidatus
Saccharimonas
Achromobacter
Roseburia
Bacteroides sp.
xylosoxidans
inulinivorans
Mogibacterium
Blautia wexlerae
Tessaracoccus
lapidicaptus
Aerococcus
Moryella
Fretibacterium
christensenii
Eremococcus
Candidatus
coleocola
Stoquefichus
Lactobacillus
Pseudomonas
fornicalis
aeruginosa
Dorea
Enterobacter
longicatena
cloacae
Oligella
Blautia
Oligella
Roseburia sp.
Aeromonas
urethralis
Bacteroides sp.
Rhodobacter
Blautia faecis
Veillonella ratti
Leuconostoc
Lactobacillus
Leuconostoc
jensenii
lactis
Granulicatella
Lactobacillus
delbrueckii
Streptococcus sp.
Bifidobacterium
animalis
Flavonifractor
Lactobacillus
curvatus
Bacteroides
ovatus
Anaerostipes sp.
Rahnella
Fusicatenibacter
Weissella
saccharivorans
Solobacterium
Blautia sp. YHC-4
Actinomyces
Fusicatenibacter
Pseudomonas
radingae
monteilii
Actinomyces
Eisenbergiella
turicensis
Eisenbergiella tayi
Catenibacterium
Peptoclostridium
Papillibacter
Globicatella
Erysipelatoclostridium
Cupriavidus
sulfidifaciens
Aerosphaera
Campylobacter
Aerosphaera
Campylobacter
taetra
concisus
Granulicatella
Achromobacter
Azospira
elegans
Lactobacillus
Flavobacterium
Collinsella
iners
intestinalis
Finegoldia
Pseudomonas
Leuconostoc
inhae
Anaeroglobus
Ralstonia pickettii
geminatus
Megamonas
Corynebacterium
atypicum
Corynebacterium
Rhizobium
mastitidis
Peptoniphilus
Mesorhizobium
loti
Sphingobium
Methylobacterium
Corynebacterium
ciconiae
Sneathia
Pseudoclavibacter
bifida
Thalassospira
Acinetobacter
Brevibacterium
Moraxella
Streptococcus sp.
paucivorans
Eremococcus
Cloacibacterium
rupense
Neisseria
Aeromonas sp.
Lactobacillus sp.
Neisseria mucosa
Acinetobacter sp.
Neisseria elongata
Paucibacter sp.
Varibaculum
Neisseria macacae
Pseudomonas sp.
cambriense
Corynebacterium
Lactococcus sp.
spheniscorum
Pseudomonas sp.
Citrobacter
Finegoldia sp.
Klebsiella
Comamonas
jiangduensis
Megasphaera
Kluyvera
Propionibacterium
micronuciformis
Veillonella
Proteus
Sutterella sp. 252
montpellierensis
Dialister sp.
Actinobacillus
Rahnella sp.
Propionibacterium
Haemophilus
influenzae
Streptococcus
Haemophilus
Shewanella
pasteurianus
parainfluenzae
Bacteroides fragilis
Actinobaculum
Parabacteroides
Lysobacter
massiliense
distasonis
Propionimicrobium
Campylobacter
Caulobacter
gracilis
Porphyromonas
Caulobacter sp.
Prevotella
Fusobacterium
Fusobacterium
Elizabethkingia
nucleatum
meningoseptica
Bacteroides
Fusobacterium
Brevundimonas
massiliensis
periodonticum
diminuta
Megasphaera
Xanthomonas
campestris
Subdoligranulum
Rhodopseudomonas
Acinetobacter
variabile
baumannii
Bifidobacterium
Moraxella
longum
nonliquefaciens
Dialister invisus
Peptostreptococcus
Psychrobacter
Peptoniphilus
Finegoldia magna
Zymomonas
Peptoniphilus
Peptostreptococcus
Klebsiella
anaerobius
pneumoniae
Actinomyces sp.
Aeromonas
salmonicida
Sutterella
Micrococcus
Photobacterium
stercoricanis
Fastidiosipila
Micrococcus luteus
Geobacillus
stearothermophilus
Helcococcus
Staphylococcus
Alloiococcus
sueciensis
otitis
Bacteroides sp.
Staphylococcus
Nocardioides
aureus
Pseudoclavibacter
Staphylococcus
Pseudonocardia
epidermidis
Oribacterium
Staphylococcus
Streptomyces
simulans
Porphyromonas
Gordonia
uenonis
Roseburia
Streptococcus
Gordonia terrae
hominis
gordonii
Dialister
Streptococcus
Brochothrix
propionicifaciens
thermophilus
Bacteroides
Streptococcus
plebeius
parasanguinis
Bacteroides
Streptococcus
coprocola
dysgalactiae
Shinella
Enterococcus
Acidovorax
Alistipes shahii
Lactococcus lactis
Sphingobium
yanoikuyae
Pelomonas
Aerococcus
Peptostreptococcus
Aerococcus urinae
Sphingobacterium
stomatis
Peptoniphilus
Atopobium
Turicella otitidis
Bacteroides sp.
Atopobium
Rhodoplanes
minutum
Parabacteroides
Cutibacterium
granulosum
Barnesiella
Bacillus
Microbacterium
lacticum
Lysinibacillus
Lysinibacillus
Exiguobacterium
sphaericus
Howardella
Clostridioides
Dietzia
difficile
Citrobacter sp.
Lactobacillus
Acinetobacter
radioresistens
Streptococcus
Lactobacillus
Paenibacillus
plantarum
Methylobacterium
Lactobacillus
Pseudomonas
gasseri
citronellolis
Prevotella amnii
Lactobacillus
Malassezia
salivarius
Alloscardovia
Lactobacillus
Nesterenkonia
vaginalis
Alloscardovia
Actinomyces
Dermacoccus
omnicolens
Jonquetella
Arthrobacter
Pseudoxanthomonas
japonensis
Jonquetella
Bifidobacterium
Brevundimonas
anthropi
subvibrioides
Pelomonas
Brevibacterium
Malassezia
aquatica
restricta
Megamonas
Corynebacterium
Pseudoxanthomonas
funiformis
diphtheriae
Alistipes sp.
Corynebacterium
Bacteroides sp.
Propionibacterium
Paraprevotella
Cutibacterium
clara
acnes
Serratia
nematodiphila
Anaerobacillus
Mycobacterium
alkalidiazotrophicus
Alistipes sp.
Rhodococcus
Brevibacterium
Rhodococcus
ravenspurgense
erythropolis
Dialister
succinatiphilus
Barnesiella
Rothia
intestinihominis
dentocariosa
Parasutterella
excrementihominis
Porphyromonas
Mobiluncus
bennonis
Cloacibacterium
Mobiluncus
mulieris
Gemella
Marmoricola
asaccharolytica
Peptoniphilus
Marmoricola
duerdenii
aurantiacus
Peptoniphilus
Methanobrevibacter
koenoeneniae
Murdochiella
Methanobrevibacter
Hymenobacter
asaccharolytica
smithii
Gardnerella
Xanthomonas
gardneri
Cloacibacillus
Gardnerella
Frigoribacterium
vaginalis
Cloacibacillus
Peptococcus
Acinetobacter
evryensis
ursingii
Atopobium sp.
Peptococcus niger
Roseomonas
Halomonas
Geobacillus
Corynebacterium
capitovis
Hydrogenophilus
Sphingomonas
islandicus
Corynebacterium
Phyllobacterium
freiburgense
Delftia lacustris
Bacteroides
eggerthii
Brevibacterium
Alistipes putredinis
Turicella
massiliense
Paraprevotella
Porphyromonas
asaccharolytica
Parasutterella
Prevotella bivia
Massilia
asaccharolytica
Enterorhabdus
Prevotella buccalis
Microbacterium
Negativicoccus
Prevotella disiens
Cellulosimicrobium
succinicivorans
Bacteroides
Cronobacter
clarus
sakazakii
Sutterella sp.
Aurantimonas
Bifidobacterium
kashiwanohense
Porphyromonas
Lautropia sp.
Gemella
Dermacoccus sp.
morbillorum
Pyramidobacter
Rhizobium etli
Pseudoclavibacter
Veillonella
Anaerostipes
hadrus
Skermanella
Roseomonas
cervicalis
Klebsiella sp.
Burkholderia
Solirubrobacter
Anaerosporobacter
Lactobacillus sp.
Jeotgalicoccus
Ochrobactrum
Leptotrichia
Salinibacterium
Anaerostipes sp.
Rothia
Staphylococcus
equorum
Campylobacter
Actinomyces neuii
Lactobacillus sp.
Cutibacterium
Rubellimicrobium
avidum
Peptoniphilus
Propionimicrobium
Sphingomonas
lymphophilum
oligophenolica
Veillonella sp.
Anaerococcus
Elizabethkingia
hydrogenalis
Corynebacterium
Peptoniphilus
canis
lacrimalis
Bilophila sp.
Anaerococcus
Dietzia cinnamea
lactolyticus
Peptoniphilus
Parvimonas micra
Corynebacterium
Anaerococcus
Wautersiella
tetradius
Corynebacterium
Anaerococcus
Empedobacter
vaginalis
falsenii
Actinomyces sp.
Microbacterium
Patulibacter
Peptoniphilus
Nubsella
zeaxanthinifaciens
Bacteroides
Bilophila
Skermanella
stercorirosoris
aerolata
Blautia stercoris
Bilophila
Acinetobacter sp.
wadsworthia
RBE2CD-76
Peptoniphilus
Dermabacter
hominis
Peptoniphilus
Corynebacterium
Stenotrophomonas
glucuronolyticum
pavanii
Stenotrophomonas
Dialister
Mycobacterium
Shinella sp.
Dialister
Pseudolabrys
pneumosintes
Bacteroides sp.
Stenotrophomonas
Bacillus safensis
Lactobacillus sp.
Brevundimonas
Nubsella
Stenotrophomonas
Jeotgalicoccus
nanhaiensis
Actinomyces sp.
Dermacoccus sp.
Bifidobacterium
Flavobacterium
Fusobacterium
Brachybacterium
Moraxella sp.
Fusobacterium
Rothia
mucilaginosa
Fusobacterium
Abiotrophia
Acinetobacter sp.
Veillonella sp.
Granulicatella
adiacens
Anaerococcus
Abiotrophia
defectiva
Anaerococcus
Bacteroides
Rummeliibacillus
stercoris
Anaerococcus
Lautropia
Pseudomonas
baetica
Anaerococcus
Lactobacillus
Photobacterium
crispatus
Bradyrhizobium
Ralstonia
Chryseomicrobium
imtechense
Sphingomonas
Brevundimonas
Enterococcus sp.
Actinobacillus
Pseudomonas sp.
porcinus
Delftia sp. BN-
Meiothermus
silvanus
Methylobacterium
Pantoea
Microbacterium
Enterococcus sp.
Anaerococcus
octavius
Staphylococcus
Kocuria
Enterobacter sp.
Chryseobacterium
Chryseomicrobium
Megasphaera sp.
Bergeyella
Staphylococcus
Megasphaera sp.
Corynebacterium
Pseudomonas sp.
ulcerans
Corynebacterium
Meiothermus
Pseudomonas sp.
epidermidicanis
Trueperella
Actinomyces
Massilia sp. MA5-1
europaeus
Mesorhizobium
Facklamia
Granulicella
Coprobacter
Facklamia sp. 164-
Micrococcus sp.
fastidiosus
Actinomyces sp.
Dietzia sp. ISA13
Peptoniphilus
Brochothrix sp.
Megasphaera sp.
Hydrogenophilus
Sphingomonas
Anaerococcus
Kocuria rhizophila
Sphingomonas
Ferruginibacter
Faecalibacterium
Sphingobacterium
Murdochiella
Tessaracoccus
Amnibacterium
Lachnoanaerobaculum
Kluyvera georgiana
Comamonas sp.C
Streptococcus
Collinsella
Massilia sp. hp37
aerofaciens
Varibaculum sp.
Campylobacter
Defluviimonas
hominis
Dermabacter sp.
Actinobaculum
Ochrobactrum
Propionibacterium
Actinomyces sp.
Halomonas
pacifica
Atopobium sp.
Bacillus
Aureimonas
pseudofirmus
phyllosphaerae
Atopobium sp.
Stenotrophomonas
Atopobium sp.
Delftia
Salinibacterium
Dialister sp. S4-
Blastocatella
Gardnerella sp.
Acinetobacter sp.
Prevotella sp.
Atopobium vaginae
Rhizobium sp.
Corynebacterium
Facklamia
Bosea sp. B0.09-
frankenforstense
languida
Megasphaera
Gemella sp. 933-88
Brevibacterium
massiliensis
Streptococcus
Exiguobacterium
Veillonella sp.
Jatrophihabitans
Alloprevotella
Chryseobacterium
Peptoniphilus
Phenylobacterium
Dialister sp.
Intestinimonas
butyriciproducens
Lactobacillus sp.
Bosea
Simonsiella
Anaerococcus
Achromobacter
Simonsiella
xylosoxidans
muelleri
Anaerococcus
Mogibacterium
Spirosoma
Finegoldia sp. S8
Propionibacterium
Aquaspirillum
Porphyromonas
Aerococcus
Pseudomonas
christensenii
fluorescens
Actinomyces sp.
Eremococcus
Moraxella sp.
coleocola
Anaerococcus
Lactobacillus
Acidiphilium
fornicalis
Anaerococcus
Dorea longicatena
Pantoea
agglomerans
Finegoldia sp. S9
Pasteurella
multocida
Murdochiella sp.
Erythrobacter
Olsenella sp. S9
Aquabacterium
Dermacoccus
nishinomiyaensis
Peptococcus sp.
Aquabacterium sp.
Streptococcus
pyogenes
Peptoniphilus
Planococcus
Coprobacter
Solobacterium
Bacillus cereus
moorei
Corynebacterium
Lactobacillus
Cellulomonas
jensenii
Atopobium
Granulicatella
Cellulosimicrobium
deltae
cellulans
Helcococcus
Actinomadura
seattlensis
Senegalimassilia
Capnocytophaga
canimorsus
Peptoniphilus
Alicyclobacillus
Romboutsia
Filifactor villosus
Veillonella
seminalis
Terrisporobacter
Solobacterium
Agromyces
Intestinibacter
Pseudomonas
Gordonia sputi
brenneri
Actinomyces
Porphyromonas
radingae
cangingivalis
Olsenella
Porphyromonas
canoris
Kocuria kristinae
Brachybacterium
faecium
Devosia
Catenibacterium
Pedomicrobium
Aerosphaera
Bibersteinia
trehalosi
Aerosphaera taetra
Actinoplanes
auranticolor
Deinococcus
Granulicatella
Kineosporia
elegans
Finegoldia
Polaromonas
Anoxybacillus
Friedmanniella
Anaeroglobus
Janib acteg
Comamonas
Anaeroglobus
Nakamurella
geminatus
Neisseria
Corynebacterium
Amaricoccus
flavescens
mastitidis
Aggregatibacter
Peptoniphilus
Kytococcus
aphrophilus
Aggregatibacter
Gallicola
Aquamicrobium
segnis
Capnocytophaga
Novosphingobium
Planomicrobium
alkanoclasticum
Capnocytophaga
Anaerococcus
Duganella
gingivalis
Thalassospira
Fusibacter
Streptococcus
Brevibacterium
Craurococcus
mutans
paucivorans
Atopobium
Eremococcus
Chitinophaga
rimae
Lactobacillus
Friedmanniella
paracasei
spumicola
Actinomyces
Microbacterium
viscosus
oxydans
Bifidobacterium
Lactobacillus sp.
Luteimonas
adolescentis
Anaeroplasma
Facklamia hominis
Virgibacillus
Asteroleplasma
Pedobacter
Methanosphaera
Varibaculum
Methanosphaera
Varibaculum
stadtmanae
cambriense
Vagococcus
Corynebacterium
spheniscorum
Prevotella
Bacillus flexus
nigrescens
Prevotella oris
Modestobacter
Dolosigranulum
Modestobacter
multiseptatus
Dolosigranulum
Veillonella
Aquamicrobium
pigrum
montpellierensis
lusatiense
Acetitomaculum
Sphingomonas
aquatilis
Mogibacterium
Ochrobactrum
timidum
tritici
Terrisporobacter
Dialister sp. E2_20
Leifsonia
glycolicus
Veillonella
Propionibacterium
Ornithinimicrobium
dispar
Succiniclasticum
Candidatus
Xiphinematobacter
Bifidobacterium
Actinobaculum
Knoellia
massiliense
Corynebacterium
Propionimicrobium
Conchiformibius
matruchotii
steedae
Actinomyces
Planomicrobium
graevenitzii
Corynebacterium
Oerskovia
durum
Microvirga
Streptococcus
peroris
Mannheimia
Haematobacter
massiliensis
Alloprevoteila
Albidovulum
tannerae
inexpectatum
Centipeda
Alkanindiges
illinoisensis
Eggerthella lenta
Bacteroides
massiliensis
Catenibacterium
Ralstonia sp.
mitsuokai
Pseudoflavonifractor
Albidovulum
capillosus
Anaerovorax
Parasporobacterium
Alistipes finegoldii
Bergeyella sp.
Bifidobacterium
Cellulomonas
longum
denverensis
Anaerotruncus
Dialister invisus
Serinicoccus
colihominis
Peptoniphilus sp.
Paucibacter
toxinivorans
Peptoniphilus sp.
Chthoniobacter
Curvibacter gracilis
Phyllobacterium
trifolii
Bacillus sp. T41
Sphingomonas
yunnanensis
Sutterella
Polaromonas
stercoricanis
aquatica
Megasphaera
Fastidiosipila
Conchiformibius
sanguinis
Actinomyces
Bacteroides sp.
Methylobacterium
dentalis
Bacteroides
Pseudoclavibacter
Candidatus
nordii
Alysiosphaera
Oribacterium
Altererythrobacter
Streptococcus
Curvibacter
Bacillus
dentirousetti
pocheonensis
Parabacteroides
Porphyromonas
Belnapia
johnsonii
uenonis
Howardella
Odoribacter
Haematobacter
ureilytica
Corynebacterium
Porphyromonas
crevioricanis
Bacteroides
Flavobacterium
salyersiae
ceti
Desulfovibrio sp.
Roseburia faecis
Flavisolibacter
Bacteroides sp.
Dialister
Nocardioides
propionicifaciens
daphniae
Gordonibacter
Bacteroides
Aureimonas
pamelaeae
coprocola
Atopobium sp.
Shinella
Microbacterium
lacus
Blautia
Parabacteroides
Chryseobacterium
glucerasea
goldsteinii
haifense
Bacteroides sp.
Alistipes shahii
Novosphingobium
panipatense
Butyricimonas
Pelomonas
Nocardioides
virosa
mesophilus
Parabacteroides
Peptostreptococcus
Microlunatus
gordonii
stomatis
aurantiacus
Robinsoniella
Bergeyella sp. AF14
Luteimonas
aestuarii
Bifidobacterium
Peptoniphilus sp.
Saccharibacillus
stercoris
Rhizobium sp.
Peptoniphilus sp.
Singulisphaera
Gordonibacter
Anoxybacillus sp.
Iamia
Slackia sp.
Barnesiella
Duganella sp. 5B
Prevotella sp.
Lysinibacillus
Mycobacterium
Capnocytophaga
Citrobacter sp.
Pseudomonas sp.
Parvimonas sp.
Pseudomonas sp.
Nitrososphaera
Anaerococcus
Brevibacterium
murdochii
pityocampae
Actinomyces sp.
Cronobacter
Oribacterium sp.
Acinetobacter sp.
Oribacterium sp.
Streptococcus sp.
Dietzia lutea
Lachnoanaerobaculum
Methylobacterium
Sphingomonas
Moraxella sp.
Rhizobium sp. sc-w
Chryseobacterium
Lysinibacillus
Veillonella rogosae
Spirosoma rigui
Pseudoflavonifractor
Jonquetella
Pedobacter sp.
Fusobacterium
Jonquetella
Exiguobacterium
anthropi
Dielma
Pelomonas
Neisseria
fastidiosa
aquatica
wadsworthii
Veillonella sp.
Alistipes sp. EBA6-
Variovorax sp.
Actinomyces sp.
Bacteroides sp.
Phascolarctobacterium
Paraprevotella
Pseudomonas sp.
clara
Parabacteroides
Oscillibacter
Jeotgalicoccus sp.
faecis
Veillonella sp.
Anaerobacillus
Nocardioides
alkalidiazotrophicus
ginsengagri
Veillonella sp.
Rhodopseudomonas
Leifsonia
boonkerdii
psychrotolerans
Rothia sp. THG-
Chryseobacterium
Cellulosimicrobium
Actinomyces sp.
Alistipes sp.
Sphingomonas sp.
Bacteroides sp.
Barnesiella
Acinetobacter sp.
intestinihominis
Tessaracoccus
Parasutterella
Jeotgalicoccus
lapidicaptus
excrementihominis
aerolatus
Robinsoniella
Pseudomonas sp.
Chryseobacterium
Candidatus
Porphyromonas
Pseudorhodoferax
Stoquefichus
bennonis
Dielma
Cloacibacterium
Burkholderia sp.
Alistipes inops
Bosea sp.
Pseudomonas sp.
Coprobacter
Peptoniphilus
Pseudomonas sp.
secundus
duerdenii
Psychrobacter
massiliensis
sanguinis
Bifidobacterium
Chryseobacterium
choerinum
Helicobacter
Parvimonas
Planomicrobium
Enterobacter
Hydrogenophilus
Comamonas sp.
cloacae
islandicus
Hafnia
Corynebacterium
Exiguobacterium
freiburgense
Hafnia alvei
Delftia lacustris
Mo destobacter
Klebsiella
Novosphingobium
Brevundimonas
oxytoca
sediminicola
Morganella
Brevibacterium
Psychrobacter sp.
massiliense
Morganella
Paraprevotella
Brevibacterium
morganii
Aeromonas
Parasutterella
Roseburia
Phyllobacterium
cecicola
Fusobacterium
Negativicoccus
varium
succinicivorans
Fusobacterium
Porphyromonas sp.
Euzebya
necrogenes
Fusobacterium
Lautropia sp. TeTO
Methylobacterium
ulcerans
Desulfovibrio
Pseudoclavibacter
Acinetobacter sp.
desulfuricans
Acidaminococcus
Micrococcus sp.
fermentans
Chryseobacterium
Leuconostoc
Rhodococcus sp.
Leuconostoc
Anaerosporobacter
Rhodococcus sp.
mesenteroides
Leuconostoc
Ochrobactrum sp.
Blastococcus sp.
lactis
Pediococcus
Lactobacillus sp.
Mesorhizobium
Clostridium
Peptoniphilus sp.
Knoellia sp.
ventriculi
Blautia hansenii
Veillonella sp. oral
Nocardioides sp.
Streptococcus
Microbacterium
Bacillus sp.
equinus
yannicii
Enterococcus
Corynebacterium
Rhizobium
hirae
canis
skierniewicense
Blautia
Tessaracoccus sp.
Shewanella sp.
coccoides
Romboutsia
Bilophila sp.
Kocuria sp.
lituseburensis
Weissella
Peptoniphilus sp.
Exiguobacterium
confusa
Lactobacillus
Anaerobacillus
Arthrobacter sp.
delbrueckii
Lactobacillus
Corynebacterium
Staphylococcus
animalis
Lactobacillus
Actinomyces sp.
Arthrobacter sp.
ruminis
Bifidobacterium
Peptococcus sp.
Frigoribacterium
catenulatum
Eubacterium
Streptococcus sp.
Methylobacterium
Eubacterium
Brevundimonas sp.
Pseudomonas sp.
limosum
Propionibacterium
Peptoniphilus coxii
Rhodococcus sp.
freudenreichii
Herbaspirillum
Stenotrophomonas
huttiense
Acholeplasma
Peptoniphilus sp.
Sphingobacterium
Sporomusa
Peptoniphilus sp.
Moraxella sp.
Epulopiscium
Ralstonia sp.
Carnobacterium
Stenotrophomonas
Carnobacterium
Negativicoccus
Photobacterium
maltaromaticum
Synergistes
Shinella sp. DR33
Erythrobacter sp.
Bifidobacterium
Bacteroides sp.
Deinococcus sp.
animalis
Lactobacillus
Lactobacillus sp.
Bacteroides sp.
curvatus
Bacteroides
Stenotrophomonas
Aquaspirillum sp.
ovatus
Rikenella
Acinetobacter sp.
Streptococcus sp.
Rikenella
Veillonella sp.
Peptococcus sp.
microfusus
Cellulosilyticum
Actinomyces sp.
Stenotrophomonas
lentocellum
Brachyspira
Fusobacterium sp.
Deinococcus
antarcticus
Brachyspira
Fusobacterium sp.
Hymenobacter
aalborgi
Bacteroides sp.
Fusobacterium sp.
Knoellia sp. Zs20
Blautia producta
Veillonella sp.
Oerskovia sp.
Lactobacillus
Veillonella sp.
Staphylococcus
kefiri
Rahnella
Anaerococcus sp.
Novosphingobium
Citrobacter
Anaerococcus sp.
Variovorax sp.
amalonaticus
Acetivibrio
Anaerococcus sp.
Janibacter sp.
Sporobacter
Anaerococcus
Kytococcus sp.
provencensis
Pseudobutyrivibrio
Bradyrhizobium
ruminis
Weissella
Sphingomonas sp.
Luteococcus
Weissella
Enterococcus sp.
Sorangium
hellenica
Sporomusa
Bosea sp. R-46060
Pedomicrobium
sphaeroides
ferrugineum
Mitsuokella
Delftia sp. BN-
Friedmanniella
Mitsuokella
Staphylococcus sp.
Alkanindiges
multacida
Enterococcus
Methylobacterium
Rheinheimera sp.
durans
Blautia
Staphylococcus sp.
Rhodococcus sp.
hydrogenotrophica
Methanobrevibacter
Brachybacterium
Actinomyces sp.
Megasphaera sp.
Ornithinimicrobium
Finegoldia sp. S5-
Bacteroides
Negativicoccus sp.
Bacteroides
Corynebacterium
thetaiotaomicron
frankenforstense
Bacteroides
Megasphaera
Leptotrichia
uniformis
massiliensis
Corynebacterium
Rothia
vulgatus
Roseburia
Streptococcus sp.
Actinomyces
neuii
Faecalibacterium
Veillonella sp.
Cutibacterium
prausnitzii
avidum
Desulfovibrio
Alloprevotella
Propionimicrobium
lymphophilum
Desulfovibrio
Dialister sp.
Peptoniphilus
lacrimalis
Acidaminococcus
Intestinimonas
Anaerococcus
butyriciproducens
lactolyticus
Herbaspirillum
Lactobacillus sp.
Anaerococcus
tetradius
Herbaspirillum
Anaerococcus sp.
Anaerococcus
seropedicae
vaginalis
Anaerococcus sp.
Microbacterium
Porphyromonas sp.
Actinomyces sp. S9
Dermabacter
Sarcina
Anaerococcus sp.
Dermabacter
Finegoldia sp. S9
Veillonella
atypica
Streptococcus
Murdochiella sp. S9
Corynebacterium
glucuronolyticum
Clostridium
Murdochiella sp. S9
Dialister
Olsenella sp. S9
Stenotrophomonas
Lachnospira
Peptococcus sp. S9
Brevundimonas
Lachnospira
Peptoniphilus sp.
pectinoschiza
Coprobacter
Coprobacter
Asaccharospora
Corynebacterium
Corynebacterium
irregularis
argentoratense
Atopobium deltae
Brachybacterium
Helcococcus
Abiotrophia
seattlensis
Phascolarctobacterium
Staphylococcus sp.
Granulicatella
adiacens
Phascolarctobacterium
Senegalimassilia
Abiotrophia
faecium
defectiva
Peptoniphilus sp.
Lautropia
Dorea
Romboutsia
Lactobacillus
formicigenerans
rhamnosus
Sutterella
Romboutsia
Lactobacillus
crispatus
Pseudobutyrivibrio
Veillonella
Ralstonia
seminalis
Bacteroides
Actinobacillus
porcinus
Holdemania
Pantoea
Holdemania
Anaerococcus
filiformis
octavius
Kocuria
Actinotignum
schaalii
Trueperella
bernardiae
Chryseobacterium
Bergeyella
Eggerthella
Corynebacterium
ulcerans
Actinomyces
europaeus
Facklamia
Bacteroides
Facklamia sp.
acidifaciens
Blautia luti
Facklamia sp.
Mesorhizobium
Bacteroides sp.
Deinococcus
Bacteroides sp.
Lactococcus
Kocuria
rhizophila
Collinsella
Lactococcus
Oscillospira
Tessaracoccus
Roseburia
Spirochaetia
Kluyvera
intestinalis
georgiana
Bacteroides sp.
Collinsella
aerofaciens
Phascolarctobacterium
Actinobaculum
succinatutens
Shuttleworthia
Comamonas
Neisseria flavescens
Bacillus
pseudofirmus
Aggregatibacter
aphrophilus
Aggregatibacter
Delftia
segnis
Pasteurella
Rodentibacter
pneumotropicus
Dorea
Fibrobacter
Facklamia
languida
Selenomonas
Gemella sp. 933-
Capnocytophaga
Bifidobacterium
adolescentis
Bifidobacterium
adolescentis
Anaeroplasma
Anaerostipes
Asteroleplasma
Bosea
Methanosphaera
Achromobacter
stadtmanae
xylosoxidans
Faecalibacterium
Porphyromonas
Mogibacterium
endodontalis
Prevotella
Propionibacterium
intermedia
Alistipes
Prevotella
Aerococcus
nigrescens
christensenii
Akkermansia
Prevotella oris
Eremococcus
coleocola
Akkermansia
Dolosigranulum
Dorea longicatena
muciniphila
Hespellia
Dolosigranulum
pigrum
Anaerotruncus
Acetitomaculum
Marvinbryantia
Mogibacterium
Aquabacterium
timidum
Subdoligranulum
Aquabacterium
Flavonifractor
Veillonella dispar
Candidatus
plautii
Saccharibacteria
Succiniclasticum
Pseudoglutamicibacter
finegoldii
albus
Lactonifactor
Bifidobacterium sp.
Solobacterium
longoviformis
moorei
Roseburia
Porphyromonas
Granulicatella
inulinivorans
catoniae
Blautia
wexlerae
Lactonifactor
Prevotella pallens
Moryella
Adlercreutzia
Streptococcus
equolifaciens
Adlercreutzia
Eggerthella lenta
Catenibacterium
Solobacterium
mitsuokai
Pseudoflavonifractor
Pseudomonas
capillosus
brenneri
Anaerovorax
Actinomyces
radingae
Parasporobacterium
Sedis
Acidaminococcus
Blautia
Turicibacter
sanguinis
Roseburia sp.
Actinomyces sp.
Bacteroides sp.
Anaerotruncus
Alistipes sp.
Victivallis
Aerosphaera
Blautia sp. Ser8
Aerosphaera
taetra
Blautia faecis
Granulicatella
elegans
Turicibacter
Lactobacillus
iners
Scardovia
Finegoldia
Corynebacterium
mastitidis
Eggerthella sp.
Peptoniphilus
Streptococcus
Sphingobium
Flavonifractor
Novosphingobium
Anaerococcus
Anaerostipes
Leptotrichia
Thalassospira
Fusicatenibacter
Megasphaera
Brevibacterium
saccharivorans
paucivorans
Blautia sp.
Scardovia wiggsiae
Eremococcus
Intestinimonas
Fusicatenibacter
Eisenbergiella
Facklamia
hominis
Eisenbergiella
Bacteroides nordii
Varibaculum
tayi
Candidatus
Varibaculum
Soleaferrea
cambriense
Peptoclostridium
Streptococcus
Corynebacterium
dentirousetti
spheniscorum
Asaccharospora
Parabacteroides
johnsonii
Erysipelatoclostridium
Howardella
ureilytica
Bacteroides
Bacteroides
Aggregatibacter
Megasphaera
thetaiotaomicron
micronuciformis
Bacteroides
Prevotella
vulgatus
nanceiensis
Roseburia
Desulfovibrio sp.
Dialister sp.
Faecalibacterium
Bacteroides sp.
Propionibacterium
prausnitzii
Herbaspirillum
Bacteroides sp.
Herbaspirillum
Gordonibacter
Propionimicrobium
seropedicae
Gordonibacter
pamelaeae
Blautia glucerasea
Bacteroides sp.
Sarcina
Bacteroides sp.
Actinomyces oris
Streptococcus
Butyricimonas
virosa
Anaerotruncus sp.
Lachnospira
Parabacteroides
gordonii
Robinsoniella
Bifidobacterium
Subdoligranulum
stercoris
variabile
Rhizobium sp. T45
Alistipes
finegoldii
Odoribacter laneus
Bifidobacterium
longum
Sutterella
Gordonibacter
Dialister invisus
Bacteroides
Gordonibacter
Peptoniphilus sp.
caccae
Slackia sp. NATTS
Peptoniphilus sp.
Prevotella sp. WAL
Curvibacter
gracilis
Bacillus sp. T41
Leptotrichia sp.
Fastidiosipila
sanguinis
Lactobacillus sp.
Helcococcus
sueciensis
Actinomyces sp.
Pseudoclavibacter
Atopobium sp.
Oribacterium
Blautia luti
Oribacterium sp.
Curvibacter
Lachnoanaerobaculum
Odoribacter
Bacteroides sp.
Lachnoanaerobaculum
Roseburia faecis
orale
Collinsella
Pseudomonas sp.
Dialister
propionicifaciens
Pseudoflavonifractor
Dialister
micraerophilus
Fusobacterium sp.
Porphyromonas
somerae
Shuttleworthia
Dielma fastidiosa
Shinella
Phascolarctobacterium
Pelomonas
Phascolarctobacterium
Peptostreptococcus
stomatis
Parabacteroides
Bergeyella sp.
faecis
Veillonella sp.
Bacteroides dorei
Veillonella sp.
Peptoniphilus sp.
Rothia sp. THG-N7
Peptoniphilus sp.
Actinomyces sp.
Prevotella
timonensis
Lysinibacillus
Bacteroides sp.
Citrobacter sp.
Faecalibacterium
Bacteroides sp.
Pseudomonas sp.
Akkermansia
Tessaracoccus
Anaerococcus
lapidicaptus
murdochii
Akkermansia
Fretibacterium
Acinetobacter sp.
muciniphila
Subdoligranulum
Robinsoniella sp.
Streptococcus sp.
Moryella
Candidatus
Methylobacterium
Stoquefichus
Butyricimonas
Veillonella
faecihominis
rogosae
Butyricimonas
Jonquetella
paravirosa
Dielma
Jonquetella
anthropi
Alistipes inops
Pelomonas
aquatica
Blautia
Coprobacter
Pantoea vagans
secundus
Bacteroides sp.
Anaerofilum
Anaerobacillus
alkalidiazotrophicus
Rhodopseudomonas
massiliensis
boonkerdii
Bifidobacterium
Brevibacterium
choerinum
ravenspurgense
Campylobacter
Dialister
jejuni
succinatiphilus
Streptococcus
Moraxella
Pseudomonas sp.
catarrhalis
Hafnia
Porphyromonas
bennonis
Fusicatenibacter
Hafnia alvei
Peptoniphilus
saccharivorans
duerdenii
Fusicatenibacter
Klebsiella oxytoca
Campylobacter
Morganella
Achromobacter
Morganella
Parvimonas
morganii
Pseudomonas
Proteus vulgaris
Corynebacterium
freiburgense
Ralstonia
Aeromonas
Delftia lacustris
pickettii
Fusobacterium
Brevibacterium
perfoetens
massiliense
Rhizobium
Fusobacterium
Phyllobacterium
varium
Mesorhizobium
Fusobacterium
Negativicoccus
loti
necrogenes
succinicivorans
Methylobacterium
Fusobacterium
Lautropia sp.
ulcerans
Neisseria
Acidaminococcus
Pseudoclavibacter
macacae
fermentans
Megasphaera
elsdenii
Ochrobactrum
Rhodobacter
Leuconostoc
Citrobacter
Leuconostoc lactis
Klebsiella sp. B12
Kluyvera
Leuconostoc
Ochrobactrum sp.
carnosum
Clostridium
Lactobacillus sp.
ventriculi
Haemophilus
Blautia hansenii
Peptoniphilus sp.
Haemophilus
Streptococcus
Veillonella sp.
influenzae
equinus
Haemophilus
Enterococcus hirae
Corynebacterium
parainfluenzae
canis
Campylobacter
Blautia coccoides
Tessaracoccus sp.
ureolyticus
Porphyromonas
Romboutsia
Peptoniphilus sp.
lituseburensis
Prevotella
Weissella confusa
Corynebacterium
Fusobacterium
Lactobacillus
Anaerobacillus
delbrueckii
Rhodopseudomonas
Lactobacillus
Corynebacterium
helveticus
Lactobacillus
Actinomyces sp.
Peptostreptococcus
Lactobacillus
Peptococcus sp.
ruminis
Finegoldiamagna
Bifidobacterium
Peptoniphilus sp.
catenulatum
Peptostreptococcus
Eubacterium
Streptococcus sp.
anaerobius
Brevundimonas
limosum
Staphylococcus
Propionibacterium
Peptoniphilus
freudenreichii
coxii
Streptococcus
Stomatobaculum
gordonii
longum
Streptococcus
Acholeplasma
Peptoniphilus sp.
thermophilus
Streptococcus
Sporomusa
Peptoniphilus sp.
agalactiae
Streptococcus
Carnobacterium
Ralstonia sp.
parasanguinis
Streptococcus
Carnobacterium
Stenotrophomonas
anginosus
maltaromaticum
Streptococcus
Synergistes
Negativicoccus
dysgalactiae
Enterococcus
Bifidobacterium
Shinella sp. DR33
Enterococcus
Bacteroides ovatus
Acinetobacter sp.
faecalis
Aerococcus
Bacteroides ovatus
Stenotrophomonas
Aerococcus
Rikenella
Veillonella sp.
urinae
Gemella
Rikenella
Actinomyces sp.
microfusus
Atopobium
Cellulosilyticum
Fusobacterium
lentocellum
Brachyspira
Fusobacterium
Bacillus
Brachyspira
Veillonella sp.
aalborgi
Lysinibacillus
Bacteroides sp.
Anaerococcus sp.
sphaericus
Lactobacillus
Blautia producta
Anaerococcus sp.
Lactobacillus
Lactobacillus kefiri
Anaerococcus sp.
acidophilus
Lactobacillus
Rahnella
Anaerococcus
gasseri
provencensis
Lactobacillus
Citrobacter
Bradyrhizobium
reuteri
amalonaticus
Lactobacillus
Acetivibrio
Sphingomonas
vaginalis
Sporobacter
Enterococcus sp.
Actinomyces
Sporobacter
Bosea sp. R-
termitidis
Actinomyces
Dysgonomonas
Delftia sp. BN-
odontolyticus
capnocytophagoides
Arthrobacter
Pseudobutyrivibrio
Staphylococcus
ruminis
Bifidobacterium
Weissella
Methylobacterium
Bifidobacterium
Weissella hellenica
Staphylococcus
bifidum
Bifidobacterium
Sporomusa
Enterobacter sp.
breve
sphaeroides
Brevibacterium
Mitsuokella
Megasphaera sp.
Corynebacterium
Mitsuokella
Sphingomonas
multacida
Corynebacterium
Enterococcus
Trueperella
durans
Propionibacterium
Eubacterium
Mesorhizobium
callanderi
Cutibacterium
Blautia
Actinomyces sp.
acnes
hydrogenotrophica
Raoultella
Sphingobium sp.
ornithinolytica
Enterobacter
Anaerococcus sp.
asburiae
Mobiluncus
Methanobrevibacter
Mobiluncus
Anaerobacter
Murdochiella
Mobiluncus
Desulfovibrio sp.
Lachnoanaerobaculum
mulieris
Varibaculum sp.
Pseudomonas
Dermabacter sp.
monteilii
Mycoplasma
Bifidobacterium
Propionibacterium
merycicum
Mycoplasma
Bifidobacterium
Stomatobaculum
hominis
pullorum
Ureaplasma
Actinomyces sp.
Ureaplasma
Anaerovibrio
Atopobium sp.
urealyticum
Gardnerella
Anaerosinus
Atopobium sp.
glycerini
Gardnerella
Atopobium sp.
vaginalis
Peptococcus
Succinivibrio
Gardnerella sp.
Globicatella
Prevotella sp. S4-
Globicatella
Lactobacillus
Negativicoccus
sanguinis
mucosae
Sphingomonas
Papillibacter
Corynebacterium
Prevotella bivia
Coprobacillus
Streptococcus sp.
Prevotella
Proteus penneri
Alloprevotella
buccalis
Prevotella
Lactobacillus
Stenotrophomonas
disiens
algidus
Anaerostipes caccae
Bradyrhizobium
Arcanobacterium
Pelistega
Anaerococcus sp.
Arcanobacterium
Cupriavidus
Anaerococcus sp.
haemolyticum
Gemella
Parasporobacterium
Finegoldia sp. S8
morbillorum
paucivorans
Veillonella
Campylobacter
Finegoldia sp. S9
parvula
faecalis
Oscillospira
Murdochiella sp.
Fusobacterium
Peptoniphilus sp.
Corynebacterium
Bacillus sp. HC15
Ralstonia sp. A52
Helcococcus
Weissella cibaria
Staphylococcus
Peptoniphilus sp.
Fusobacteria
Azospira
Veillonella
seminalis
Leptotrichia
Collinsella
Intestinibacter
intestinalis
Rothia
Anaerosinus
Actinomyces
Dysgonomonas
neuii
Propionimicrobium
Bifidobacterium
lymphophilum
scardovii
Anaerococcus
Enterococcus
hydrogenalis
pallens
Peptoniphilus
Raoultella
lacrimalis
Anaerococcus
Marvinbryantia
lactolyticus
formatexigens
Anaerococcus
Victivallis vadensis
Cutibacterium
prevotii
Anaerococcus
Blautia schinkii
vaginalis
Lactobacillus
Robinsoniella
Deinococcus
johnsonii
peoriensis
Dermabacter
Lactococcus
Veillonella
atypica
Dialister
Stenotrophomonas
Sneathia
Mitsuokella
Neisseria canis
sanguinegens
jalaludinii
Gelria
Aggregatibacter
aphrophilus
Sedimentibacter
Aggregatibacter
segnis
Brachybacterium
Corynebacterium
atypicum
Abiotrophia
Capnocytophaga
Abiotrophia
Capnocytophaga
defectiva
gingivalis
Lactobacillus
Allisonella
Capnocytophaga
rhamnosus
sputigena
Lactobacillus
Allisonella
crispatus
Allisonella
Actinomyces
histaminiformans
viscosus
Anaerococcus
Allisonella
Cardiobacterium
octavius
histaminiformans
Actinotignum
Anaerofustis
Cardiobacterium
schaalii
stercorihominis
hominis
Trueperella
Tannerella
bernardiae
forsythia
Actinomyces
Eggerthella sinensis
Porphyromonas
europaeus
endodontalis
Facklamia
Bifidobacterium
Prevotella
thermacidophilum
nigrescens
Facklamia sp.
Bacteroides sp.
Prevotella oris
Facklamia sp.
Slackia faecicanis
Prevotella
oulorum
Mesorhizobium
Anaerosporobacter
Dolosigranulum
mobilis
Anaerofustis
Dolosigranulum
pigrum
Alistipes
Leptotrichia
massiliensis
buccalis
Veillonella sp. ADV
Bifidobacterium
Tessaracoccus
Catabacter
Porphyromonas
catoniae
Kluyvera
Catabacter
Corynebacterium
georgiana
hongkongensis
matruchotii
Collinsella
Pseudoclavibacter
Capnocytophaga
aerofaciens
bifida
granulosa
Campylobacter
Bacteroides sp.
Actinomyces
hominis
georgiae
Actinobaculum
Actinomyces
graevenitzii
Bifidobacterium
Prevotella pallens
gallicum
Bacillus
Proteiniphilum
Corynebacterium
pseudofirmus
durum
Streptococcus sp.
Streptococcus
peroris
Delftia
Bacteroides sp.
Alloprevotella
tannerae
Bacteroides sp.
Centipeda
Paucibacter
Centipeda
periodontii
Atopobium
Alistipes
Mogibacterium
vaginae
onderdonkii
pumilum
Facklamia
Mitsuokella sp.
Moraxella caprae
languida
Oscillibacter
valericigenes
Bifidobacterium
tsurumiense
Megasphaera sp.
Turicibacter
sanguinis
Anaerococcus sp.
Leptotrichia
wadei
Weissella sp. H1a
Rothia aeria
Bacteroides
Turicibacter
xylanisolvens
Barnesiella
viscericola
Bosea
Pediococcus sp.
Achromobacter
Cronobacter
xylosoxidans
dublinensis
Mogibacterium
Cronobacter
Actinomyces
turicensis
dentalis
Aerococcus
Aggregatibacter
christensenii
Lactobacillus
Cellulosilyticum
Prevotella
fornicalis
ruminicola
nanceiensis
Oligella
Bacteroides sp.
Lachnoanaerobaculum
saburreum
Oligella
Sutterella
Leptotrichia
urethralis
parvirubra
hongkongensis
Paraprevotella
Bifidobacterium
xylaniphila
stercoris
Bacteroides sp.
Rhizobium sp.
Candidatus
Parabacteroides sp.
Leptotrichia sp.
Saccharibacteria
Pseudoglutamicibacter
Nosocomiicoccus
Alloprevotella
albus
rava
Lactobacillus
Nosocomiicoccus
Neisseria
jensenii
ampullae
skkuensis
Granulicatella
Comamonas sp. j41
Capnocytophaga
Butyricicoccus
Leptotrichia sp.
pullicaecorum
Bulleidia
Cloacibacterium
Fusobacterium
rupense
Bulleidia
Fusobacterium sp.
Brevundimonas
extructa
Mitsuokella sp.
Moraxella sp.
Pseudomonas sp.
Butyricimonas
Lysinibacillus sp.
synergistica
Actinomyces
Selenomonas sp.
Fusobacterium
turicensis
Streptococcus sp.
Neisseria oralis
Asaccharobacter
Rothia sp. THG-
Coprobacillus sp.
Candidatus
Saccharimonas
Globicatella
Bifidobacterium sp.
Bacteroides sp.
sulfidifaciens
Aerosphaera
Butyricicoccus
Pseudomonas
aeruginosa
Aerosphaera
Bacteroides sp. D20
Moraxella
taetra
catarrhalis
Granulicatella
Hydrogenoanaerobacterium
Enterobacter
elegans
cloacae
Lactobacillus
Bacteroides fluxus
Morganella
iners
Finegoldia
Bacteroides
Morganella
oleiciplenus
morganii
Anaeroglobus
Alistipes
Aeromonas
indistinctus
Anaeroglobus
Slackia piriformis
Rhodobacter
geminatus
Pseudoglutamic
Collinsella tanakaei
Leuconostoc
ibacter
cumminsii
Peptoniphilus
Pyramidobacter
Leuconostoc
piscolens
lactis
Gallicola
Bacteroides sp. TP-5
Weissella confusa
Sphingobium
Anaerostipes
Lactobacillus
butyraticus
curvatus
Anaerococcus
Aeromonas sp. B11
Sneathia
Acinetobacter sp.
Pseudomonas
monteilii
Brevibacterium
Acinetobacter sp.
paucivorans
Parabacteroides sp.
Pantoea gaviniae
Lactobacillus
Enterorhabdus
Corynebacterium
caecimuris
atypicum
Facklamia
Bacteroides faecis
hominis
Actinomyces
Bacteroides faecis
hongkongensis
Lactobacillus
Blautia sp. Ser5
Pseudoclavibacter
coleohominis
bifida
Varibaculum
Eubacterium sp.
Nosocomiicoccus
Varibaculum
Bacteroides
Nosocomiicoccus
cambriense
rodentium
ampullae
Corynebacterium
Paucibacter sp. 186
Aeromonas sp.
spheniscorum
Cellulosilyticum
Acinetobacter sp.
Caldicoprobacter
Lactobacillus sp.
Enterobacter sp.
Pseudomonas sp.
Lactobacillus sp.
Pseudomonas sp.
Veillonella
Bifidobacterium
Lactococcus sp.
montpellierensis
biavatii
Dialister sp.
Megasphaera sp.
Pseudomonas sp.
Propionibacterium
Pseudomonas sp.
Propionibacterium
Streptococcus
Pseudomonas sp.
Streptococcus sp.
pasteurianus
Rothia sp. RV13
Rahnella sp.
Actinobaculum
Klebsiella sp.
Shewanella
massiliense
Propionimicrobium
Lactococcus sp.
Caulobacter
Pseudomonas sp.
Caulobacter sp.
Campylobacter sp.
Lactobacillus sp.
Leuconostoc sp.
Brevundimonas
diminuta
Bacteroides
Bacteroides sp.
massiliensis
Parabacteroides sp.
Aeromonas
salmonicida
Bifidobacterium
Bacteroides sp.
Streptococcus
longum
sobrinus
Peptoniphilus
Methanomassiliicoccus
Alloiococcus
Peptoniphilus
Anaerovibrio sp.
Alloiococcus otitis
Actinomyces sp.
Anaerovibrio sp.
Pseudonocardia
Curvibacter
Acidaminococcus
gracilis
Bacillus sp. T41
Finegoldia sp.
Brochothrix
Sutterella
Cruoricaptor
Solanum
stercoricanis
ignavus
lycopersicum
Fastidiosipila
Lactococcus sp.
Solanum
Fastidiosipila
Herbaspirillum sp.
Acidovorax
sanguinis
Helcococcus
Phascolarctobacterium
Sphingobium
sueciensis
yanoikuyae
Pseudoclavibacter
Peptococcus sp.
Curvibacter
Proteiniclasticum
Turicella otitidis
Porphyromonas
Bacteroides sp.
Staphylococcus
uenonis
saprophyticus
Dialister
Comamonas
Janthinobacterium
propionicifaciens
jiangduensis
Dialister
Turicibacter sp.
Cutibacterium
micraerophilus
granulosum
Porphyromonas
Acidaminococcus
Microbacterium
somerae
lacticum
Pelomonas
Propionibacterium
Variovorax
Peptoniphilus
Brachybacterium
Blastococcus
aggregatus
Peptoniphilus
Butyricimonas sp.
Acinetobacter
radioresistens
Prevotella
Butyricimonas sp.
Leucobacter
timonensis
Lysinibacillus
Anaerostipes
Nesterenkonia
rhamnosivorans
Howardella
Butyricimonas sp.
Dermacoccus
Citrobacter sp.
Streptococcus sp.
Anaerococcus
Streptococcus sp.
murdochii
Arcanobacterium
Veillonella sp.
Streptococcus
Sutterella sp. 252
Methylobacterium
Roseburia sp. 499
Prevotella
Anaerostipes sp.
amnii
Alloscardovia
Anaerostipes sp.
Alloscardovia
Rahnella sp. FB303
omnicolens
Rhizobium sp.
Citrobacter sp.
Jonquetella
Megasphaera sp.
Pelomonas
Megasphaera sp.
Hymenobacter
aquatica
Anaerobacillus
Candidatus
Acinetobacter
alkalidiazotrophicus
Methanomethylophilus
ursingii
Rhodopseudomonas
Rahnella sp. BSP15
Dyadobacter
boonkerdii
Brevibacterium
Rahnella sp. BSP18
ravenspurgense
Dialister
Bacteroides
Turicella
succinatiphilus
caecigallinarum
Porphyromonas
Pelistega indica
bennonis
Bosea sp.
Cruoricaptor
Massilia
Peptoniphilus
Terrisporobacter
Sphingomonas
duerdenii
petrolearius
aerolata
Murdochiella
asaccharolytica
Dermacoccus sp.
Atopobium sp.
Sedis
Parvimonas
Solirubrobacter
Brachybacterium
muris
Corynebacterium
Bacteroides
Actinomyces
freiburgense
thetaiotaomicron
Delftia lacustris
Bacteroides
Staphylococcus
uniformis
equorum
Bifidobacterium
Bacteroides
vulgatus
Brevibacterium
Roseburia
Rubellimicrobium
massiliense
Porphyromonas
Faecalibacterium
Burkholderia sp.
prausnitzii
Pseudoclavibacter
Herbaspirillum
Sphingomonas
oligophenolica
Herbaspirillum
seropedicae
Methylobacterium
adhaesivum
Lactobacillus
Dermacoccus sp.
Lactobacillus
Sarcina
Flavobacterium
Ochrobactrum
Moraxella sp.
Lactobacillus
Streptococcus
Peptoniphilus
Clostridium
Kocuria sp.
Corynebacterium
Brevundimonas
canis
Tessaracoccus
Lachnospira
Pseudomonas sp.
Staphylococcus
Lachnospira
Microbacterium
pectinoschiza
Peptoniphilus
Corynebacterium
Anaerobacillus
Asaccharospora
Aerococcus sp.
Peptoniphilus
Acinetobacter sp.
Peptoniphilus
Micrococcus sp.
Herbaspirillum
Phascolarctobacterium
Nesterenkonia sp.
huttiense
faecium
Peptoniphilus
Pseudomonas sp.
Peptoniphilus
Dorea
Sphingomonas
formicigenerans
Stenotrophomonas
Sutterella
Sphingomonas
Negativicoccus
Pseudobutyrivibrio
Ferruginibacter
Lactobacillus
Bacteroides caccae
Massilia sp. hp37
Fusobacterium
Defluviimonas
Fusobacterium
Ochrobactrum sp.
Anaerococcus
Acinetobacter sp.
Anaerococcus
Rhizobium sp.
Anaerococcus
Brevibacterium
Anaerococcus
Eggerthella
Moraxella sp.
provencensis
Enterococcus
Yersinia
Bosea sp. R-
Yersinia
enterocolitica
Lactobacillus
Bacteroides
Streptococcus
acidifaciens
oralis
Delftia sp. BN-
Blautia luti
Bacillus cereus
Staphylococcus
Peredibacter
starrii
Megasphaera
Bacteroides sp.
Brachybacterium
faecium
Megasphaera
Bacteroides sp.
Kytococcus
Corynebacterium
Collinsella
Duganella
epidermidicanis
Trueperella
Oscillospira
Fusibacter
Mesorhizobium
Actinomyces sp.
Roseburia
Sphingomonas
intestinalis
aquatilis
Jonquetella sp.
Ochrobactrum
tritici
Prevotella sp.
Shuttleworthia
Peptoniphilus
Candidatus
Xiphinematobacter
Megasphaera
Albidovulum
inexpectatum
Sphingobium
Alkanindiges
illinoisensis
Anaerococcus
Albidovulum
Faecalibacterium
Dorea
Murdochiella
Varibaculum
Conchiformibius
Varibaculum
Propionibacterium
Staphylococcus
Actinomyces sp.
Rothia sp. BBH4
Atopobium sp.
Luteimonas
aestuarii
Atopobium sp.
Anaerostipes
Acidovorax sp.
Atopobium sp.
Sedis
Dialister sp. S4-
Chryseobacterium
Finegoldia sp.
Faecalibacterium
Pseudomonas sp.
Gardnerella sp.
Chryseobacterium
Prevotella sp.
Alistipes
Kocuria sp. M1-
Peptoniphilus
Akkermansia
Stenotrophomonas
Finegoldia sp.
Akkermansia
Deinococcus sp.
muciniphila
Negativicoccus
Anaerotruncus
Variovorax sp.
Corynebacterium
Marvinbryantia
Kytococcus sp.
frankenforstense
Megasphaera
Subdoligranulum
Alkanindiges
massiliensis
Corynebacterium
Flavonifractor
Campylobacter
sputorum
Streptococcus
Roseburia
Pseudomonas
inulinivorans
syringae
Veillonella sp.
Blautia wexlerae
Bordetella
Peptoniphilus
Moryella
Pedobacter
heparinus
Dialister sp.
Bergeyella
zoohelcum
Stenotrophomonas
Porphyrobacter
Lactobacillus
Streptococcus
pneumoniae
Bradyrhizobium
Aerococcus
viridans
Anaerococcus
Blautia
Actinomyces
israelii
Finegoldia sp.
Roseburia sp.
Mycobacterium
chelonae
Porphyromonas
Bacteroides sp. D22
Brachymonas
Actinomyces sp.
Alistipes sp. RMA
Pseudomonas
agarici
Anaerococcus
Blautia faecis
Staphylococcus
vitulinus
Anaerococcus
Moraxella
lincolnii
Finegoldia sp.
Alkalibacterium
Murdochiella
Streptococcus sp.
Xenophilus
Peptococcus sp.
Flavonifractor
Flavobacterium
Peptoniphilus
Corynebacterium
caspium
Corynebacterium
Anaerostipes sp.
Epilithonimonas
Atopobium
Fusicatenibacter
Porphyrobacter
deltae
saccharivorans
Parvibacter
Blautia sp. YHC-4
Brevundimonas
Ralstonia sp.
Massilia oculi
Helcococcus
Xenophilus sp.
seattlensis
Staphylococcus
Eisenbergiella
Kocuria sp. LW2-
Senegalimassilia
Eisenbergiella tayi
Pseudochrobactrum
Veillonella
Candidatus
Pseudochrobactrum
seminalis
Soleaferrea
Peptoclostridium
Flavobacterium
johnsoniae
Asaccharospora
Brochothrix
thermosphacta
Erysipelatoclostridium
cynodegmi
Campylobacter
Microbacterium
xylanilyticum
Campylobacter
Pseudomonas sp.
concisus
Achromobacter
Flavobacterium
rivuli
Flavobacterium
Chryseobacterium
anthropi
Lactobacillus
Deinococcus
taiwanensis
taklimakanensis
Phascolarctobacterium
Rhizobium
Halomonas sp.
succinatutens
Leptotrichia
Methylobacterium
Alkalibacterium
hongkongensis
Methylobacterium
Methylobacterium
organophilum
Bifidobacterium
Moraxella
Shewanella sp.
choerinum
Mycoplasma
Sphingobacterium
spermatophilum
Aerococcus
Neisseria
Bradyrhizobium
sanguinicola
Brevibacterium
Neisseria mucosa
Pseudonocardia
Lactobacillus
Neisseria elongata
Chryseobacterium
Neisseria macacae
Myroides
Bacteroides
Kocuria sp. PDM-7
Bacteroides
Flavobacterium
thetaiotaomicron
Bacteroides
Kluyvera
thetaiotaomicron
Bacteroides
Bacteroides
uniformis
Bacteroides
Actinobacillus
Bacteroides
uniformis
uniformis
Bacteroides
Haemophilus
Bacteroides
vulgatus
vulgatus
Bacteroides
Haemophilus
Roseburia
vulgatus
influenzae
Roseburia
Haemophilus
Faecalibacterium
parainfluenzae
prausnitzii
Roseburia
Bacteroides fragilis
Herbaspirillum
Faecalibacterium
Parabacteroides
prausnitzii
distasonis
Desulfovibrio
Campylobacter
gracilis
Desulfovibrio
Campylobacter
ureolyticus
Acidaminococcus
Butyrivibrio
Sarcina
Herbaspirillum
Porphyromonas
Herbaspirillum
Prevotella
Streptococcus
seropedicae
Fusobacterium
Clostridium
Fusobacterium
nucleatum
Fusobacterium
Lachnospira
periodonticum
Megasphaera
Lachnospira
pectinoschiza
Sarcina
Peptostreptococcus
Sarcina
Staphylococcus
Phascolarctobacterium
Staphylococcus
Phascolarctobacterium
epidermidis
faecium
Streptococcus
Streptococcus
gordonii
Streptococcus
Streptococcus
Dorea
thermophilus
formicigenerans
Clostridium
Streptococcus
Sutterella
parasanguinis
Clostridium
Enterococcus
Pseudobutyrivibrio
Aerococcus
Bacteroides
caccae
Gemella
Lachnospira
Atopobium
Verrucomicrobia
Lachnospira
pectinoschiza
Lachnospira
Clostridioides
pectinoschiza
difficile
Erysipelatoclostridium
ramosum
Lactobacillus
Lactobacillus
salivarius
Asaccharospora
Lactobacillus
Bacteroides
irregularis
vaginalis
acidifaciens
Blautia luti
Actinomyces
Actinomyces
Bacteroides sp.
odontolyticus
Bifidobacterium
Bacteroides sp.
Phascolarctobacterium
Corynebacterium
Collinsella
Phascolarctobacterium
Corynebacterium
Phascolarctobacterium
Propionibacterium
Roseburia
faecium
intestinalis
Cutibacterium
acnes
Dorea
formicigenerans
Dorea
Mycobacterium
Shuttleworthia
formicigenerans
Sutterella
Pseudobutyrivibrio
Rothia
dentocariosa
Pseudobutyrivibrio
Bacteroides
caccae
Bacteroides
Methanobrevibacter
Dorea
Holdemania
Methanobrevibacter
smithii
Holdemania
Gardnerella
Holdemania
Gardnerella
filiformis
vaginalis
Holdemania
Peptococcus
Verrucomicrobiae
filiformis
Verrucomicrobia
Sphingomonas
Anaerostipes
Bacteroides
eggerthii
Alistipes putredinis
Odoribacter
Faecalibacterium
splanchnicus
Prevotella bivia
Alistipes
Eggerthella
Prevotella disiens
Akkermansia
Anaerotruncus
Subdoligranulum
Bacteroides
Gemella
Blautia wexlerae
acidifaciens
morbillorum
Blautia luti
Veillonella
Moryella
Blautia luti
Veillonella parvula
Bacteroides sp.
Bacteroides sp.
Sedis
Bacteroides sp.
Blautia
Bacteroides sp.
Eggerthia
Roseburia sp.
catenaformis
Collinsella
Leptotrichia
Blautia faecis
Collinsella
Rothia
Oscillospira
Parvimonas micra
Oscillospira
Bilophila
Streptococcus sp.
Roseburia
Bilophila
Flavonifractor
intestinalis
wadsworthia
Roseburia
Veillonella atypica
intestinalis
Corynebacterium
Anaerostipes sp.
glucuronolyticum
Dialister
Fusicatenibacter
saccharivorans
Dialister
Fusicatenibacter
pneumosintes
Sutterella
Peptoclostridium
wadsworthensis
Shuttleworthia
Brevundimonas
Erysipelatoclostridium
Campylobacter
Flavobacterium
Rothia
Pseudomonas
mucilaginosa
Butyrivibrio
crossotus
Abiotrophia
Bradyrhizobium
Granulicatella
Rhizobium
adiacens
Abiotrophia
Mesorhizobium
defectiva
loti
Parabacteroides
Methylobacterium
merdae
Bacteroides
stercoris
Dorea
Lautropia
Acinetobacter
Dorea
Lactobacillus
crispatus
Neisseria
Actinobacillus
Neisseria mucosa
porcinus
Pantoea
Neisseria
macacae
Chryseobacterium
Ochrobactrum
Bergeyella
Corynebacterium
Citrobacter
ulcerans
Enterobacter
Klebsiella
Kluyvera
Anaerostipes
Tessaracoccus
Proteus
Anaerostipes
Kluyvera georgiana
Serratia
Collinsella
aerofaciens
Actinobacillus
Haemophilus
Faecalibacterium
Delftia
Haemophilus
influenzae
Haemophilus
parainfluenzae
Alistipes
Parabacteroides
distasonis
Alistipes
Gemella sp. 933-88
Campylobacter
ureolyticus
Akkermansia
Porphyromonas
Akkermansia
Prevotella
muciniphila
Hespellia
Fusobacterium
Hespellia
Fusobacterium
nucleatum
Anaerotruncus
Mogibacterium
Anaerotruncus
Aerococcus
Peptostreptococcus
christensenii
Marvinbryantia
Dorea longicatena
Finegoldia magna
Marvinbryantia
Peptostreptococcus
anaerobius
Subdoligranulum
Flavonifractor
Candidatus
Micrococcus
plautii
Saccharibacteria
Bacteroides
Solobacterium
Micrococcus
finegoldii
moorei
luteus
Lactonifactor
Granulicatella
Staphylococcus
longoviformis
Roseburia
Staphylococcus
inulinivorans
aureus
Roseburia
Staphylococcus
inulinivorans
epidermidis
Blautia
wexlerae
Thermus
Blautia
Solobacterium
Streptococcus
wexlerae
gordonii
Lactonifactor
Olsenella
Streptococcus
thermophilus
Moryella
Streptococcus
parasanguinis
Adlercreutzia
Enterococcus
equolifaciens
Adlercreutzia
Catenibacterium
Aerococcus
equolifaciens
Adlercreutzia
Granulicatella
Gemella
elegans
Adlercreutzia
Finegoldia
Anaeroglobus
Bacillus
Anaeroglobus
Lysinibacillus
geminatus
sphaericus
Megamonas
Lactobacillus
Anaerococcus
Actinomyces
Sedis
Actinomyces
odontolyticus
Sedis
Acidaminococcus
Bifidobacterium
Blautia
Varibaculum
Brevibacterium
cambriense
Blautia
Corynebacterium
Corynebacterium
spheniscorum
Roseburia sp.
Corynebacterium
diphtheriae
Roseburia sp.
Corynebacterium
Bacteroides sp.
Propionibacterium
Alistipes sp.
Cutibacterium
acnes
Blautia sp. Ser8
Megasphaera
micronuciformis
Blautia faecis
Propionibacterium
Mycobacterium
Blautia faecis
Rhodococcus
Rhodococcus
erythropolis
Rothia
dentocariosa
Bacteroides
massiliensis
Gardnerella
Gardnerella
vaginalis
Eggerthella sp.
Subdoligranulum
Halomonas
HGA1
variabile
Streptococcus
Alistipes finegoldii
Globicatella
Flavonifractor
Bifidobacterium
Globicatella
longum
sanguinis
Flavonifractor
Dialister invisus
Phyllobacterium
Peptoniphilus sp.
Alistipes
putredinis
Sutterella
Odoribacter
stercoricanis
splanchnicus
Anaerostipes
Oribacterium
Porphyromonas
asaccharolytica
Anaerostipes
Porphyromonas
Prevotella
uenonis
buccalis
Fusicatenibacter
Odoribacter
Prevotella disiens
saccharivorans
Fusicatenibacter
Roseburia hominis
saccharivorans
Blautia sp.
Roseburia faecis
Intestinimonas
Dialister
Arcanobacterium
micraerophilus
Intestinimonas
Bacteroides
Gemella
plebeius
morbillorum
Fusicatenibacter
Bacteroides
Rhizobium etli
coprocola
Fusicatenibacter
Parabacteroides
Veillonella
goldsteinii
Eisenbergiella
Bacteroides
Veillonella
intestinalis
parvula
Eisenbergiella
Peptostreptococcus
tayi
stomatis
Candidatus
Peptoniphilus sp.
Soleaferrea
Peptoclostridium
Bacteroides sp.
Helcococcus
Asaccharospora
Parabacteroides
Erysipelatoclostridium
Barnesiella
Fusobacteria
Erysipelatoclostridium
Howardella
Leptotrichia
Campylobacter
Streptococcus sp.
Rothia
Achromobacter
Alloscardovia
Actinomyces
neuii
Flavobacterium
Alloscardovia
Cutibacterium
omnicolens
avidum
Pseudomonas
Veillonella rogosae
Propionimicrobium
lymphophilum
Megamonas
Anaerococcus
funiformis
hydrogenalis
Bradyrhizobium
Alistipes sp. EBA6-
Peptoniphilus
lacrimalis
Moraxella
Bacteroides sp.
Anaerococcus
lactolyticus
Oscillibacter
Parvimonas
micra
Neisseria
Alistipes sp.
Anaerococcus
prevotii
Neisseria
Barnesiella
Anaerococcus
mucosa
intestinihominis
tetradius
Neisseria
Parasutterella
Microbacterium
macacae
excrementihominis
Porphyromonas
Dermabacter
bennonis
Ochrobactrum
Dermabacter
hominis
Corynebacterium
Incertae Sedis
glucuronolyticum
Citrobacter
Parvimonas
Dialister
Klebsiella
Stenotrophomonas
Kluyvera
Delftia lacustris
Brevundimonas
Proteus
Butyricimonas
Proteus
Paraprevotella
mirabilis
Parasutterella
Corynebacterium
argentoratense
Enterorhabdus
Brachybacterium
Actinobacillus
Bacteroides clarus
Rothia
mucilaginosa
Haemophilus
Bifidobacterium
Granulicatella
kashiwanohense
adiacens
Haemophilus
Lautropia sp. TeTO
Bacteroides
stercoris
Haemophilus
Anaerostipes
Lactobacillus
influenzae
hadrus
crispatus
Haemophilus
Ralstonia
parainfluenzae
Haemophilus
parainfluenzae
Bacteroides
Actinobacillus
fragilis
porcinus
Bacteroides
Anaerosporobacter
Meiothermus
fragilis
silvanus
Parabacteroides
Lactobacillus sp.
Pantoea
distasonis
Parabacteroides
Campylobacter sp.
Anaerococcus
distasonis
octavius
Campylobacter
Lactobacillus sp.
Kocuria
gracilis
Campylobacter
Veillonella sp. oral
Trueperella
ureolyticus
bernardiae
Butyrivibrio
Anaerobacillus
Chryseobacterium
Porphyromonas
Actinomyces sp.
Meiothermus
Prevotella
Peptococcus sp.
Facklamia
Fusobacterium
Streptococcus sp.
Facklamia sp.
Fusobacterium
Stomatobaculum
Mesorhizobium
mortiferum
longum
Fusobacterium
Bacteroides
nucleatum
stercorirosoris
Fusobacterium
Blautia stercoris
Kocuria
periodonticum
rhizophila
Desulfovibrio
Alistipes sp. HGB5
piger
Megasphaera
Bacteroides sp.
Weeksella
Lactobacillus sp.
Tessaracoccus
Weeksella
Veillonella sp.
Kluyvera
virosa
georgiana
Actinomyces sp.
Collinsella
aerofaciens
Peptostreptococcus
Bifidobacterium sp.
Actinobaculum
Finegoldia
Campylobacter sp.
magna
Finegoldia
Fusobacterium sp.
Bacillus
magna
pseudofirmus
Peptostreptococcus
Fusobacterium sp.
anaerobius
Staphylococcus
Bradyrhizobium sp.
Delftia
Staphylococcus
Delftia sp. BN-
simulans
Methylobacterium
Thermus
Streptococcus
Staphylococcus sp.
Gemella sp. 933-
gordonii
Streptococcus
Megasphaera sp.
thermophilus
Streptococcus
Coprobacter
thermophilus
fastidiosus
Streptococcus
Actinomyces sp.
parasanguinis
Enterococcus
Enterococcus
Faecalibacterium
faecalis
Lactococcus
Lachnoanaerobaculum
lactis
Lactococcus
Streptococcus sp.
Bosea
lactis
Aerococcus
Stomatobaculum
Achromobacter
xylosoxidans
Gemella
Solobacterium sp.
Propionibacterium
Atopobium
Megasphaera
Aerococcus
massiliensis
christensenii
Atopobium
Streptococcus sp.
Lactobacillus
minutum
fornicalis
Veillonella sp.
Dorea longicatena
Bacillus
Eggerthia
Clostridioides
Alloprevotella
difficile
Erysipelatoclostridium
Finegoldia sp. S8
ramosum
Lactobacillus
Finegoldia sp. S9
Pseudoglutamicibacter
albus
Lactobacillus
Coprobacter
Solobacterium
acidophilus
moorei
Lactobacillus
Staphylococcus sp.
Granulicatella
gasseri
Lactobacillus
Terrisporobacter
reuteri
Lactobacillus
Intestinibacter
salivarius
Lactobacillus
vaginalis
Actinomyces
Solobacterium
Actinomyces
Actinomyces
odontolyticus
radingae
Arthrobacter
Johnsonella
Arthrobacter
Bifidobacterium
Bifidobacterium
Bacteroides sp.
Bifidobacterium
Phascolarctobacterium
Aerosphaera
bifidum
succinatutens
Bifidobacterium
Campylobacter
Aerosphaera taetra
bifidum
showae
Bifidobacterium
Comamonas
Lactobacillus
breve
iners
Bifidobacterium
Neisseria flavescens
Finegoldia
dentium
Brevibacterium
Neisseria sicca
Corynebacterium
mastitidis
Corynebacterium
Bergeriella
Peptoniphilus
denitrificans
Corynebacterium
Kingella oralis
Gallicola
Corynebacterium
Eikenella
Novosphingobium
diphtheriae
Corynebacterium
Eikenella corrodens
Anaerococcus
diphtheriae
Corynebacterium
Aggregatibacter
Brevibacterium
aphrophilus
paucivorans
Propionibacterium
Aggregatibacter
segnis
Cutibacterium
Pasteurella
acnes
Rodentibacter
Corynebacterium
pneumotropicus
spheniscorum
Mycobacterium
Fibrobacter
Rhodococcus
Porphyromonas
gingivalis
Desulfobulbus
Propionibacterium
Rothia
Selenomonas
dentocariosa
Capnocytophaga
Mobiluncus
Capnocytophaga
gingivalis
Mobiluncus
Capnocytophaga
curtisii
ochracea
Mobiluncus
Capnocytophaga
mulieris
sputigena
Streptococcus
mutans
Mycoplasma
Streptococcus
intermedius
Mycoplasma
Atopobium
Subdoligranulum
hominis
parvulum
variabile
Ureaplasma
Atopobium rimae
Peptoniphilus sp.
Ureaplasma
Lactobacillus
Peptoniphilus sp.
urealyticum
paracasei
Actinomyces
Bacillus sp. T41
naeslundii
Actinomyces
Fastidiosipila
viscosus
Methanobrevibacter
Bifidobacterium
Fastidiosipila
adolescentis
sanguinis
Methanobrevibacter
Pseudopropionibacterium
Cloacibacterium
smithii
propionicum
normanense
Gardnerella
Anaeroplasma
Helcococcus
sueciensis
Gardnerella
Mycoplasma
Pseudoclavibacter
vaginalis
salivarium
Peptococcus
Methanosphaera
Odoribacter
Peptococcus
Methanosphaera
Roseburia faecis
stadtmanae
Peptococcus
Cardiobacterium
Dialister
niger
propionicifaciens
Cardiobacterium
Porphyromonas
hominis
somerae
Bifidobacterium
Vagococcus
Pelomonas
pseudocatenulatum
Phyllobacterium
Streptococcus mitis
Peptoniphilus sp.
Bacteroides
Tannerella
Peptoniphilus sp.
eggerthii
forsythia
Alistipes
Porphyromonas
Parabacteroides
putredinis
endodontalis
Alistipes
Prevotella
Lysinibacillus
putredinis
intermedia
Odoribacter
Prevotella
Citrobacter sp.
splanchnicus
nigrescens
Odoribacter
Prevotella oralis
Pseudomonas sp.
splanchnicus
Porphyromonas
Prevotella oris
Anaerococcus
asaccharolytica
murdochii
Prevotella
Prevotella oulorum
Acinetobacter sp.
buccalis
Prevotella
Actinomyces sp.
Streptococcus sp.
disiens
Cronobacter
Dolosigranulum
Methylobacterium
sakazakii
Dolosigranulum
Rhizobium sp. sc-w
pigrum
Arcanobacterium
Acetitomaculum
Pelomonas
aquatica
Arcanobacterium
Kingella
Bacteroides sp.
haemolyticum
Mogibacterium
Pantoea vagans
timidum
Veillonella
Terrisporobacter
Anaerobacillus
glycolicus
alkalidiazotrophicus
Veillonella
Veillonella dispar
Brevibacterium
ravenspurgense
Veillonella
Leptotrichia
Parasutterella
parvula
buccalis
excrementihominis
Porphyromonas
Porphyromonas
catoniae
bennonis
Corynebacterium
matruchotii
Incertae Sedis
Catonella
Parvimonas
Catonella morbi
Corynebacterium
freiburgense
Filifactor
Delftia lacustris
Helcococcus
Capnocytophaga
Butyricimonas
granulosa
Capnocytophaga
Parasutterella
haemolytica
Leptotrichia
Actinomyces
Phyllobacterium
georgiae
Rothia
Actinomyces
Porphyromonas
gerencseriae
Actinomyces
Actinomyces
Pseudoclavibacter
neuii
meyeri
Cutibacterium
Actinomyces
Klebsiella sp. B12
avidum
graevenitzii
Propionimicrobium
Ochrobactrum sp.
lymphophilum
Anaerococcus
Prevotella pallens
Lactobacillus sp.
hydrogenalis
Peptoniphilus
Corynebacterium
Veillonella sp.
lacrimalis
durum
Anaerococcus
Microbacterium
lactolyticus
yannicii
Parvimonas
Streptococcus
Corynebacterium
micra
peroris
canis
Anaerococcus
Streptococcus
Tessaracoccus sp.
prevotii
infantis
Anaerococcus
Alloprevotella
Staphylococcus
tetradius
tannerae
Anaerococcus
Parascardovia
Anaerobacillus
vaginalis
denticolens
Anaerococcus
Centipeda
Corynebacterium
vaginalis
Lactobacillus
Centipeda
Actinomyces sp.
johnsonii
periodontii
Eggerthella lenta
Peptoniphilus sp.
Bilophila
Gemella sanguinis
Streptococcus sp.
Bilophila
Cryptobacterium
Brevundimonas
wadsworthia
Dermabacter
Cryptobacterium
Stomatobaculum
curtum
longum
Veillonella
Rothia sp. CCUG
Herbaspirillums
atypica
huttiense
Corynebacterium
Mannheimia
Peptoniphilus sp.
glucuronolyticum
granulomatis
Dialister
Mogibacterium
Ralstonia sp.
pumilum
Dialister
Mycoplasma
Stenotrophomonas
falconis
Dialister
Mycoplasma
Negativicoccus
pneumosintes
subdolum
Sneathia
Catenibacterium
Shinella sp. DR33
sanguinegens
mitsuokai
Sutterella
Anaerovorax
Acinetobacter sp.
wadsworthensis
C-S-NA3
Leptotrichia
Stenotrophomonas
trevisanii
Parasporobacterium
Veillonella sp.
Corynebacterium
Actinomyces sp.
argentoratense
Brachybacterium
Fusobacterium
Rothia
Filifactor alocis
Veillonella sp.
mucilaginosa
Butyrivibrio
Leptotrichia wadei
Anaerococcus sp.
crossotus
Abiotrophia
Leptotrichia
Anaerococcus sp.
hofstadii
Granulicatella
Leptotrichia shahii
Anaerococcus sp.
adiacens
Abiotrophia
Leptotrichia
Anaerococcus
defectiva
goodfellowii
provencensis
Parabacteroides
Actinomyces sp.
Bradyrhizobium
merdae
Parabacteroides
Rothia aeria
Delftia sp. BN-
merdae
Bacteroides
Victivallis
Methylobacterium
stercoris
Lautropia
Staphylococcus
Lactobacillus
Megasphaera sp.
rhamnosus
Alysiella
Sphingomonas
Actinobacillus
Tannerella
Corynebacterium
porcinus
epidermidicanis
Pantoea
Scardovia
Trueperella
Anaerococcus
Parascardovia
Mesorhizobium
octavius
Actinotignum
Peptoniphilus sp.
schaalii
Trueperella
Sphingobium sp.
bernardiae
Bergeyella
Anaerococcus sp.
Corynebacterium
ulcerans
Facklamia
Faecalibacterium
Facklamia sp.
Murdochiella
Facklamia sp.
Varibaculum sp.
Leptotrichia
Dermabacter sp.
genomo sp. C1
Megasphaera
Propionibacterium
genomo sp. C1
Scardovia wiggsiae
Stomatobaculum
Tessaracoccus
Selenomonas
Negativicoccus
genomo sp. P5
Kluyvera
Corynebacterium
georgiana
Collinsella
Veillonella sp.
aerofaciens
Collinsella
Neisseria
Alloprevotella
aerofaciens
bacilliformis
Campylobacter
Actinomyces
Peptoniphilus sp.
hominis
dentalis
Actinobaculum
Stenotrophomonas
Bifidobacterium
Bacteroides nordii
Bradyrhizobium
gallicum
Anaerococcus sp.
Delftia
Capnocytophaga
Anaerococcus sp.
Capnocytophaga
Finegoldia sp. S9
Atopobium
Bergeriella
Peptococcus sp.
vaginae
Slackia
Capnocytophaga
Peptoniphilus sp.
Gemella sp.
Streptococcus
Ralstonia sp. A52
dentirousetti
Parabacteroides
Staphylococcus
johnsonii
Terrisporobacter
Intestinibacter
Aggregatibacter
Prevotella
nanceiensis
Veillonella sp.
Desulfovibrio sp.
Actinomyces
massiliensis
Lachnoanaerobaculum
Cutibacterium
saburreum
Bacteroides sp.
Deinococcus
Achromobacter
Gordonibacter
Lactococcus
xylosoxidans
Mogibacterium
Atopobium sp.
Johnsonella
Aerocoecus
Olsenella sp.
christensenii
Eremococcus
Bacteroides sp.
Comamonas
coleocola
Lactobacillus
Actinomyces oris
Selenomonas
fornicalis
Dorea
Butyricimonas
Capnocytophaga
longicatena
virosa
Dorea
Anaerotruncus sp.
longicatena
Oligella
Leptotrichia
Actinomyces
hongkongensis
viscosus
Oligella
Prevotella
urethralis
oulorum
Parabacteroides
Dolosigranulum
gordonii
Prevotella
Dolosigranulum
aurantiaca
pigrum
Neisseria
Corynebacterium
shayeganii
matruchotii
Pseudoglutamicibacter
Lachnoanaerobaculum
Actinomyces
albus
umeaense
georgiae
Solobacterium
Rhizobium sp. T45
Actinomyces
moorei
gerencseriae
Veillonella ratti
Odoribacter laneus
Lactobacillus
Gordonibacter
Corynebacterium
jensenii
durum
Granulicatella
Slackia sp. NATTS
Streptococcus
peroris
Fretibacterium
Centipeda
fastidiosum
Oribacterium sp.
Centipeda
periodontii
Prevotella sp. oral
Solobacterium
Leptotrichia sp.
Leptotrichia
wadei
Actinomyces
Oribacterium sp.
Leptotrichia
radingae
hofstadii
Actinomyces
Alloprevotella rava
turicensis
Olsenella
Prevotella sp. WAL
Neisseria skkuensis
Actinomyces sp.
Capnocytophaga
Selenomonas
genomo sp. P5
Actinomyces sp.
Actinomyces
dentalis
Catenibacterium
Actinomyces sp.
Aggregatibacter
Catenibacterium
Capnocytophaga
Lachnoanaerobaculum
saburreum
Globicatella
Capnocytophaga
Rhizobium sp.
sulfidifaciens
Aerosphaera
Desulfobulbus sp.
Actinomyces sp.
Aerosphaera
Leptotrichia sp.
Brevundimonas
taetra
Lactobacillus
Oribacterium sp.
Pseudomonas sp.
iners
Finegoldia
Prevotella sp. oral
Lysinibacillus sp.
Finegoldia
Shuttleworthia sp.
Rothia sp. THG-
Anaeroglobus
Streptococcus sp.
Bacteroides sp.
Anaeroglobus
Tannerella sp. oral
Moraxella
geminatus
catarrhalis
Pseudoglutamicibacter
Parvimonas sp. oral
Enterobacter
cumminsii
cloacae
Megamonas
Morganella
Corynebacterium
Leptotrichia sp.
Morganella
mastitidis
morganii
Peptoniphilus
Leptotrichia sp.
Aeromonas
PTE15
Gallicola
Lactobacillus sp.
Leuconostoc
NRCT-KU 1
Novosphingobium
Methylobacterium
Weissella
longum
Anaerococcus
Capnocytophaga
Sneathia
Mogibacterium sp.
Pseudomonas
monteilii
Thalassospira
Mogibacterium sp.
Brevibacterium
Selenomonas sp.
paucivorans
Eremococcus
Actinomyces sp.
Actinomyces sp.
Azospira
Actinomyces sp.
Raoultella
Atopobium sp.
Fusobacterium sp.
Lactobacillus
Oribacterium sp.
Facklamia
Lachnoanaerobaculum
Paucibacter
hominis
Actinomyces
Lachnoanaerobaculum
Paucibacter sp.
hongkongensis
Lactobacillus
Brevundimonas sp.
Pseudomonas sp.
coleohominis
Vagococcus sp.
Pseudomonas sp.
Varibaculum
Moraxella sp.
Finegoldia sp.
Varibaculum
Lachnoanaerobaculum
Comamonas
orale
jiangduensis
Varibaculum
Actinomyces sp.
Propionibacterium
cambriense
Pseudomonas sp.
Lysinibacillus sp.
Shewanella
Fusobacterium sp.
Lysobacter
Veillonella sp. JL-2
Caulobacter
Neisseria oralis
Megasphaera
Veillonella
Gemmata
micronuciformis
tobetsuensis
Acidaminococcus
Actinomyces sp.
intestini
Veillonella
Neisseria sp.
Elizabethkingia
montpellierensis
meningoseptica
Phascolarctobacterium
Brevundimonas
diminuta
Dialister sp.
Parabacteroides
Xanthomonas
faecis
Dialister sp.
Streptococcus sp.
Propionibacterium
Veillonella sp.
Acinetobacter
baumannii
Rothia sp. THG-N7
Moraxella
nonliquefaciens
Propionimicrobium
Capnocytophaga
Zymomonas
Actinomyces sp.
Aeromonas
salmonicida
Streptococcus
sobrinus
Candidatus
Bacillus
Saccharimonas
megaterium
Bacteroides
Bacteroides sp.
Geobacillus
massiliensis
stearothermophilus
Tessaracoccus
Kurthia
lapidicaptus
Fretibacterium
Nocardioides
Subdoligranulum
Robinsoniella sp.
Pseudonocardia
variabile
Alistipes
Butyricimonas
Streptomyces
finegoldii
faecihominis
Alistipes
Butyricimonas
Gordonia terrae
finegoldii
paravirosa
Bifidobacterium
Alistipes inops
longum
Bifidobacterium
longum
massiliensis
Dialister invisus
Peptoniphilus
Bacteroides
Solanum
lycopersicum
Peptoniphilus
Bacteroides
Solanum
vulgatus
Actinomyces sp.
Roseburia
Basidiomycota
Sutterella
Faecalibacterium
Acidovorax
stercoricanis
prausnitzii
Fastidiosipila
Herbaspirillum
Acidothermus
Fastidiosipila
Sphingobacterium
sanguinis
Helcococcus
Turicella otitidis
sueciensis
Bacteroides sp.
Staphylococcus
saprophyticus
Pseudoclavibacter
Sarcina
Microlunatus
Oribacterium
Rhodoplanes
Porphyromonas
Streptococcus
Janthinobacterium
uenonis
Odoribacter
Clostridium
Cutibacterium
granulosum
Odoribacter
Microbacterium
lacticum
Corynebacterium
Lachnospira
Exiguobacterium
Bacteroides
Lachnospira
Variovorax
salyersiae
pectinoschiza
Bacteroides
Dietzia
salyersiae
Roseburia
Blastococcus
hominis
aggregatus
Roseburia
Acinetobacter
hominis
radioresistens
Roseburia
Paenibacillus
faecis
Roseburia
faecis
Dialister
Dorea
Pseudomonas
propionicifaciens
formicigenerans
citronellolis
Dialister
Pseudobutyrivibrio
Malassezia
micraerophilus
Bacteroides
Leucobacter
plebeius
Parabacteroides
Dermacoccus
goldsteinii
Alistipes shahii
Malassezia
restricta
Alistipes shahii
Bacteroides
intestinalis
Pelomonas
Peptostreptococcus
stomatis
Bergeyella sp.
Blautia luti
Bacteroides
dorei
Bacteroides
Bacteroides sp.
dorei
Peptoniphilus
Bacteroides sp.
Peptoniphilus
Collinsella
Bacteroides sp.
Roseburia
intestinalis
Moryella
indoligenes
Parabacteroides
Parabacteroides
Shuttleworthia
Marmoricola
Prevotella
Marmoricola
timonensis
aurantiacus
Barnesiella
Facklamia
tabacinasalis
Barnesiella
Howardella
Hymenobacter
Citrobacter sp.
Frigoribacterium
Anaerococcus
Dorea
Acinetobacter
murdochii
ursingii
Arcanobacterium
Dyadobacter
Cronobacter
Roseomonas
Streptococcus
Geobacillus
Prevotella
Corynebacterium
amnii
capitovis
Alloscardovia
Anaerostipes
Corynebacterium
felinum
Alloscardovia
omnicolens
Veillonella
Faecalibacterium
rogosae
Jonquetella
Alistipes
anthropi
Pelomonas
Akkermansia
Turicella
aquatica
Megamonas
Akkermansia
funiformis
muciniphila
Alistipes sp.
Anaerotruncus
Massilia
Bacteroides sp.
Subdoligranulum
Microbacterium
Bacteroides sp.
Roseburia
Cellulosimicrobium
inulinivorans
Paraprevotella
Blautia wexlerae
clara
Oscillibacter
Moryella
Gemmatimonas
Oscillibacter
Aurantimonas
Alistipes sp.
Sphingomonas
Incertae Sedis
aerolata
Alistipes sp.
Blautia
Brevibacterium
Roseburia sp.
Thermoactinomy
ravenspurgense
Dialister
Blautia faecis
Dermacoccus sp.
succinatiphilus
Barnesiella
intestinihominis
Barnesiella
Microbacterium
intestinihominis
paraoxydans
Parasutterella
Streptococcus sp.
Planctomycetes
excrementihominis
Parasutterella
Flavonifractor
Planctomycetia
excrementihominis
Porphyromonas
Anaerostipes sp.
bennonis
Cloacibacterium
Fusicatenibacter
saccharivorans
Gemella
Fusicatenibacter
Skermanella
asaccharolytica
Peptoniphilus
Erysipelatoclostridium
Roseomonas
duerdenii
cervicalis
Peptoniphilus
Campylobacter
Solirubrobacter
koenoeneniae
Murdochiella
Achromobacter
Brachybacterium
asaccharolytica
muris
Flavobacterium
Cloacibacillus
Pseudomonas
Cloacibacillus
evryensis
Atopobium sp.
Bradyrhizobium
Kocuria marina
Rhizobium
Actinomyces
genomo sp. C1
Sedis
Parvimonas
Mesorhizobium loti
Salinibacterium
Methylobacterium
Corynebacterium
freiburgense
Delftia lacustris
Acinetobacter
Lysobacter
brunescens
Butyricimonas
Moraxella
Bifidobacterium
Rubellimicrobium
Brevibacterium
Neisseria
Elizabethkingia
massiliense
Paraprevotella
Neisseria mucosa
Parasutterella
Neisseria elongata
Dietzia cinnamea
Parasutterella
Neisseria macacae
Fluviicola
Enterorhabdus
Bacteroides
Ochrobactrum
Truepera
clarus
Bacteroides
Methylobacterium
clarus
adhaesivum
Sutterella sp.
Citrobacter
Bifidobacterium
Enterobacter
kashiwanohense
Porphyromonas
Klebsiella
Patulibacter
Lautropia sp.
Kluyvera
Solirubrobacter
Pseudoclavibacter
Proteus
Sphingomonas
anadarae
Anaerostipes
Nubsella
hadrus
zeaxanthinifaciens
Anaerostipes
Actinobacillus
Skermanella
hadrus
aerolata
Haemophilus
Actinomycetospora
Haemophilus
Acinetobacter sp.
influenzae
Haemophilus
Flavobacterium
parainfluenzae
lindanitolerans
Klebsiella sp.
Campylobacter
Sphingomonas
ureolyticus
mathurensis
Anaerosporobacter
Porphyromonas
Lactobacillus
Prevotella
Stenotrophomonas
pavanii
Ochrobactrum
Fusobacterium
Mycobacterium
Anaerostipes
Fusobacterium
Pseudolabrys
nucleatum
Lactobacillus
Fusobacterium
Bacillus safensis
periodonticum
Peptoniphilus
Megasphaera
Nubsella
Veillonella sp.
Rhodopseudomonas
Microbacterium
Corynebacterium
Dermacoccus sp.
canis
Tessaracoccus
Peptostreptococcus
Flavobacterium
Bilophila sp.
Finegoldia magna
Methylobacterium
Peptoniphilus
Peptostreptococcus
anaerobius
Anaerobacillus
Acinetobacter
kyonggiensis
Corynebacterium
Micrococcus
Acinetobacter sp.
Peptoniphilus
Micrococcus luteus
Streptococcus
Staphylococcus
Rummeliibacillus
Streptococcus
Staphylococcus
Chryseomicrobium
aureus
imtechense
Lactobacillus
Brevundimonas
Thermus
Peptoniphilus
Streptococcus
Pseudomonas sp.
coxii
thermophilus
Stomatobaculum
Streptococcus
longum
parasanguinis
Bacteroides
Enterococcus
Pseudomonas sp.
stercorirosoris
Blautia stercoris
Enterococcus
Methylobacterium
faecalis
Blautia stercoris
Lactococcus lactis
Methylobacterium
Peptoniphilus
Aerococcus
Peptoniphilus
Aerococcus urinae
Peptoniphilus
Gemella
Pseudomonas sp.
Ralstonia sp.
Chryseomicrobium
Alistipes sp.
Bacillus
Bryobacter
Negativicoccus
Lysinibacillus
Novosphingobium
sphaericus
Bacteroides sp.
Lactobacillus
Staphylococcus
Lactobacillus
Lactobacillus
Pseudomonas sp.
plantarum
Stenotrophomonas
Lactobacillus
Pseudomonas sp.
reuteri
Actinomyces sp.
Lactobacillus
Acinetobacter sp.
salivarius
Bifidobacterium
Granulicella
Fusobacterium
Actinomyces
Mycobacterium
Fusobacterium
Actinomyces
Acinetobacter
odontolyticus
Veillonella sp.
Arthrobacter
Micrococcus sp.
Veillonella sp.
Bifidobacterium
Dietzia sp. ISA13
bifidum
Anaerococcus
Brevibacterium
Psychrobacter sp.
Anaerococcus
Corynebacterium
Pseudomonas sp.
Anaerococcus
Corynebacterium
Sphingomonas
provencensis
diphtheriae
Enterococcus
Corynebacterium
Gaiella occulta
Delftia sp. BN-
Propionibacterium
Ferruginibacter
Enterococcus
Cutibacterium
Sphingobacterium
acnes
Brachybacterium
Amnibacterium
Enterobacter
Rhodococcus
Rhizobium
nepotum
Megasphaera
Rhodococcus
erythropolis
Corynebacterium
Comamonas sp.
epidermidicanis
Trueperella
Rothia
Massilia sp. hp37
dentocariosa
Coprobacter
Mobiluncus
Defluviimonas
fastidiosus
mulieris
Coprobacter
Gardnerella
Ochrobactrum sp.
fastidiosus
Actinomyces sp.
Gardnerella
vaginalis
Jonquetella sp.
Peptococcus
Prevotella sp.
Halomonas
Gaiella
Peptoniphilus sp.
Aureimonas
phyllosphaerae
Megasphaera
Globicatella
Massilia sp. S5-
Anaerococcus
Globicatella
Stenotrophomonas
sanguinis
Sphingomonas
Stenotrophomonas
Faecalibacterium
Phyllobacterium
Sphingomonas
Faecalibacterium
Alistipes putredinis
Chryseobacterium
Murdochiella
Odoribacter
Blastocatella
splanchnicus
fastidiosa
Lachnoanaerobaculum
Porphyromonas
Massilia sp.
asaccharolytica
Streptococcus
Prevotella bivia
Salinibacterium
Varibaculum sp.
Prevotella buccalis
Blastocatella
Varibaculum
Prevotella disiens
Acinetobacter sp.
Dermabacter
Sphingomonas
Stomatobaculum
Chryseobacterium
Actinomyces sp.
Rhizobium sp.
Atopobium sp.
Gemella
Bosea sp. B0.09-
morbillorum
Atopobium sp.
Rhizobium etli
Sphingomonas
Atopobium sp.
Veillonella
Exiguobacterium
Dialister sp. S4-
Veillonella parvula
Jatrophihabitans
Gardnerella sp.
Chryseobacterium
Prevotella sp.
Flavobacterium
qiangtangense
Solobacterium
Mycobacterium
Helcococcus
Pseudonocardia
This application is a continuation-in-part of U.S. application Ser. No. 15/606,743, filed 26 May 2017, which is a continuation of U.S. application Ser. No. 14/919,614, filed 21 Oct. 2017, which claims the benefit of U.S. Provisional Application Ser. No. 62/066,369 filed 21 Oct. 2014, U.S. Provisional Application Ser. No. 62/087,551 filed 4 Dec. 2014, U.S. Provisional Application Ser. No. 62/092,999 filed 17 Dec. 2014, U.S. Provisional Application Ser. No. 62/147,376 filed 14 Apr. 2015, U.S. Provisional Application Ser. No. 62/147,212 filed 14 Apr. 2015, U.S. Provisional Application Ser. No. 62/147,362 filed 14 Apr. 2015, U.S. Provisional Application Ser. No. 62/146,855 filed 13 Apr. 2015, and U.S. Provisional Application Ser. No. 62/206,654 filed 18 Aug. 2015, which are each incorporated in its entirety herein by this reference. This application additionally claims the benefit of U.S. Provisional Application Ser. No. 62/514,356 filed 2 Jun. 2017 and U.S. Provisional Application Ser. No. 62/515,396 filed 5 Jun. 2017, which are each herein incorporated in their entirety by this reference.
| Number | Date | Country | |
|---|---|---|---|
| 62514356 | Jun 2017 | US | |
| 62515396 | Jun 2017 | US | |
| 62066369 | Oct 2014 | US | |
| 62087551 | Dec 2014 | US | |
| 62092999 | Dec 2014 | US | |
| 62147376 | Apr 2015 | US | |
| 62147212 | Apr 2015 | US | |
| 62147362 | Apr 2015 | US | |
| 62146855 | Apr 2015 | US | |
| 62206654 | Aug 2015 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | 14919614 | Oct 2015 | US |
| Child | 15606743 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | 15606743 | May 2017 | US |
| Child | 15997654 | US |
