Patent Application
20140278127
The present disclosure relates, generally, to nucleotide sequence data and, more particularly, to computer files and methods supporting forensic analysis of nucleotide sequence data.
Polymorphic tandem repeats of nucleotide sequences are found throughout the human genome, and the particular combinations of allelic states at multiple repeat sites are sufficiently unique to an individual that these repeating sequences can be used in human or other organism identification. These markers are also useful in genetic mapping and linkage analysis, where the tandem repeat sites may be linked to sites important for determining, for example, predisposition for disease. Tandem repeats can be used directly in human identity testing, such as in forensics analysis. There are many types of tandem repeats of nucleic acids, all falling under the general term variable number tandem repeats (VNTR). These minisatellites and microsatellites are also called short tandem repeats (STR).
One application of tandem repeat analysis is in forensics or human identity testing. In current forensics analyses, highly polymorphic STRs are identified using a DNA sample from an individual and DNA amplification steps, such as polymerase chain reaction (PCR), to provide amplified samples of partial DNA sequences, or amplicons, from the individual's DNA. The amplicons can then be matched by size (i.e., repeat numbers) to reference databases, such as the sequences stored in national or local DNA databases. For example, amplicons that originate from STR loci can be matched to reference STR databases, including the FBI CODIS database in the United States, or the NDNAD database in Europe, to identify the individual by matching to the STR alleles specific to that individual.
Forensic DNA analysis is about to cross a threshold where DNA samples will begin to be analyzed routinely by massively parallel sequencing (MPS), also sometimes referred to in the art as next-generation sequencing. The advent of routine MPS for forensic DNA analysis will create large quantities of nucleotide sequence data that may enable richer exploitation of DNA in forensic applications. Once information is generated on the genetic profile of an individual (e.g., for either forensic investigative purposes or confirmatory matching), the resulting nucleotide sequence data should be formatted for exchange among law enforcement entities. However, no uniform, workable standards for nucleotide sequence data storage and exchange suitable for law enforcement applications currently exist.
In addition to the foregoing storage and exchange problem, forensic analysis requires the preservation of data, including raw data, for evidentiary purposes. In conventional capillary-electrophoresis (CE) workflows, the raw data typically preserved as evidence includes raw nucleotide sequence data (e.g., “.hid” or “.fsa” files) and an image of the electropherogram (e.g., printed or a graphics file). This evidence is routinely provided during the jurisprudence discovery process. The forensic results can be reproduced from “.hid” or “.fsa” files using appropriate software applications. Alternatively, these results can be read directly from the electropherograms by persons practiced in the art. The “.hid” and “.fsa” files produced by typical CE workflows are relatively small and can be easily transmitted or stored. The electropherogram graphics are also small enough to be easily transferred or stored. By contrast, data files created by MPS workflows are typically larger than 1 GB making them difficult to transmit or store. In addition, these files, while text-based, are not human-readable in any practical sense because of their large size. Thus, other human readable forms of the raw data from MPS workflows are needed.
According to one aspect, a method may comprise receiving a first text-based computer file including one or more records, each of the one or more records comprising nucleotide sequence data generated by a read of a massively parallel sequencing (MPS) instrument, determining, for each of the one or more records of the first text-based file, whether a portion of the nucleotide sequence data of the record represents a short tandem repeat (STR) associated with a locus, placing each portion of the nucleotide sequence data determined to represent an STR associated with a locus into one of a number of locus-specific lists, determining, for each of the locus-specific lists, a number of occurrences within the locus-specific list of identical nucleotide sequence data representing a unique STR, and generating a second text-based computer file including one or more records, each of the one or more records corresponding to a unique STR for which the number of occurrences of identical nucleotide sequence data representing the unique STR exceeded an abundance threshold.
In some embodiments, the first text-based computer file may be formatted as a FASTQ file. Determining whether a portion of the nucleotide sequence data of a record represents an STR associated with a locus may comprise determining whether a portion of the nucleotide sequence data of the record represents a primer sequence used to amplify the locus. Determining whether a portion of the nucleotide sequence data of the record represents a primer sequence used to amplify the locus may comprise referencing an updateable library of primer sequences. Placing each portion of the nucleotide sequence data determined to represent an STR associated with a locus into one of a number of locus-specific lists may comprise removing a portion of the nucleotide sequence data representing a flanking sequence. Removing a portion of the nucleotide sequence data representing a flanking sequence may comprise referencing an updateable library of flanking sequences. The abundance threshold may be user-defined.
Generating the second text-based computer file may comprise, for each of the one or more records, writing nucleotide sequence data representing the corresponding unique STR to a second text line of the record. Generating the second text-based computer file may further comprise, for each of the one or more records, writing average quality scores for the nucleotide sequence data representing the corresponding unique STR to a fourth text line of the record. The average quality scores may be formatted as average Phred quality scores. Generating the second text-based computer file may further comprise, for each of the one or more records, writing forensic metadata associated with the nucleotide sequence data representing the corresponding unique STR to a first text line of the record. The forensic metadata may be copied from the first text-based computer file. Generating the second text-based computer file may further comprise, for each of the one or more records, writing an attribute-value pair specifying the number of occurrences of identical nucleotide sequence data representing the corresponding unique STR to a third text line of the record.
In some embodiments, generating the second text-based computer file may further comprise, for each of the one or more records, writing a human-readable sequence-based allele (HRSBA) designation that is deterministic of the corresponding unique STR to a third text line of the record. Generating the HRSBA designation may comprise reading a plurality of nucleotide bases in a sliding window that moves along the nucleotide sequence data, determining whether the plurality of nucleotide bases corresponds to a canonical motif of a locus associated with the corresponding unique STR, adding the plurality of nucleotide bases to the HRSBA in response to determining that the plurality of nucleotide bases corresponds to a canonical motif of the locus and represents a first instance of the canonical motif, and moving the sliding window by a plurality of positions in response to determining that the plurality of nucleotide bases corresponds to a canonical motif of the locus.
In some embodiments, generating the HRSBA designation may further comprise adding only a first nucleotide base of the plurality of nucleotide bases to the HRSBA in response to determining that the plurality of nucleotide bases does not correspond to a canonical motif of the locus and moving the sliding window by one position in response to determining that the plurality of nucleotide bases does not correspond to a canonical motif of the locus. The sliding window may move along the nucleotide sequence data in a 5′ to 3′ direction. Determining whether the plurality of nucleotide bases corresponds to a canonical motif of a locus associated with the corresponding unique STR may comprise referencing an updatable library of canonical motifs of one or more loci. Generating the HRSBA designation may further comprise determining whether a final plurality of nucleotide bases of the nucleotide sequence corresponds to a canonical ending motif of a locus associated with the corresponding unique STR and generating a user alert in response to determining that the final plurality of nucleotide bases does not correspond to a canonical ending motif of the locus. Generating the HRSBA designation may comprise referencing an updatable library associating common nucleotide sequences with corresponding HRSBA designations.
According to another aspect, a method may comprise receiving forensic metadata associated with nucleotide sequence data generated by a massively parallel sequencing (MPS) instrument and writing the forensic metadata to a text-based computer file comprising the nucleotide sequence data. In some embodiments, receiving the forensic metadata may comprise receiving data from a case management system of a laboratory operating the MPS instrument. The text-based computer file comprising the nucleotide sequence data may include one or more records, each of the one or more records representing a read of the MPS instrument. The text-based computer file comprising the nucleotide sequence data may be formatted as a FASTQ file.
In some embodiments, writing the forensic metadata to the text-based computer file may comprise writing the forensic metadata to a first text line of each of the one or more records of the text-based computer file. The first text line of each of the one or more records of the text-based computer file may comprise a unique sequence identifier created by the MPS instrument when generating the nucleotide sequence data. Writing the forensic metadata to the first text line of each of the one or more records of the text-based computer file may comprise appending the forensic metadata to the unique sequence identifier. Writing the forensic metadata to the text-based computer file may comprise writing one or more attribute-value pairs to the text-based computer file, each of the one or more attribute-value pairs specifying one of a file format, a unique case identifier, a unique sample identifier, a unique laboratory identifier, and a unique technician identifier.
According to yet another aspect, a computer-readable medium may storing a text-based file comprising one or more records, each of the one or more records including a first text line comprising forensic metadata, a second text line comprising a number of characters representing nucleotide sequence data, a third text line, and a fourth text line comprising a number of characters representing quality scores associated with the nucleotide sequence data.
In some embodiments, each of the characters of the second text line may represent an output of a base call algorithm performed by a massively parallel sequencing (MPS) instrument. The first text line may further comprise a unique sequence identifier created by a massively parallel sequencing (MPS) instrument when generating the nucleotide sequence data. The forensic metadata of the first text line may comprise one or more attribute-value pairs, each of the one or more attribute-value pairs specifying one of a file format, a unique case identifier, a unique sample identifier, a unique laboratory identifier, and a unique technician identifier.
In some embodiments, the nucleotide sequence data may represent a short tandem repeat (STR) for which a read count from a sample exceeded an abundance threshold. The third text line may comprise an attribute-value pair specifying the read count of the STR. Each of the characters of the fourth text line may represent an average quality score associated with a corresponding character of the second text line, the average quality score being a function of quality scores associated with all reads of the STR. Each of the characters of the fourth text line may be formatted as an average Phred quality score. The third text line may comprise one or more attribute-value pairs, each of the one or more attribute-value pairs specifying one of a locus of the STR, a strand of the STR, an analytic threshold associated with the STR, and a designation of the STR as corresponding to one of an allele, a stutter, and an artifact.
In some embodiments, the third text line may comprise a human-readable sequence-based allele (HRSBA) designation that is deterministic of the STR. The HRSBA designation may comprise a first attribute-value pair summarizing the STR in a 5′ to 3′ direction. The first attribute-value pair of the HRSBA designation may comprise one or more integers each followed by one or more characters, each of the one or more integers representing a nucleotide position and each of the one or more characters representing a nucleotide base. The one or more characters following each of the one or more integers in the first attribute-value pair of the HRSBA designation may be bracketed if the one or more characters correspond to a repeat motif of the STR. Each of the characters in the first attribute-value pair of the HRSBA designation may be an International Union of Pure and Applied Chemistry (IUPAC) nucleotide base code. The HRSBA designation may comprise a second attribute-value pair specifying a length of the STR.
The concepts described in the present disclosure are illustrated by way of example and not by way of limitation in the accompanying figures. For simplicity and clarity of illustration, elements illustrated in the figures are not necessarily drawn to scale. For example, the dimensions of some elements may be exaggerated relative to other elements for clarity. Further, where considered appropriate, reference labels have been repeated among the figures to indicate corresponding or analogous elements. The detailed description particularly refers to the accompanying figures in which:
While the concepts of the present disclosure are susceptible to various modifications and alternative forms, specific embodiments thereof have been shown by way of example in the figures and will be described herein in detail. It should be understood, however, that there is no intent to limit the concepts of the present disclosure to the particular forms disclosed, but on the contrary, the intention is to cover all modifications, equivalents, and alternatives consistent with the present disclosure and the appended claims.
References in the specification to “one embodiment,” “an embodiment,” “an illustrative embodiment,” etc., indicate that the embodiment described may include a particular feature, structure, or characteristic, but every embodiment may or may not necessarily include that particular feature, structure, or characteristic. Moreover, such phrases are not necessarily referring to the same embodiment. Further, when a particular feature, structure, or characteristic is described in connection with an embodiment, it is submitted that it is within the knowledge of one skilled in the art to effect such feature, structure, or characteristic in connection with other embodiments whether or not explicitly described.
The disclosed embodiments may be implemented, in some cases, in hardware, firmware, software, or any combination thereof. The disclosed embodiments may also be implemented as instructions carried by or stored on a transitory or non-transitory computer-readable storage medium, which may be read and executed by one or more processors. A computer-readable storage medium may be embodied as any storage device, mechanism, or other physical structure for storing or transmitting information in a form readable by a computing device (e.g., a volatile or non-volatile memory, a media disc, or other media device).
In the drawings, some structural or method features may be shown in specific arrangements and/or orderings. However, it should be appreciated that such specific arrangements and/or orderings may not be required. Rather, in some embodiments, such features may be arranged in a different manner and/or order than shown in the illustrative figures. Additionally, the inclusion of a structural or method feature in a particular figure is not meant to imply that such feature is required in all embodiments and, in some embodiments, may not be included or may be combined with other features.
The present disclosure relates to a new computer file format useful for storing, exchanging, and/or preserving evidence regarding nucleotide sequence data, including short tandem repeat (STR) profiling data, particularly when the data originates from a massively parallel sequencing (MPS) instrument or workflow. As used herein, the term “nucleotide sequence data” generally refers to symbolic representations of nucleotides arranged in sequential fashion. This new computer file format, which is illustratively referred to herein as the “FASTF” file format, accommodates the conventions and requirements of MPS-derived data, as well as the conventions and requirements of evidence preservation in forensic DNA analysis. For example, MPS-derived data typically conforms to the following conventions, among others: (1) use of text-based computer files for data exchange, (2) storage of the raw nucleotide sequence data, (3) storage of per-nucleotide quality scores in Phred format, (4) storage of sequencer-derived metadata, and (5) traceability of data to the level of individual reads. Forensic DNA analysis may require (1) recording of information necessary for evidence traceability, including case management metadata, (2) expression of STR alleles in a human-readable format, and (3) translation of allele lengths into a CODIS-compatible format. In at least some embodiments, the FASTF file format addresses each of the foregoing concerns.
The FASTF file format achieves compatibility with MPS workflows by incorporating certain aspects of the FASTQ file format that is output by many MPS instruments (and, thus, currently serves as a de facto industry standard). First, the FASTF file format uses a text-based format that allows human readability. Second, the FASTF file format uses a repeating four-row record convention. Unlike FASTQ, however, the FASTF file format supports the evidentiary requirements inherent to forensic DNA analysis through the addition of certain metadata content.
In some illustrative embodiments, a FASTF file may conform to the file format specification outlined in Table 1 and further described below. Table 1 sets forth a FASTF file format specification with reference to the FASTQ file (the third column denoting several distinguishing features of FASTF files, as compared to FASTQ files). It will be appreciated that, in other embodiments, a FASTF file may be formatted according to additional or different specifications than those set forth in Table 1.
Similar to FASTQ files, a FASTF file may comprise one or more records, where each record includes data in four text lines. The first and third text lines of a record may contain metadata related to case management and to evidence preservation. The second text line contains nucleotide sequence data, while the fourth text line contains quality scores for the nucleotide calls represented by the second text line. In the illustrative embodiment, the quality scores of the fourth text line are formatted as Phred quality scores. FASTF files may contain either raw nucleotide sequence data or summary nucleotide sequence data and meta data (e.g., created by an allelotyping process). As described further below, an attribute-value pair within the first text line of each record may indicate whether a particular file contains raw or summary data and metadata.
The present disclosure also relates to methods of creating and/or processing FASTF files. For instance, a pair of algorithms (illustratively referred to in the present disclosure as “FASTF Creator Raw” or “F_CR” and as “FASTF Creator Summary” or “F_CS,” respectively) may be used to create FASTF files containing raw or summary data and metadata. The FASTF Creator Raw and FASTF Creator Summary algorithms are designed for use at different stages of the forensic DNA analysis process. In some embodiments, these two FASTF file creation algorithms may be complemented by a number of utility algorithms that convert FASTF files to other formats and otherwise manipulate FASTF files (illustratively referred to herein as “FASTFtools”).
Referring now to
The F_CR algorithm 104 may also ingest a case file 110 received from a laboratory case management system. In other embodiments of the method 100, the case management information may be manually input by a user of the MPS instrument via a user interface. Using these inputs, the F_CR algorithm 104 may write forensic metadata to each record of contained in the FASTQ file 106 a text-based computer file comprising the nucleotide sequence data. The output of the F_CR algorithm 104 is a FASTF raw-format file 102, one illustrative example of which is shown in
As mentioned above, the FASTF raw-format file 102 includes forensic metadata (e.g., derived from the case file 110) in a first text line of each record of the file 102. In some embodiments, the forensic metadata may be in the form of one or more attribute-value pairs 300. In the illustrative embodiment of
Referring now to
The F_CS algorithm 404 ingests a FASTF raw-format file 102 (e.g., generated by the F_CR algorithm 104) and the FASTF summary-format file 402. In order to perform this manipulation, the F_CS algorithm 404 may call a library 406 containing nucleotide sequences that may be used for locus identification and grooming operations. The nucleotide sequences stored in the library 406 may include 5′ primer sequences used to PCR amplify each STR locus, flanking sequences that are immediately 5′ and 3′ to each STR locus, and lists of canonical motifs found within each STR locus. The library 406 may be an updatable library 406, such that new information regarding the foregoing primer sequences, flanking sequences, and canonical motifs may be easily added and used by the F_CS algorithm 404. One illustrative example of a FASTF summary-format file 402 that may be output by the F_CS algorithm 404 is shown in
Generally, the F_CS algorithm 404 generates the FASTF summary-format file 402 from the FASTF raw-format file 102 (or from a FASTQ file 106 or other raw file 102) by identifying all records of the raw file 102 that contain identical nucleotide sequence data representing a unique STR and grouping these records together to create the records of the summary file 402. After receiving the FASTF raw-format file 102 (or other raw file 102), the F_CS algorithm 404 examines each record in the raw file 102 to determine whether a portion of the nucleotide sequence data (e.g. in the second text line of each record) represents an STR associated with a locus. In some embodiments, this determination may involve identifying whether a portion of the nucleotide sequence data of each record represents a 5′ primer sequence used to amplify the locus. As noted above, the F_CS algorithm 404 may reference the library 406, which includes the primer sequences for each locus, when making this determination.
For each record in which a portion of the nucleotide sequence data is determined to represent an STR associated with a locus, the F_CS algorithm 404 will then place that portion of the nucleotide sequence data representing the STR associated into a locus-specific list. This may involve trimming away portions of the nucleotide sequence data that do not represent the STR. In particular, in some embodiments, the F_CS algorithm 404 may remove portions of the nucleotide sequence data that represent a flanking sequence. Once again, the F_CS algorithm 404 may reference the library 406, which contains lists of flanking sequences, when trimming the nucleotide sequence data.
After the portions of nucleotide sequence data representing STRs are each placed into locus-specific lists, the F_CS algorithm 404 will determine a number of occurrences within each locus-specific list of identical nucleotide sequence data representing a unique STR. In some embodiments, the F_CS algorithm 404 may compare the characters of each portion of nucleotide sequence data to all other portions in the same locus-specific list to find identical matches. This exact-matching scheme will result in a read count (i.e., a number of occurrences) for each even slightly different STR. For instance, where nucleotide sequence data reflects a single nucleotide polymorphism (SNP), the F_CS algorithm 404 will count this nucleotide sequence data as its own unique STR (and will not group it with a similar STR not exhibiting the SNP). It will be appreciated that this approach is quite different from prior alignment-based systems, which attempts to identify which reference sequence each portion of nucleotide sequence data most resembles. Such prior alignment-based system would typically lump the nucleotide sequence data reflecting the SNP in with similar sequences.
After the number of occurrences of each unique STR within each locus-specific list is determined, the F_CS algorithm 404 will select which nucleotide sequence data to write to the summary file 402. This determination is made based on some abundance threshold, which may take a number of forms and may be user-configurable, in some embodiments. For instance, the F_CS algorithm 404 may sort the unique STRs may relative number of occurrences and select a certain number of those at the top of the list for inclusion in the summary file 402. The remaining data, which essentially represents noise, will be discarded from the summary file 402 (but might still be retrieved from the raw file 102, if needed). In other embodiments, the abundance threshold may be embodied as a certain percentage threshold. For instance, all unique STRs that make up over 10% of the total reads for their locus-specific list might be included in the summary file 402. For each unique STR that is to be included in the summary file 402, the F_CS algorithm 404 will create one record in the summary file 402.
The FASTF summary-format file 402 retains the human readability feature of all FASTF files. This human readability allows direct use by adjudicating parties, who need evidentiary data presented in a way that is easily read and understood by lay audiences without the need for special software and bioinformatics expertise. As illustrated in
The portion of the FASTF summary-format file 402 illustrated in
The HRSBA designation contained in the third text line of each record of the FASFT summary-format file 402 assists with the difficult task of reading repetitive DNA sequences and discerning the sometimes-subtle differences between them. HRSBA also provides a deterministic and non-arbitrary naming convention for sequence-defined alleles. Under current practice, STR alleles with identical nucleotide lengths but variant sequences are discriminated by denoting the allele length in International Society for Forensic Genetics (ISFG) X.Y format, followed by arbitrary notations such as letters, or prime or asterisk characters. These arbitrary notations fall short in two ways. First, they require the user to refer back to a lookup table for the actual sequence variations indicated by an asterisk or prime symbol. Second, without a complex and globally coordinated configuration management system, it is impossible to avoid inadvertent re-use of the same shorthand designations for different sequence variants by different laboratories. By contrast, the HRSBA designation disclosed herein has the advantage of determinism and clarity. By following a rule-based approach to generating HRSBA designations, every sequence variant will map to a unique HRSBA designation, allowing the underlying sequence to be easily derived from the HRSBA designation. The HRSBA designations may also be parsed by computer algorithms (e.g., FASTFtools utility algorithms) and rendered into alternative formats, including the original nucleotide sequence data.
In the illustrative embodiment, the HRSBA designation is reflected in two attribute-value pairs 500. First, the “hrsba” attribute-value pair 500 summarizes the STR sequence in the 5′ to 3′ direction, starting with the first nucleotide position in the STR sequence as nucleotide position 1. Nucleotide position numbers may be followed by the repeat motif bracketed (e.g., in square brackets) or by a single nucleotide code. It is contemplated that the HRSBA may use any type of punctuation to “bracket” a repeat motif. The nucleotide position refers to the position of the first nucleotide to the 3′ of the integer. In the case of a repeat motif, it refers to the first nucleotide of the motif. For example, a non-variant CSF1PO locus normally begins with the designation 1 [AGAT], where the 5′ nucleotide A occupies nucleotide position number 1. The remaining three nucleotides (i.e., GAT) of the motif occupy sequence positions 2, 3 and 4, respectively. In the case of nucleotides not contained in a repeat motif, the positions of each nucleotide are denoted. For example, the TH01 allele 8.3 sequence listed by the National Institute of Standards and Technology (NIST) is as follows:
In the illustrative embodiment, the HRSBA designation reflecting this sequence would be:
This HRSBA designation indicates that the 5′ nucleotide A in the AATG repeat is the first nucleotide in the sequence, while the nucleotide A in the non-repeat motif ATG is in sequence position 21. Furthermore, the 5′ nucleotide A in the second repeat motif is in sequence position 24, and the total length of the sequence is 35, as indicated by the “length” attribute-value pair 500. Thus, the 3′ nucleotide G in the sequence is in sequence position 35. It will be appreciated from the foregoing that the HRSBA designation is unambiguous and deterministic for any sequence variant that might be encountered in actual sequencing. Any mutation can be accommodated, including complex ones such as segment inversions. Unlike the alignment-based methods typically used in current allelotyping processes, the HRSBA designation in not dependent upon reference sequences. Rather, it is reference independent and will not be affected by possible future changes to reference sequences.
As noted above, the value of the “length” attribute-value pair 500 denotes the total length of nucleotides comprising the STR sequence. This attribute-value pair 500 serves two purposes. First, it denotes the end of the STR sequence and therefore defines the number of repeats of the ending canonical motif of the sequence. Second, the length attribute-value pair 500 quickly identifies the allele length in nucleotides by visual inspection. Combined with the “locus” attribute-value pair, this information may be used to derive the ISFG allele designation, although this information is also encoded by the value of the “stack” attribute-value pair 500 in cases where the stack corresponds to an allele.
In the illustrative embodiment, the HRSBA designation described above is generated using a motif aware allellotyper (MAA) algorithm, which may form part of the F_CS algorithm 404. Pseudo-code for the MAA algorithm is presented in
The illustrative embodiment of the MAA algorithm uses the ISFG convention that the initial repeat sequence motif be set equal to the first 5′ nucleotides that can define a repeat motif. Using this information, the deterministic HRSBA designation can be generated for each allele regardless of sequence. The MAA algorithm initializes on the occurrence of a canonical opening motif (see Table 4 below) immediately following the unique 5′ flanking sequence. In this embodiment, mutations in either of the 5′ flanking region or the initial repeat motif will result in allele dropout but not in an aberrant allele call, due to the deterministic property of the algorithm. In some embodiments, users may have the option of manually examining reads corresponding to loci exhibiting allele dropout.
Once initialized, the MAA algorithm utilizes a motif-aware 5′ to 3′ sliding window to parse the nucleotide sequence data of each allele. The sliding window follows two movement rules. First, if a canonical motif is present in the sliding window, a bracketing function is implemented for the canonical motif and then the sliding window moves four nucleotides to the right (3′). For the first instance of a canonical motif, the bracketing function adds the nucleotide bases in the sliding window to the HRSBA. In subsequent instances of the same canonical motif that is immediately adjacent to a prior instance, the bracketing function merely increments the nucleotide position to the immediate 3′ side of the canonical motif. Second, if a canonical motif is not found in the sliding window, the first nucleotide base in the window is pushed into a register and then the sliding window moves one position. This one-position movement of the four-nucleotide sliding window continues until the four-nucleotide sliding window encounters a canonical motif for that locus. When that occurs, the MAA algorithm again proceeds in four-position increments.
This movement of the sliding window continues until the final nucleotide of the STR allele sequence is encountered. The MAA algorithm performs a quality check to be sure the sequence ends in a canonical ending motif, generating a user alert otherwise. While the foregoing description uses the illustrative example of a four-nucleotide sliding window (to target tetramers), it is contemplated that the sliding window may be of any size. By way of example, the MAA algorithm may utilize a five-nucleotide sliding window to target pentameric repeats, such as those found in the pentaD and pentaE loci. In some embodiments, the MAA algorithm may switch to using a five-nucleotide sliding window when one of these loci is detected based on the uniquely-identifying 5′ primer sequence.
During operation, the MAA algorithm may call the library 406 described above to reference the canonical motifs for a particular locus.
Reference to the external library 406 allows routine updating of information about forensic STR loci, without changes to the underlying software code implementing the MAA algorithm. The discovery of previously unknown allele sequences may be anticipated as the number of individuals sequenced increases. Some of these sequences may be observed frequently enough to be included in the library 406 as common alleles. Similarly, the canonical motifs used by the MAA algorithm may be updated in the future if additional repeat motifs are discovered for a given locus. Thus, the updatable library 406 allows software implementing the MAA algorithm to remain relevant and accurate.
Referring now to
While certain illustrative embodiments have been described in detail in the figures and the foregoing description, such an illustration and description is to be considered as exemplary and not restrictive in character, it being understood that only illustrative embodiments have been shown and described and that all changes and modifications that come within the spirit of the disclosure are desired to be protected. There are a plurality of advantages of the present disclosure arising from the various features of the apparatus, systems, and methods described herein. It will be noted that alternative embodiments of the apparatus, systems, and methods of the present disclosure may not include all of the features described yet still benefit from at least some of the advantages of such features. Those of ordinary skill in the art may readily devise their own implementations of the apparatus, systems, and methods that incorporate one or more of the features of the present disclosure.
