Methods and systems for digitally counting features on arrays

Abstract

Methods, systems and platforms for digital imaging of multiple regions of an array, and detection and counting of the labeled features thereon, are described.

Description

SEQUENCE LISTING

The instant application contains a Sequence Listing which has been submitted electronically in ASCII format and is hereby incorporated by reference in its entirety. Said ASCII copy, created on Nov. 5, 2014, is named 41977-711.201_SL.txt and is 255,804 bytes in size.

BACKGROUND OF THE INVENTION

Array technologies have been widely used in biomedical studies for the detection of biomolecules and profiling of gene expression levels, etc. Arrays are typically comprised of immobilized probes which can bind to or hybridize with target molecules in a sample. Detection of binding or hybridization events is often achieved through the use of optical labels (e.g. fluorophores) and scanning or imaging techniques (e.g. fluorescence scanning or imaging). A feature on an array is a small region of immobilized probes that are specific for a given target molecule, e.g. probes that hybridize to specific DNA or RNA sequences. Identifying the pattern of labeled features on a hybridized array thus provides information about specific molecules, e.g. DNA or RNA molecules in the sample, which in turn can provide valuable data in biomedical studies. Two important engineering requirements for providing high quality, quantitative data for biomedical investigations are (i) to correctly image the hybridized arrays, and (ii) to correctly analyze the images to extract quantitative data. Existing optical imaging systems typically image one region of an array at a time, which can be a slow process if a number of different regions need to be imaged. In addition, current methods of image analysis typically determine a signal intensity level (i.e. an analog quantity) for each array feature. Intensity level measurements are often subject to a variety of instrumental drift and analysis errors, therefore improved methods for determining whether or not target molecules are bound to a given array feature, and improved methods for transforming that data into quantitative measures of the number of target molecules present in a sample, are of great importance to expanding the use of array technologies in biomedical applications.

SUMMARY OF THE INVENTION

The methods, systems, and platforms of the present disclosure provide means for digital counting of labeled features on arrays, and thereby enable quantitative determination of the number of target molecules present in a sample through the use of stochastic labeling techniques.

In some embodiments, an array reader system comprising an output unit for calculating an absolute number of target molecules in a sample is described, wherein the array reader system is configured to read an array comprising a plurality of labeled and non-labeled features. In some embodiments, the array reader system may further comprise an optical imaging system. In some embodiments, the calculation of absolute number of target molecules in a sample is based on transforming optical image data produced by the optical imaging system into a count of the number of labeled and non-labeled features on an array. In some embodiments, the output unit comprises a digital processor and executable software, wherein the executable software comprises computer code for transforming optical image data into a count of the number of labeled and non-labeled features. In some embodiments, the array comprises a microarray, microscope slide, or microwell plate.

In some embodiments of the disclosed array reader system, the optical imaging system has a magnification of less than 1, equal to 1, or greater than 1. In some embodiments, the optical imaging system comprises a fluorescence imaging system. In some embodiments, the optical imaging system comprises a phosphorescence imaging system. In some embodiments, the optical imaging system comprises an imaging system that operates in a transmitted light, reflected light, or scattered light imaging mode, or combinations thereof. In some embodiments, the optical imaging system comprises one or more image sensors, wherein the one or more image sensors have a resolution of at least 320×240 pixels. In some embodiments, the one or more image sensors are CCD image sensors, while in some embodiments, the one or more image sensors are CMOS image sensors. In some embodiments, the one or more image sensors comprise one or more circuit boards. In some embodiments, the optical imaging system further comprises one or more components selected from the group including, but not limited to, a microscope objective, a camera lens, a finite-conjugate lens, an infinite-conjugate lens, a plano-convex lens, a double convex lens, a plano-concave lens, a double concave lens, an achromatic cemented doublet, or a bandpass filter. In some embodiments, the optical imaging system comprises a fluorescence imaging system that is designed for use with fluorescein, Cy3, Cy5, or phycoerythrin fluorophores. In some embodiments, the optical imaging system further comprises an illumination system including at least one light source, wherein the at least one light source is an LED or LED assembly. In some embodiments, the at least one light source is electronically synchronized with the image sensor, the at least one light source being turned on when the image sensor is acquiring an image and turned off when the image sensor is not acquiring an image.

In some embodiments of the disclosed array reader system, the illumination system is an off-axis illumination system that satisfies the Scheimpflug condition. In some embodiments, the illumination system is an off-axis illumination system does not satisfy the Scheimpflug condition. In some embodiments, the illumination system is an off-axis illumination subsystem comprising a Kohler illumination system. In some embodiments, the illumination system is an off-axis illumination system comprising an Abbe illumination system. In some embodiments, the illumination system is an epi-illumination system comprising a Kohler illumination system. In some embodiments, the illumination system is an epi-illumination system comprising an Abbe illumination system. In some embodiments, the illumination system is a trans-illumination system comprising a Kohler illumination system. In some embodiments, the illumination system is a trans-illumination system comprising an Abbe illumination system.

In some embodiments of the disclosed array reader system, the optical imaging system further comprises a translation stage, wherein the translation stage is a single-axis translation stage, a dual-axis translation stage, or a multi-axis translation stage.

In some embodiments of the disclosed array reader system, the optical imaging system and output unit are combined within a single, stand-alone instrument. In some embodiments, the optical imaging system and output unit are configured as separate instrument modules.

In some embodiments of the disclosed array reader system, the executable software automatically locates features of the array within the acquired image. In some embodiments, the executable software also performs local background correction by (i) centering a predefined analysis window on each array feature within an image, (ii) calculating an intensity value statistic for signal and background pixels according to a predefined pattern of pixels within the feature, and (iii) utilizing the signal and background intensity value statistics to calculate a background corrected signal intensity value for each feature.

In some embodiments of the disclosed array reader system, the executable software performs a k-means clustering analysis of the background corrected signal intensity values for the complete set of array features, thereby determining a dynamic signal intensity threshold for discrimination between labeled and non-labeled features of the array. In some embodiments, the executable software also performs a k-medoids clustering analysis of the background corrected signal intensity values for the complete set of array features, thereby determining a dynamic signal intensity threshold for discrimination between labeled and non-labeled features of the array. In some embodiments, the executable software also performs a mixture model statistical analysis of the background corrected signal intensity values for the complete set of array features, thereby determining a dynamic signal intensity threshold for discrimination between labeled and non-labeled features of the array. In some embodiments, the executable software also performs an empirical analysis based on sorting of background corrected signal intensity values for the complete set of array features, thereby determining a dynamic signal intensity threshold for discrimination between labeled and non-labeled features of the array. In some embodiments, the executable software also performs an empirical analysis based on sorting of pairwise differences in background corrected signal intensity values for the complete set of array features, thereby determining a dynamic signal intensity threshold for discrimination between labeled and non-labeled features of the array. In some embodiments, the executable software module also performs one or more statistical analyses of the background corrected signal intensity values for the complete set of array features, thereby determining a dynamic signal intensity threshold for discrimination between labeled and non-labeled features of the array, and wherein the one or more statistical analyses are selected from the list including, but not limited to, k-means clustering, k-medoids clustering, mixture model statistical analysis, or an empirical analysis.

In some embodiments of the disclosed array reader system, the executable software calculates the absolute number of target molecules in a sample based on the number of labeled and non-labeled features detected and the predictions of the Poisson distribution. In some embodiments, the executable software also calculates a confidence interval for the number of target molecules.

Also disclosed herein is a digital imaging platform comprising: (a) an optical instrument configured to generate an image of one or more regions of an array, wherein the array comprises a plurality of features comprising oligonucleotide probes, and wherein the oligonucleotide probes are complementary to a set of labels; and (b) a digital processor, wherein the digital processor is configured to perform image analysis comprising: (i) transforming background corrected signal intensities for a plurality of features to produce binary output data that determines the number of labeled and non-labeled features in the one or more regions of the array; and (ii) calculating a number of target molecules present in a sample based on the number of labeled and non-labeled features detected within the one or more regions of the array. In some embodiments, the image analysis further comprises automatically locating the features of the array within the image. In some embodiments, the image analysis further comprises correcting a signal intensity for each feature for a local background intensity. In some embodiments, the image analysis further comprises performing one or more statistical analyses of the corrected signal intensities for a plurality of features to define one or more dynamic signal intensity thresholds for the one or more regions of the array, where the statistical analyses are selected from the list including, but not limited to, k-means clustering, k-medoids clustering, mixture model statistical analysis, or an empirical analysis. In some embodiments, the calculation of the number of target molecules present in a sample is based on both the number of labeled and non-labeled features detected within the one or more regions of the array and on the predictions of the Poisson distribution.

Disclosed herein is an imaging platform comprising: (a) an optical instrument configured to generate an image of one or more regions of an array, wherein the array comprises a plurality of features, and wherein the plurality of features comprise a set of oligonucleotide probes, and wherein the oligonucleotide probes are complementary to a set of labels; and (b) a processor configured to perform image analysis, wherein the image analysis comprises: (i) reading the image generated by the optical instrument; (ii) locating the features of the array within the image; (iii) measuring a signal intensity for each feature; (iv) measuring a local background intensity for each feature; (v) calculating a local background corrected signal intensity for each feature using the signal intensity and local background intensities; (vi) analyzing the local background corrected signal intensities for the complete set of features to determine a dynamic signal intensity threshold for discriminating between labeled and non-labeled features; and (vii) calculating a number of target molecules present in a sample based on the number of labeled and non-labeled features detected and the predictions of the Poisson distribution. In some embodiments, the image generated by the optical instrument is a fluorescence image. In some embodiments, the image generated by the optical instrument is a phosphorescence image. In some embodiments, the image generated by the optical instrument is a transmitted light, reflected light, or scattered light image. In some embodiments, the image analysis further comprises reading an image that has been previously acquired and stored in a memory device. In some embodiments, locating the features of the array within the image comprises identifying predefined fiducial features on the array. In some embodiments, the calculation of a local background corrected signal intensity is performed by (i) centering a predefined analysis window on each feature within the image, (ii) calculating an intensity value statistic for signal and background pixels according to a predefined pattern of pixels within the feature, and (iii) utilizing the signal and background intensity value statistics to calculate a local background corrected signal intensity for each feature. In some embodiments, the intensity value statistic used for calculating a local background corrected signal intensity for each feature is selected from the list including, but not limited to, the mean, the median, or the ratio of signal to background intensities. In some embodiments, the analyzing of local background corrected signal intensities for the complete set of features to determine a dynamic signal intensity threshold comprises performing one or more statistical analyses selected from the list including, but not limited to, k-means clustering, k-medoids clustering, mixture model statistical analysis, or an empirical analysis. In some embodiments, the analyzing of local background corrected signal intensities for the complete set of features to determine a dynamic signal intensity threshold comprises fitting a model function to the intensity data by varying model parameters. In some embodiments, the analyzing of local background corrected signal intensities for the complete set of features to determine a dynamic signal intensity threshold comprises maximizing a quality metric relating to a statistical difference between intensities above the threshold and below the threshold.

Also disclosed herein is a non-transitory computer readable medium storing a program that calculates a number of labeled features on an array, wherein the array comprises a plurality of feature sets, and wherein individual features of a feature set comprise a set of oligonucleotide probes that are capable of hybridizing to a set of labels, the non-transitory computer readable medium comprising: (a) computer code that locates individual features of the array within a digital image of the array; (b) computer code that performs a local background correction of a signal intensity for each feature; (c) computer code that analyzes the corrected signal intensity data for the complete set of features and determines a corrected signal intensity threshold; and (d) computer code that transforms the corrected signal intensity for each feature into binary output data, thereby providing a count of the number of labeled features on the array. In some embodiments, the computer code for locating individual features of the array within the digital image comprises identifying predefined fiducial features on the array. In some embodiments, the computer code for performing a local background correction of signal intensity for each feature comprises a calculation utilizing a statistic for signal and background intensities selected from the list including, but not limited to, the mean, the median, or the ratio of signal to background intensities. In some embodiments, the computer code for analyzing corrected signal intensities for the complete set of features to determine a corrected signal intensity threshold comprises performing one or more statistical analyses selected from the list including, but not limited to, k-means clustering, k-medoids clustering, mixture model statistical analysis, or an empirical analysis.

Also disclosed herein is a computer implemented method for performing local background correction of array signal intensity data, the method comprising: (a) centering a predefined data analysis window on a feature within a digital image of the array; (b) calculating an intensity value statistic for signal and background pixels according to a predefined pattern of pixels within or around the array feature; and (c) utilizing the signal and background intensity value statistics to calculate a background corrected signal intensity for the array feature. In some embodiments, the computer implemented method further comprises automatically locating the array feature using a predefined set of fiducial features on the array. In some embodiments, the intensity value statistic used for calculation of a background corrected signal intensity is selected from the list including, but not limited to, the mean, the median, or the ratio of signal to background intensities.

Disclosed herein is a computer implemented method for determining a dynamic image intensity threshold for use in discriminating between labeled and non-labeled features on an array comprising a plurality of labeled and non-labeled features, the computer implemented method comprising: (a) measuring image intensity data for each feature of the array; (b) performing a local background correction on the image intensity data for each feature on the array; and (c) performing one or more statistical analyses of the background corrected image intensity data for the complete set of array features, thereby determining a dynamic image intensity threshold for discrimination between labeled and non-labeled features of the array, and wherein the one or more statistical analyses are selected from the list including, but not limited to, k-means clustering, k-medoids clustering, mixture model statistical analysis, or an empirical analysis.

Also disclosed is a mechanism comprising: (a) a closure; (b) a housing which magnetically holds the closure in a first position; and (c) a translation stage which magnetically holds the closure in a second position. In some embodiments, the mechanism further comprising a gasket positioned between the closure and the housing. In some embodiments, the gasket is attached to the closure. In some embodiments, the gasket is attached to the housing. In some embodiments, the closure and housing are substantially opaque, and the gasket creates a substantially light-tight seal between the closure and the housing in the first position. In some embodiments, one or more magnets are positioned to hold the closure onto the housing in the first position. In some embodiments, one or more magnets are positioned to hold the closure onto a first surface of the translation stage in the second position. In some embodiments, two or more pairs of mating locating features to align the closure with the translation stage in the second position. In some embodiments, two or more pairs of mating locating features to align the closure with the housing in the first position. In some embodiments, the pairs of mating locating features comprise conical pins and conical holes. In some embodiments, the housing comprises an optical instrument. In some embodiments, the translation stage includes a sample holder. In some embodiments, the sample holder is designed to hold a microscope slide, a microarray, or a microwell plate. In some embodiments, the closure is not hinged. In some embodiments, the closure is not attached to either the housing or the translation stage through the use of fasteners such as screws or clips. In some embodiments, the closure is not attached to either the housing or the translation stage through the use of an adhesive.

BRIEF DESCRIPTION OF THE DRAWINGS

The novel features of the invention are set forth with particularity in the appended claims. A better understanding of the features and advantages of the present invention will be obtained by reference to the following detailed description that sets forth illustrative embodiments, in which the principles of the invention are utilized, and the accompanying drawings of which:

FIGS. 1A-1G show one example of an optical system, and components thereof. FIG. 1A depicts an isometric projection of the exemplary optical system. FIG. 1B depicts a top view of the optical system. FIG. 1C depicts a dimetric view of the optical system. FIG. 1D depicts a front view of the optical system. FIG. 1E depicts a side view of the exemplary optical system comprising a single axis stage, an imaging system, and an illumination system. FIG. 1F depicts a back view of the optical system. FIG. 1G depicts components that control the operation of the optical system.

FIG. 2 shows an exemplary layout of lenses in an imaging system.

FIG. 3 shows an exemplary layout of lenses in an illumination system.

FIG. 4A shows an array image acquired from the optical instrument. FIG. 4B shows a histogram of intensities for individual features.

FIG. 5 shows a feature intensity distribution observed for hybridization of array probes with labeled target molecules in atitration experiment.

FIG. 6A shows a noisy array image. FIG. 6B shows the intensity distribution before background adjustment. FIG. 6C shows the intensity distribution after background adjustment.

FIGS. 7A-7D show external views of instrument designed for digital counting of features on arrays. FIG. 7A is an axonometric view of the instrument as viewed from the upper right. FIG. 7B is an axonometric view of the instrument as viewed from the right-front. FIG. 7C is a front view of the instrument. FIG. 7D is an axonometric view of the instrument as viewed from the upper left.

FIG. 8 shows an internal view (front view; 3D CAD model) of an instrument designed for digital counting of features on arrays.

FIG. 9 depicts an internal view (rear view; 3D CAD model) of an instrument designed for digital counting of features on arrays.

FIG. 10 shows a photograph of a system with the sample loading stage in the extended (loading) position, having pulled the door away from the front panel. A Pixel 16 array assembly is shown in the loading tray.

FIG. 11A shows an exploded view of a door assembly that utilizes a magnetic mechanism for positioning a door on a sample compartment stage. FIG. 11B shows another exploded view of the door assembly that illustrates conical locator features for ensuring proper alignment of the door with the stage.

FIG. 12 depicts an exploded view of an upper stage assembly with magnets which mate with a corresponding pair of magnets on the door.

FIG. 13 shows an exploded view of a front panel assembly with magnets which mate with a corresponding pair of magnets on the door.

FIG. 14 shows the viewing reference orientation for array production and analysis in one embodiment of an array, showing the 16 array locations on a glass substrate. Nominal dimensions are shown (in millimeters).

FIG. 15 shows the layout of features on one embodiment of an array. Nominal dimensions are shown (in millimeters).

FIG. 16 shows the layout of an array designed for digital counting of target molecules in a sample, including the positions of positive controls (fiducials), negative controls, and index spots.

FIG. 17A shows an example of an array image after transformation to the reference orientation. FIG. 17B shows the image size (in pixels) and a schematic of feature positions for the two-array image.

FIGS. 18A-B depict software workflows for performing an experiment on an instrument designed for digital counting of features on arrays. FIG. 18A: workflow for a single-axis system with manual sample loading. FIG. 18B: workflow for a dual-axis system with automatic sample tray loading.

FIG. 19 depicts an analysis window comprising a 12×12 pixel area associated with each feature in the array.

FIG. 20 depicts a map of the pixel designations within the analysis window for each feature in the array.

FIG. 21 depicts a scatter plot (upper) of intensity data obtained from an image of an array that illustrates the different categories of features identified by the analysis software, and a histogram (lower) of the feature intensity data. Dashed lines indicate examples of intensity thresholds determined by the software that are used to discriminate between labeled (“on”) and non-labeled (“off”) features.

FIG. 22 depicts a scatter plot (upper) and histogram (middle) of array feature intensity data that illustrate the use of an intensity threshold (dashed lines) that discriminate between labeled (“on”) and non-labeled (“off”) features of an array. In one embodiment of the presently described analysis methods, the threshold is determined from the maximum slope of a plot of sorted intensity data (lower).

FIG. 23 depicts the results of fitting a 3-component distribution model used to determine an intensity threshold in one embodiment to a 128-bin feature intensity histogram.

FIG. 24A illustrates deviance calculations for fitting one normal distribution to histograms of array feature intensity data. FIG. 24B illustrates the deviance calculations for fitting two normal distributions to histograms of array feature intensity data. In some embodiments, deviance measurement may be used as a quality metric.

FIG. 25 depicts the uncertainties calculated for various methods of combining output data from replicate experiments.

FIG. 26 shows dilution series data for using digital counting of labeled features on an array to measure the number of target RNA molecules in a sample.

FIG. 27 shows a screenshot of the output data provided by the system software for a dilution series experiment. For each array used in the dilution series experiment, the software displays a histogram of feature intensity data with a blue line indicating the value of the threshold used for counting, overlaid on a digital representation of the array.

DETAILED DESCRIPTION OF THE INVENTION

Array technologies have been widely used in biomedical studies for the detection of biomolecules and profiling of gene expression levels, etc. Arrays are typically comprised of immobilized probes which can bind to or hybridize with target molecules in a sample. Detection of binding or hybridization events is often achieved through the use of optical labels (e.g. fluorophores) and scanning or imaging techniques (e.g. fluorescence scanning or imaging). A feature on an array is a small region of immobilized probes that are specific for a given target molecule, e.g. probes that hybridize to specific DNA or RNA sequences. Identifying the pattern of labeled features on a hybridized array thus provides information about the presence of specific molecules, e.g. DNA or RNA molecules in the sample, which in turn can provide valuable data in biomedical studies. Two important engineering requirements for providing high quality, quantitative data for biomedical investigations are (i) to correctly image the hybridized arrays, and (ii) to correctly analyze the images to extract quantitative data. Existing optical imaging systems typically image one region of an array at a time, which can be a slow process if a number of different regions need to be imaged. In addition, current methods of image analysis typically determine an analog signal intensity level (i.e. a signal that can have any value between some minimum and maximum values that are determined by various instrumental and experimental parameters) for each array feature. Analog intensity level measurements are often subject to a variety of instrumental drift and analysis errors, therefore improved methods for determining whether or not target molecules are bound to a given array feature, and improved methods for transforming that data into quantitative measures of the number of target molecules present in a sample, are of great importance to expanding the use of array technologies in biomedical applications.

The advantages of the methods, systems, and platforms disclosed herein include: (i) simultaneous imaging of multiple regions of an array for higher throughput image acquisition, and (ii) improved methods for reduction of image data to a digital determination of the presence or absence of bound target molecules (or target molecule labels) for each feature of an array, thereby providing for improved quantitation in some types of array experiments, for example, those utilizing a set of stochastic labels for quantifying the number of target molecules present in a sample. The use of stochastic labeling techniques is described in U.S. Pat. No. 8,835,358 and PCT application US2011/065291, which are incorporated in their entirety herein by reference. In addition to providing a means for more quantitative detection of target molecules, the use of stochastic labeling techniques allows for mitigation of amplification bias in assays involving nucleic acid amplification.

Accordingly, disclosed herein are methods, devices, systems, and platforms for digital counting of labeled features on arrays comprising: (i) optical instruments configured to form images of one or more regions of an array, (ii) arrays comprising a plurality of features further comprising a plurality of probes, and wherein one or more regions of an array may comprise one or more sub-arrays, and wherein the arrays or sub-arrays are designed for use with sets of stochastic labels, and (iii) computer implemented methods for receiving input image data; locating array features within array images; correcting the signal intensity values associated with each feature for local background intensity values; determining dynamic signal intensity thresholds for the one or more array regions by performing statistical analyses of the corrected signal intensity data for a plurality of features; counting the number of labeled and non-labeled features on the one or more regions of the array by comparing corrected signal intensity data for the features to signal intensity thresholds; and calculating the number of target molecules in a sample, for one or more target molecule species, from the number of labeled and non-labeled features detected on the one or more regions of the array.

In some embodiments, systems are described which comprise: (i) an optical instrument (or reader) configured to form images of one or more regions of an array, (ii) a digital processor configured to perform executable instructions and store data in memory devices, and (iii) computer code for performing image analysis in order to transform image data into a digital count of the number of labeled and non-labeled features on the one or more regions of the array. In some embodiments, the computer code further comprises performing a calculation of the number of target molecules in a sample, for one or more target molecule species, from the number of labeled and non-labeled features detected on the one or more regions of the array.

In some embodiments, platforms are described which comprise: (i) arrays designed for use in stochastic labeling experiments, wherein the arrays comprise a plurality of features further comprising a plurality of probes, and wherein one or more regions of an array may comprise one or more sub-arrays, and wherein the arrays or sub-arrays are designed for use with sets of stochastic labels, (ii) an optical instrument (or reader) configured to form images of one or more regions of an array, (iii) a digital processor configured to perform executable instructions and store data in memory devices, and (iv) computer code for performing image analysis in order to transform image data into a digital count of the number of labeled and non-labeled features on the one or more regions of the array. In some embodiments, the computer code further comprises performing a calculation of the number of target molecules in a sample, for one or more target molecule species, from the number of labeled and non-labeled features detected on the one or more regions of the array.

In some embodiments, software applications (or computer code products) are described that determine the number of labeled features on an array, wherein the software application includes code for performing one or more of the following computer implemented methods: (i) receiving input image data, (ii) locating array features within array images, (iii) correcting the signal intensity values associated with each feature for local background intensity values, (iv) determining dynamic signal intensity thresholds for the one or more array regions by performing statistical analyses of the corrected signal intensity data for a plurality of features, (v) counting the number of labeled and non-labeled features on the one or more regions of the array by comparing corrected signal intensity data for the features to signal intensity thresholds, and (vi) calculating the number of target molecules in a sample, for one or more target molecule species, from the number of labeled and non-labeled features detected on the one or more regions of the array.

In some embodiments, computer implemented methods are described for performing local background correction of array signal intensity data, the methods comprising: (i) centering a predefined data analysis window on each array feature within a digital image of the array, (ii) calculating mean or median intensity values for signal and background pixels according to a predefined pattern of pixels within or around each array feature, and (iii) subtracting the mean or median background intensity from the mean or median signal intensity to determine a background corrected signal intensity value for each array feature.

In some embodiments, computer implemented methods are described for determining dynamic image intensity thresholds from the corrected image intensity data for a plurality of features on an array, the methods comprising: (i) collecting image intensity data for each feature of the array, (ii) optionally performing a local background correction on the image intensity data for each feature on the array; and (iii) performing one or more statistical analyses of the background corrected image intensity data for the complete set of array features, thereby determining a dynamic image intensity threshold for discrimination between labeled and non-labeled features of the array. In some embodiments, the one or more statistical analyses are selected from the list including, but not limited to, k-means clustering, k-medoids clustering, mixture model statistical analysis, or empirical analyses based on sorting of image intensity values or pairwise differences in image intensity values. As used herein, the term “dynamic intensity threshold” refers to a parameter that is determined based on an analysis of data derived from the experiment in progress. The use of a dynamic image intensity threshold for discrimination between labeled and non-labeled features of an array helps to minimize or eliminate errors in data processing that may arise from instrumental drift or experimental procedure.

DEFINITIONS

Unless otherwise defined, all technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art in the field to which this disclosure belongs. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” include plural references unless the context clearly dictates otherwise. Any reference to “or” herein is intended to encompass “and/or” unless otherwise stated.

As used herein, the terms “system” and “platform” are used interchangeably. Similarly, the terms “image sensor”, “imaging sensor”, “sensor chip”, and “camera” are used interchangeably to describe two dimensional photosensors used for imaging purposes, and the use of the terms “image intensity” and “signal intensity” are also used interchangeably in describing data analysis methods. Finally, unless otherwise stated, the terms “software”, “software application”, “software module”, “computer program”, and “computer code” are also used interchangeably.

Stochastic Labeling Methods

The use of stochastic labeling techniques is described in U.S. Pat. No. 8,835,358 and PCT application US2011/065291, which are incorporated in their entirety herein by reference.

Briefly, high-sensitivity single molecule digital counting may be achieved through the stochastic labeling of a collection of identical target molecules. Each copy of a target molecule is randomly labeled using a large, non-depleting reservoir of unique labels. The uniqueness of each labeled target molecule is determined by the statistics of random choice, and depends on the number of copies of identical target molecules in the collection compared to the diversity of labels. The size of the resulting set of labeled target molecules is determined by the stochastic nature of the labeling process, and analysis of the number of labels detected then allows calculation of the number of target molecules present in the original collection or sample. When the ratio of the number of copies of a target molecule present to the number of unique labels is low, the labeled target molecules are highly unique (i.e. there is a very low probability that more than one target molecule will have been labeled with a given label), and the digital counting efficiency is high. This stochastic metholodology transforms the problem of counting molecules from one of locating and identifying identical molecules to a series of yes/no digital questions regarding detection of a set of predefined labels. In some embodiments, the labeled products are detected by means of DNA sequencing. In other embodiments, the labeled products for one or more target molecules of choice are detected with high specificity using the array readout systems described herein.

Arrays and Features

Disclosed herein are arrays designed for use in stochastic counting of one or more target molecules in a sample. Arrays provide a means of detecting the presence of labeled target molecules, wherein the labels comprise a large and diverse set of unique labels.

In many embodiments, arrays comprise a plurality of features (or spots) on the surface of a substrate, wherein each feature further comprises a plurality of attached probes. In some embodiments, the array may comprise one or more regions, each of which may comprise a plurality of features or sub-arrays. For example, an array may comprise 2, 3, 4, 5, 6, 7, 8, 9, 10 or more regions, or alternatively, an array may comprise 15, 20, 25, 30, 35, 40, 45, 50 or more regions. In some embodiments, an array may comprise 60, 70, 80, 90, 100 or more regions. In other embodiments, an array may comprise hundreds, thousands, or tens of thousands of regions.

Non-limiting examples of arrays include microtiter plates, microwell plates, 16-well microscope slides, spotted microarrays, or microarrays fabricated by in situ solid-phase synthesis. A region of an array may comprise one well of a 16-well microscope slide, one well of a glass-bottomed 96-well plate, or one well of a glass-bottomed 384-well plate. Alternatively, a region of an array may comprise more than one well, for example, in some embodiments, a region may comprise 2 adjacent wells, 4 adjacent wells; or a larger number of wells positioned in close proximity to each other. In some embodiments, the arrays may comprise high-density oligonucleotide arrays with more than 1,000 features per square millimeter, and a region on the array may comprise a selected area of the array substrate surface, for example, an area of approximately 1 mm×1 mm.

As indicated previously, in many embodiments, the set of probes attached to a set of features of an array are selected for detection of a specific set of unique labels designed for use in stochastic labeling studies. The attachment of the probes to the array substrate may be covalent or non-covalent, and permanent or temporary. A probe may be a sequence of monomers including, but not limited to, for example, deoxy-ribonucleotides, ribonucleotides, amino acids, or synthetic monomers, or they may be a sequence of oligomers, including, but not limited to, for example, oligonucleotides (e.g. DNA or RNA sequences) or peptide sequences. In some cases, a probe may be a macromolecule, including but not limited to, for example, antibodies or antibody fragments. Each feature on an array corresponds to a small area of the array substrate comprising immobilized probes having the same molecular sequence that bind to or hybridize with the same target molecule. Two or more features on the array may be identical, similar, or different. In many embodiments, arrays will include one or more fiducial marks used for alignment or orientation purposes, as well as positive and negative control features in addition to feature sets used for detection of a stochastic label set. Positive control features may comprise probes that bind to or hybridize with molecules known to be always present in a sample, or probes that bind to or hybridize with molecules spiked into a sample in a controlled fashion. Negative control features may comprise probes that are specific for molecules that are known to be absent from a sample, or they may comprise features having no probes attached to the substrate surface at all.

In many embodiments, the array substrate, also called a support, may be fabricated from a number of materials. The materials may be solid. The materials may be semi-solid. Examples of materials that may be used to fabricate array substrates include, but are not limited to, glass, fused silica, silicon, polymer, or paper.

In some embodiments, the present disclosure also describes arrays for use in stochastic labeling studies. In particular, arrays are described wherein the arrays comprise a plurality of features having immobilized probes thereon that are complementary to a set of labels designed for use in stochastic labeling experiments, and wherein there is at least one feature on the array for every label in the label set. Some embodiments include an array comprising: (a) a plurality of features, optionally organized into a plurality of sub-arrays, wherein the plurality of features comprise: (i) one or more fiducial features comprising oligonucleotide probes of a defined fiducial sequence; (ii) one or more positive control features comprising oligonucleotide probes of one or more defined positive control sequences; (iii) one or more negative control features having no oligonucleotide probes; and (iv) a plurality of label set features comprising oligonucleotide probes, wherein each individual feature comprises a unique sequence selected from a set of label sequences designed for stochastic labeling of one or more target molecules. In some embodiments, the arrays described in the present disclosure comprise oligonucleotide probe sequences comprising 25-mers, wherein the 5′ terminus may optionally be labeled with a 6 carbon atom amino-modifier. In some embodiments, the arrays described in the present disclosure further comprise oligonucleotide probes comprising the set of 960 unique oligonucleotide sequences listed in Table 1. In some embodiments, the arrays described in the present disclosure comprise a set of olignucleotide probes that are 70% homologous, 80% homologous, 85% homologous, 90% homologous, or 95% homologous with the set of sequences listed in Table 1. In some embodiments, the array described in the present disclosure comprise a set of oligonucleotide probes that includes 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, or 90% or more of the sequences listed in Table 1.

Hybridization and Detection

In many embodiments of the disclosed methods, systems, and platforms, samples may be processed prior to placing them in contact with the immobilized probes on arrays. For example, target molecules in the samples may be labeled with fluorescent dye molecules and/or stochastic labels during the sample preparation step. Prior to hybridization with oligonucleotide probes, for example, target DNA or RNA molecules may be covalently linked to fluorescent dye molecules including, but not limited to, fluorescein, Cy3, or Cy5. Alternatively, target molecules may be labeled after binding or hybridizing to probes on the array. For example, target molecules may be covalently linked to biotin prior to binding or hybridization with probes on the array. Following the binding or hybridization step, the immobilized target molecules may then be labeled with streptavidin conjugated to optical tags including, but not limited to, phycoerythrin, quantum dot nanoparticles, gold nanoparticles, or blue latex beads. There are many methods for labeling target molecules, either before or after binding or hybridization to the array, and many possible choices for suitable optical labels or tags.

Once a sample has been contacted with an array, the array (or one or more regions of the array) may comprise one or more labeled features. Each region of an array that has been contacted with a sample comprising labeled target molecules (where the target molecules are labeled either before or after contact with the array) may, for example, comprise zero, one, two, or more labeled features. Alternatively, a region of an array that has been contacted with a sample may comprise 2, 3, 4, 5, 6, 7, 8, 9, 10 or more labeled features. In some embodiments, a region of an array that has been contacted with a sample may comprise 15, 20, 25, 30, 35, 40, 45, 50, or more labeled features. In high-density arrays, a region of an array that has been contacted with a sample may comprise more than 100 labeled features, more than 1,000 labeled features, more than 10,000 labeled features, more 100,000 labeled features, or more than 1,000,000 labeled features.

Optical Instruments

The methods, systems, and platforms described herein may comprise an optical instrument used for finite-conjugate digital imaging of one or more regions of an array, wherein the instrument typically includes an illumination system, an imaging system, and a translation stage. In some embodiments, the instrument operates as a “macroscope” having a magnification of less than one. In other embodiments, the instrument operates as a “microscope” having a magnification of greater than one. In still other embodiments, the instrument operates as a “contact imager” having a magnification equal to one. The choice of magnification will typically depend on the field of view required to image the region of interest, and on the size of the image sensor.

By way of non-limiting example, if a region of an array comprises a single well of a 16-well microscope slide, or a single well of a glass-bottomed 96-well plate, the dimensions of the region to be imaged may be approximately 7 mm×7 mm, and the pitch (center-to-center distance between two adjacent regions of the array may be approximately 9 mm. In some embodiments, the optical instrument may be used to take an image of one well at a time, or an image of 2 adjacent wells simultaneously, or an image of 4 (2×2) adjacent wells simultaneously, and the required field of view, or region to be imaged, may be adjusted accordingly. Similarly, the optical instrument may form an image of 6 (3×2 or 2×3), 8 (4×2 or 2×4), 9 (3×3), 10 (5×2 or 5×2), or 12 (6×2, 4×3, 3×4, or 2×6) adjacent wells simultaneously.

By way of another non-limiting example, if a region of an array is a single well of a glass-bottomed 384-well plate, the dimensions of the region to be imaged may be approximately 3 mm×3 mm, and the pitch between two adjacent regions of the array may be approximately 4.5 mm. Again, in some embodiments, the optical instrument may be used to take an image of one well at a time, or an image of 2 adjacent wells simultaneously, or an image of 4 (2×2), 6 (3×2 or 2×3), 8 (4×2 or 2×4), 12 (4×3 or 3×4), or 16 (4×4) adjacent wells simultaneously.

In another non-limiting example, the optical instrument may be used to image high-density oligonucleotide arrays, for example arrays having more than 1,000 features per square millimeter, and a region on the array may be approximately 1 mm×1 mm in area, for example.

Imaging System

One main component of the optical instrument is an imaging system. The imaging system may include one or more lenses in addition to a CCD or CMOS camera. Typically the CCD or CMOS camera will have a resolution between a few hundred thousand and a few million pixels. A high resolution camera may have tens of millions of pixels, or more.

The imaging system may be configured to magnify the image of the array. The required magnification of the imaging system can be determined by the required field of view and by the size of the CCD or CMOS sensor. By way of a non-limiting example, if the optical instrument is used to take an image of 2 adjacent wells of a 16-well microscope slide simultaneously, the required field of view is approximately 16 mm×8 mm. If the light-sensitive area of the CCD or CMOS sensor is about 4.8 mm×3.6 mm, the instrument is a macroscope and a magnification of about 0.3 is required. In this case, only data from the central 4.8 mm×2.4 mm of the sensor would be used.

By way of non-limiting example, an appropriate imaging system with a magnification of 0.3 may be constructed using an achromatic cemented doublet lens with a focal length of 85 mm and an infinite-conjugate camera lens with a focal length of 25 mm. If a spectrally selective emission filter is used (for example, a single-band interference filter, multi-band interference filter, longpass interference filter, or longpass colored glass filter), and this filter is typically located between the achromatic cemented doublet lens and the camera lens. Additional configurations of an imaging system with a magnification of 0.3 are possible. For example, the achromatic cemented doublet lens can be omitted, and a finite-conjugate camera lens can be used instead of an infinite-conjugate camera lens. In this case, the spectrally selective emission filter is preferably located on the long-conjugate side of the camera lens.

A sensor with a light-sensitive area of 4.8 mm×3.6 mm is known as a ⅓-inch format sensor. If a sensor of different size is used, the required magnification will be different. By way of a non-limiting example, if the required field of view is 16 mm×8 mm and a sensor having a light-sensitive area of 6.4 mm×4.8 mm (known as a ½-inch format sensor) is used, then the required magnification is 0.4. An appropriate imaging system with a magnification of 0.4 can be constructed using, for example, an achromatic cemented doublet lens with a focal length of 85 mm and an infinite-conjugate camera lens with a focal length of 35 mm.

As another non-limiting example, if the dimensions of a region are about 0.66 mm×0.66 mm and a sensor with a light-sensitive area of 8.8 mm×6.6 mm (known as a ⅔-inch format sensor) is used, then the instrument is a microscope and the required magnification is about 10. In this case, only data from the central 6.6 mm×6.6 mm of the sensor will be used. An appropriate imaging system with a magnification of 10 can be constructed using, for example, an infinite-conjugate microscope objective with a focal length of 20 mm and a microscope tube lens with a focal length of 200 mm, with a spectrally selective emission filter typically located between the microscope objective and the tube lens. Alternatively a finite-conjugate 10× microscope objective can be used and the microscope tube lens can be omitted. In this case the spectrally selective emission filter can be located on the long-conjugate side of the microscope objective.

An imaging system of any required magnification can be constructed using a combination of off-the-shelf and custom optical elements that does not necessarily include either a camera lens or a microscope objective. The optical elements may have various combinations of spherical, flat, aspheric, or diffractive surfaces.

Illumination System

Another main component of the optical instrument is an illumination system. The purpose of the illumination system is to illuminate the array within the field of view of the CCD or CMOS camera. To reduce sensitivity to edge effects and to misalignment, it may be desirable for the illuminated area to be slightly larger than the camera's field of view. By way of a non-limiting example, if the field of view is about 16 mm×8 mm, a reasonable illuminated area may be about 18 mm×10 mm. The types of illumination may be Abbe, Kohler, or neither Abbe nor Kohler illumination. Abbe illumination and Kohler illumination are well known and are described in, for example, Chapter 14 of Optical System Design, Second Edition by Robert E. Fischer et al., SPIE Press, McGraw-Hill, N.Y., 2008.

In some embodiments, the illumination system may be used for off-axis illumination. In other embodiments, the illumination system may be used for trans-illumination or epi-illumination. If the illumination system is used for off-axis illumination or trans-illumination, then the illumination system and the imaging system are separate from each other, with no shared optical components. If the illumination system is used for epi-illumination, then the illumination system and the imaging system may share a beamsplitter and possibly one or more lenses. The beamsplitter may be a plate beamsplitter or a cube beamsplitter. If the optical instrument is used for fluorescence imaging, the beamsplitter is typically a single-edge or multi-edge longpass dichroic beamsplitter.

Often the illumination system may contain a square or rectangular aperture so that the illuminated area has the same shape as the region that is imaged by the CCD or CMOS camera. In embodiments where off-axis illumination is used, the aperture may be trapezoidal in shape instead of square or rectangular. An off-axis illumination system may or may not satisfy the Scheimpflug condition. The Scheimpflug condition is described in, for example, Modern Optical Engineering, Second Edition by Warren J. Smith, McGraw-Hill, N.Y., 1990.

In some embodiments, the illumination system may contain one or more of the following: spherical lenses, aspheric lenses, a solid homogenizing rod with a rectangular or trapezoidal cross section, a hollow homogenizing light tunnel with a rectangular or trapezoidal cross section, a microlens array or a pair of microlens arrays, a stationary or rotating diffuser, a compound parabolic concentrator, a non-imaging optical element other than a compound parabolic concentrator (e.g., a free-form catadioptric element), an optical fiber, a fiber bundle, or a liquid light guide.

The illumination system may contain one or more light sources, selected from the group including, but not limited to, one or more LEDs, one or more lasers, a xenon arc lamp, a metal halide lamp, or an incandescent lamp, or a combination thereof. The illumination system may also contain a spectrally selective excitation filter selected from the list including, but not limited to, a single-band interference filter, a multi-band interference filter, or a shortpass interference filter. If the illumination system contains two or more light sources, they may be the same (by way of non-limiting example, two or more LEDs with peak emission wavelengths of about 525 nm for excitation of Cy3 dye, mounted as close together as possible on a circuit board) or different (by way of non-limiting example, an LED with a peak excitation wavelength of about 525 nm for excitation of Cy3 dye, and an LED with a peak excitation wavelength of about 625 nm for excitation of Cy5 dye, mounted as close together as possible on a circuit board). Two-color or multicolor LED assemblies are available from, for example, LED Engin, Inc. (San Jose, Calif.) and Innovations in Optics, Inc. (Woburn, Mass.).

In some embodiments, a light source in the illumination system may be controlled electronically. By way of a non-limiting example, a light source may be synchronized with the CCD or CMOS camera so that the light source turns on when the CCD or CMOS camera begins an exposure and turns off when the camera finishes an exposure. If the illumination system contains two or more light sources, they may optionally be controlled together or independently of each other.

In some embodiments, a light source may be left on continuously. In this case, the illumination system may contain an electronically controlled shutter, and the shutter may be synchronized with the CCD or CMOS camera so that the shutter opens when the CCD or CMOS camera begins an exposure and closes when the camera finishes an exposure.

In some embodiments, the optical instrument may contain a single illumination system. In other embodiments, the instrument may contain two or more illumination systems that are identical. In yet other embodiments, the instrument may contain two or more illumination systems that are different. By way of non-limiting examples, an optical instrument for detecting fluorescence from Cy3 and Cy5 may contain one illumination system for Cy3 excitation and another illumination system for Cy5 excitation, or it may contain a single illumination system that is used for both Cy3 and Cy5 excitation.

Translation Stage

Yet another main component of the optical instrument may be one or more translation stages. One purpose of the translation stage may be to move sample holders in and out of the field view of the imaging system. Another purpose of the translation stage system may be to move the imaging system, components of the imaging system, the illumination system, or components of the illumination system relative to the sample or relative to one another, for obtaining the best possible image.

In many embodiments of the presently disclosed systems, the translation stage may further comprise a sample holder. By way of non-limiting examples, if the optical instrument is used to take images of 16-well microscope slides, the translation stage contains a slide holder. If the optical instrument is used to take images of 96-well plates or 384-well plates, and it contains a plate holder. The slide holder, plate holder, or other array support holder may be mounted on the translation stage system in any of a variety of ways known to those skilled in the art.

The translation stage may have one or more axes of motion. By way of a non-limiting example, if the support is a 16-well microscope slide and the instrument takes images of 2 adjacent wells simultaneously, a single axis of motion may be sufficient. By way of another non-limiting example, if the support is a 96-well plate and the instrument takes images of 2 adjacent wells simultaneously, then at least 2 axes of motion would be required. Additional axes of motion for adjustment of focus and tilt may also be added. If the instrument can take an image of all of the regions on the support in a single exposure, then the translation stage may be omitted in some embodiments of the optical instrument.

Housing

The systems and devices described herein can include features for insuring that the sensors of the device detect appropriate signal. For example the systems and devices can include light excluding features. The light excluding features generally reduce unintended signal from reaching light sensitive sensors. In many embodiments, one or more of the imaging system, illumination system, translation stage, and other components of the instrument are surrounded by a housing. The housing can be opaque. The housing can, in some instances, act as a faraday cage. In some instances a single housing is sufficient to exclude light from systems. The single housing can also provide external protection of the system. Alternatively, multiple housings may individually contain one or more components of the instrument. In some instances the housings are nested housings. In various embodiments, the housing can be gas and/or liquid tight.

The housing may have an access point which can exclude light from the interior of the housing. The access point may comprise materials that absorb light in the spectrum relevant to the sensors within the housing, e.g. vantablack in the visible spectrum. The access point may comprise a closure device. The closure device may be opaque. The closure device may be, e.g., a door. The closure device may be substantially light-tight in a closed position. The closure may be light-tight in a closed position.

The closure device can be opened, e.g., for insertion and removal of a 16-well slide, 96-well plate, 384-well plate, or other array support. A sensor (for example, a photointerrupter) may be used to determine whether the closure device is open or closed. The instrument's software or electronic hardware may prevent the light source in the illumination system from turning on when the closure device is open, may prevent power from being applied to the image sensor, and/or may prevent the translation stage from moving when the closure device is open.

In some embodiments, the housing may further comprise a mechanism for automated opening and closing of the closure device, as illustrated in FIGS. 10-13. The closure device can provide access to the interior of the housing. The closure device can provide access for the array to be loaded in and out of the instrument. This operation can be performed automatically. In some instances, the closure device can exclude ambient light during imaging, while opening reliably to permit loading.

In some instances the closure device does not comprise pivoting parts. In some embodiments of the disclosed systems and platforms, the closure device is held by magnets to the housing. Magnets can hold the closure device to the housing in a closed position. Magnets can hold the closure device to a loading device, e.g. a tray, in an open position. During a transition from an open to closed position the closure device can transition from being primarily magnetically attached to a loading device to being primarily magnetically attached to the housing. During a transition from a closed to open position the closure device can transition from being primarily magnetically attached to the housing to being primarily magnetically attached to the loading device. In some instances the transition between the open and closed state is magnetically unstable, such instability causing the closure device to move from the transition state to either the more stable open or closed position.

The closure device can comprise a self-locating function provided by conical features on the door. The thicknesses of the parts which support the magnets on each side of a mating pair, and the depth of retaining pockets within those parts, defines the spacing between magnets in each mating pair, and thus the holding forces. The design geometry is matched to the power of the motors to provide enough retaining force, without requiring high motor torque. The system is further designed such that the motor current and speed (and hence torque) can be controlled to improve the performance, and avoid creating a safety hazard. Two of the four magnet pairs are used to temporarily hold the door to the front of the sample tray, when the tray moves outward for loading an array assembly, as depicted in FIG. 10. The other two magnet pairs are used to hold the door closed against the front panel, after the tray has moved inwards (and separated the other two magnet pairs in the process). The respective allocation of magnets is shown in FIG. 11A. The mating magnets on the front of the stage are shown in FIG. 12. The locations of the mating magnets in the front panel are shown in FIG. 13. To provide for secure grip (and therefore reliable operation), rare earth magnets provide high strength (e.g. neodymium magnets). Some embodiments of the design call for disc magnets approximately 8 mm in diameter and 3 mm thick, with the magnetic field parallel to the axis. In some embodiments, it is sufficient to replace one magnet from each pair with a weaker magnet, or with a piece of magnetic material such as iron or mild steel.

In some embodiments of the systems and platforms disclosed herein, a mechanism for providing for automated door or lid closure on one or more instrument compartments is provided, wherein the mechanism comprises: (a) a closure; (b) a housing which magnetically holds the closure in a first position; and (c) a translation stage which magnetically holds the closure in a second position. In some embodiment, the mechanism further comprises a gasket positioned between the closure and the housing. In some embodiments of the mechanism, the gasket is attached to the closure. In other embodiments, the gasket is attached to the housing. In some embodiments, the closure and housing are substantially opaque, and the gasket creates a substantially light-tight seal between the closure and the housing in the first position. In some embodiments of the mechanism, one or more magnets are positioned to hold the closure onto the housing in the first position. In some embodiments of the mechanism, one or more magnets are positioned to hold the closure onto a first surface of the translation stage in the second position. In some embodiments, the mechanism further comprises two or more pairs of mating locating features to align the closure with the translation stage in the second position. In some embodiments, the mechanism further comprises two or more pairs of mating locating features to align the closure with the housing in the first position. In some embodiments of the mechanism, the pairs of mating locating features comprise conical pins and conical holes. In some embodiments, the housing comprises an optical instrument. In some embodiments, the translation stage includes a sample holder. In some embodiments, the sample holder is designed to hold a microscope slide, a microarray, or a microwell plate. In some embodiments, the closure is not hinged. In some embodiments, the closure is not attached to either the housing or the translation stage through the use of fasteners such as screws or clips. In some embodiments, the closure is not attached to either the housing or the translation stage through the use of an adhesive.

Image Data

The methods, systems, and platforms described herein for counting one or more labeled features on an array may comprise data input, or use of the same. The data input may comprise imaging information and/or images of one or more regions of arrays. The images comprise pixel data, wherein each unit of pixel data may be encoded in, by way of non-limiting examples, 4, 8, 12, 14, 16, 32, 64, 128, 256, or more bits. An image may encompass one or more regions of an array. The spatial resolution of an image may be determined by the spatial resolution of the optical instrument, but in some embodiments of the disclosed methods and systems, spatial resolution may be enhanced by digital image processing schemes based on, by way of non-limiting examples, interpolations, extrapolations, modeling, and/or transforms.

The methods, systems, and platforms described herein for counting one or more labeled features on an array may comprise acquisition and analysis of images of one, two, or more distinct regions on an array. In some embodiments, two or more regions to be imaged may overlap, partially overlap, or not overlap at all. Furthermore, two or more regions to be imaged may be adjacent, or non-adjacent.

The methods, software, systems, and platforms described herein for counting one or more labeled features on an array may comprise acquisition and analysis of images of all or a portion of an array. In some embodiments, the region of an array that is imaged may comprise at least about 1% of the total area of the array. In some embodiments, the region of the array that is imaged image may comprise at least about 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10% or more of the total area of the array. In other embodiments, the region of the array to be imaged may comprise at least about 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, 20%, 21%, 22%, 23%, 24%, 25% or more of the total area of the array. In still other embodiments, the region of the array to be imaged may comprise at least about 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70% or more of the total area of the array. In some embodiments, the region of the array to be imaged may comprise at least about 75%, 80%, 85%, 90%, 92%, 95%, 97% or more of the total area of the array.

The methods, software, systems, and platforms described herein for counting one or more labeled features on an array may comprise acquisiton and analysis of images of all or a portion of the features of an array. In some embodiments, the image may encompass between 10% and 100% of the total number of features on the array. In some embodiments, the image may encompass at least 5%, at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 90%, or at least 95% of the total number of features on the array. In some embodiments, the image may encompass at most 95%, at most 90%, at most, 80%, at most 70%, at most 60%, at most 50%, at most 40%, at most 30%, at most 20%, at more 10%, or at most 5% of the total number of features on the array. The number of features encompassed by the image may fall within any range bounded by any of these values (e.g. from about 15% to about 90% of the total number of features of the array).

Image Acquisition

The methods, systems, and platforms described herein comprise software for acquiring images from an optical instrument. In some embodiments, e.g. for optical instruments comprising two or more image sensors, the image acquisition may operate in a parallel mode, i.e. where two or more images are acquired simultaneously. Alternatively, the image acquisition may operate in a serial mode, where two or more images are acquired sequentially. In general, image acquisition may be performed in a continuous fashion (i.e., wherein the image is acquired within a single exposure time period) or intermittently (i.e., wherein the image is acquired in a discontinuous fashion, e.g. using two or more separate exposure time periods, wherein in some embodiments two more more images are combined for signal averaging purposes).

In a non-limiting example, an array may comprise 16 wells where an image is formed for each well. The image acquisition module may sequentially read the 16 images. Reading the 16 images can be completed in a continuous time period; or, the system may read a first image followed by analyzing the first image, and then the procedure of image reading and image analysis repeats till the 16th image is analyzed. Alternatively, the image acquisition module may read a pair of images at once, and repeat the reading till all the 16 images are acquired. The 16 images may be read sequentially in a single time period. In some applications, a pair of images may be read, followed by immediate image analyses.

Image Analysis

In general, one of the objectives in performing image processing and analysis is to improve signal-to-noise ratios and quantitation. In an ideal array experiment, labeled features comprising bound target molecules and/or labels would produce a uniform, non-saturated signal level when imaged and non-labeled features would appear uniformly dark, with a signal level of close to zero. In reality, a variety of artifacts due to instrumental and/or assay procedural issues including, but not limited to, stray light, background fluorescence (in the case of fluorescence-based imaging), particulate contaminants, and non-specific binding of assay components, can produce images that hinder one's ability to extract quantitative signal intensity data and make definitive calls as to which features of the array are labeled. Accordingly, the methods, systems, and platforms disclosed herein may comprise software for performing a variety of image processing tasks including, but not limited to, feature location, image orientation correction, background correction, intensity measurement, data scaling, data thresholding, and data analysis functions.

Image orientation and location of features. In some embodiments, fiducial features incorporated into the design of an array are used to orient the image and locate features in an automated fashion using custom image analysis software. By way of non-limiting example, the microarray pattern shown in FIGS. 15 and 16 consists of a 32×32 array of features, where fiducial features in the top and bottom rows permit location of the array in the digital images. The fiducial features are typically arranged in an asymmetric pattern whose orientation is readily identifiable, e.g. fiducial features located in the top row of features in an array such as that depicted in FIG. 16 may comprise a distinctive pattern for which the left and right ends of the row are asymmetric, while the pattern of fiducial features in the bottom row is typically different from that in the top row. This permits easy manual and automatic identification of incorrect placement of the array, and also facilitated detection of imaging problems. In some embodiments, the image may be transformed also transformed so that the apparent orientation of the images corresponds to the orientation as viewed by a user, often referred to as the viewing reference orientation, as shown in FIG. 14 for a specific embodiment of an array designed for use with the methods, systems, and platforms disclosed herein.

Refinement of feature locations. In some embodiments, the measured location of each feature is refined so as to account for array fabrication errors, which can produce offsets of several image pixels. The locations of features obtained during the initial image orientation and feature location step may be used to subdivide the array or array region into analysis windows, for example an array may be divided into 32×32 analysis windows, wherein each analysis window comprises an image area of 12×12 pixels centered on each feature, as shown in FIG. 19. The size of the analysis window used is dependent on the size of the features on the array, and may be any size that is necessary to correctly locate and distinguish between features and background regions on the array. By way of non-limiting examples, the analysis window may be defined as a 5×5, 7×7, 9×9, 15×15, 51×51, or 101×101 pixel area that is centered on the array feature. The position of the feature within the window may be determined on the basis of the signal intensity distribution and clustering of the pixels within the analysis window. The refined location of the feature is calculated as an offset in coordinates X and Y from the site predicted by a perfect rectilinear grid. In some embodiments, distortion of the feature location results due to defects such as dust is avoided by making use of the correlation between printing artifacts between different arrays on the same substrate. Since the printing artifacts are typically consistent, the correction relative to a hypothetical rectilinear grid is also consistent. The feature location optimization results for a given feature are combined across all of the arrays being analyzed, and the median offset is used for subsequent analysis, which greatly decreases noise in the final experimental results.

Local background correction. Once the feature pixel set “S” and background pixel set “B” have been defined for each location in the array (for example, see FIG. 20), the local background is removed via a calculation involving signal intensity and background intensity statistics. Examples of suitable signal and background intensity statistics for use in local background correction calculations include, but are not limited to, the mean, the median, or a ratio of signal-to-background. In some embodiments, following feature location refinement performed as described above, the pixels within the analysis window are assigned to be signal pixels, background pixels, or transitional pixels, i.e. pixels to be disregarded, in subsequent calculations of signal and background intensity statistics (see FIG. 20). In some embodiments of the disclosed methods, local background correction is performed via subtraction in logarithm space, i.e. a calculation that is closely related to a signal-to-background ratio calculation, as illustrated in a non-limiting example below:

Given the 16-bit pixel data measurements for a defined feature and background area, on next calculates a single value S for the signal pixels and a value B for the background pixels respectively. One useful statistic is the median value for each set of pixels, i.e.

• • S=the median of the pixel values for the set of pixels “S”
  • B=the median of the pixel values for the set of pixels “B”Various other statistics could be used in this situation, such as the mean of the set of values, or a nominated percentile within the set. It is not necessary, and may not be optimal, to use the same statistic for both S and B. For example, low data noise and strong separation between “on” and “off” data points can be obtained by using:
  • S=the median of the pixel values for the set of pixels “S”, and
  • B=the 25^th percentile of the pixel values for the set of pixels “B”.As a further enhancement, the particular percentile used can be a pre-stored and re-configurable parameter stored in a settings file.The background-corrected intensity statistic for each spot is:I=log₂(16S)−log₂(16B)An example of a scatter plot (intensity statistic vs feature number) and histogram of intensity data are shown in FIG. 21. In this example, the background is corrected for by performing a subtraction of logarithms, such that the intensity metric is related to a ratio of S and B. In some situations, a linear subtraction (e.g. I=S−B) is preferable. Once the background-corrected intensity statistics have been calculated for the compete set of features, the next task is to determine which of the features are labeled (i.e. “on”, or “positive”) and which are non-labeled (i.e. “off” or “negative”). This is accomplished by determining a signal intensity threshold value based on a statistical analysis of the local background-corrected feature intensities, and subsequently counting how many features, k, have background-corrected signal intensities that are larger than this threshold level. The signal intensity threshold may be considered a “dynamic” signal intensity threshold in that the threshold is determined through analysis of the data from the current experiment, and thereby eliminates potential errors due to such factors as instrumental drift and variations in assay procedure.

Determination of dynamic signal intensity thresholds. In many embodiments of the methods, systems, and platforms disclosed herein, a dynamic signal intensity threshold is determined for one or more regions of an array by performing one or more statistical analyses of the background corrected signal intensity data for the complete set of features. Any of a variety of statistical (or empirical) analysis techniques may be used, including but not limited to, k-means clustering, k-medoids clustering, mixture model statistical analysis, probe reference distribution methods, or empirical analysis based on sorting of background corrected signal intensity values, sorting of pairwise differences in background corrected signal intensity values, etc. In some embodiments, analyses may utilize spatial and/or temporal information collected across multiple analysis windows, across multiple array regions, or over specified periods of time, or combinations thereof, to improve the quality of the analysis and thereby improve the quantitative aspects of the disclosed methods. In some embodiments, other sources of information, including, but not limited to, for example, locations of probes, frequently occurring artifact patterns, previously derived results, literature reports, array manufacturers' suggestions, human knowledge, and/or human guidance may also be integrated into the analysis.

By way of a non-limiting example of threshold determination, in some embodiments of the disclosed methods, the background corrected signal intensity threshold may be determined using an empirical approach (e.g. the “E-Derivative” approach; see FIG. 22) wherein the background corrected signal intensity data for the complete set of array features constitutes a set I={I_i}. The set I is sorted in increasing order to obtain a set of ordered corrected signal intensity values z={z_i}={Sort[y_i]}. Next, the differences between each sorted array value are calculated to obtain d={d₁, d₂, . . . , d_m}, where d_i=z_i+1−z_i. The intensity differences are then smoothed using a “window” whose width is w, to produce a smoothed, sorted array s:

$s_j=\frac{\sum _{i=j-w}^{j+w}{d}_i}{2w+1}$ The threshold is T, the point for which the slope of the smoothed, sorted data is steepest (see FIG. 21):T=max(s_j)The number of features, k, which are “on” (or labeled) is:k=Σ_i=1^mI[I_i>T].

By way of another non-limiting example of threshold determination, in some embodiments the background corrected signal intensity threshold may be determined by fitting the background corrected feature intensity data to two more more assumed distributions (i.e. a “Mixture Model” approach), wherein the assumed distributions comprise normal distributions, uniform distributions, etc. The mixture model approach essentially models the underlying process that generated the data, by assuming that the positive feature intensities are generated from a positive feature distribution with higher average signal intensity, and the negative feature intensities are generated from a negative feature distribution with lower average signal intensity. This approach additionally models the variability in the feature intensities generated by each distribution, which can be useful in cases where the negative feature intensities tend to be much less variable, while the positive feature intensities tends to be much more variable. The choice of the distributions is determined by the shape of the data curve in a background corrected feature intensity histogram. The parameters of the model, e.g. the estimated average intensities for “on” and “off” features and their corresponding variance, are estimated from the data using a method such as the Expectation Maximization algorithm.

By way of another non-limiting example of threshold determination, in some embodiments the background corrected signal intensity threshold may be determined by fitting the background corrected feature intensity data to a model function comprising three assumed distributions (i.e. a “3-Component Model” approach), wherein the assumed distributions comprise a log-normal distribution, Dist1, for the “off” spots, a normal distribution, Dist2, for the “on” spots, and a flat offset FlatLevel. Adjustable parameters for the model include: (i) the number of bins in the starting histogram, (ii) Dist1 amplitude, (iii) Dist1 position, (iv) Dist1 standard deviation, (v) Dist2 amplitude, (vi) Dist2 position, (vii) Dist2 standard deviation, and (viii) FlatLevel. An example fit to histogram data is shown in FIG. 23. One non-limiting example of a method to determine the threshold after fitting feature intensity data to such a distribution is as follows: (i) fit the 3-component distribution to the histogram data, and (ii) set the threshold T by calculating the following values: (1) the intensity t_low where the high-intensity side of the fitted log-normal distribution component drops below 1 (or a defined parameter for comparison), (2) the intensity t_subflat where the high-intensity side of the fitted log-normal distribution component drops below the fitted FlatLevel result, (3) the intensity t_subnorm where the high-intensity side of the fitted log-normal distribution component drops below the value of the fitted normal distribution at that histogram bin, and (4) choosing T=min[t_low, t_subflat, t_subnorm]. Alternative approaches for determining a threshold using a 3-component model approach will be apparent to those of skill in the art. It can be beneficial to calculate starting values of model parameters, to improve the speed and reliability of the modelling process, which can be achieved using methods such as a coarse search to identify the dominant peaks in the histogram, or based on assumptions derived from typical historical data sets.

By way of another non-limiting example of threshold determination, in some embodiments the background corrected signal intensity threshold may be determined using a “Peak Split Fiducials” approach. This approach, which copes well with low-quality data, is described as follows. An initial split of the feature intensity data into high and low intensity groups is made using the scale defined naturally by the spread between “on” (label present) and “off” (label absent) features in the fiducial rows. Then, the histogram peak (after optionally smoothing the data using standard methods such as a moving average filter) is found for each group. The threshold is then determined by examining the spread in the intensity data around the low-intensity group peak. Define upper and lower bounds of fiducial intensity: (i) F_off=[median of OFF fiducials], F_on=[median of ON fiducials], and (iii) F_range=F_on−F_off. Perform an initial split of the data based on the fiducial scale, at the level Splitvalue=F_off+PeakSplit×F_range, where the parameter PeakSplit is a percentage of F_range. Find 2 peaks: (i) Peak4=the intensity peak for which the histogram is a maximum, for all features of intensity less than Splitvalue, (ii) Peak2=the intensity peak for which the histogram is a maximum, for all features of intensity greater than Splitvalue. Calculate the standard deviation, Stdev1, of all the features in the neighbourhood of Peak1, defined as all index features from the lowest intensity up to Peak1+PeakOffsetFraction×(Peak2−Peak1), where PeakOffsetFraction is an adjustable parameter. Set the threshold to the lesser of T_psf and T_LocMin, which are calculated as follows: (i) T_psf=Peak1+StdevMultiple×Stdev1, where StdevMultiple is a parameter, OR T_LocMin=the intensity corresponding to the minimum of a smoothed histogram curve between Peak1 and T_psf. Similar approaches using different methods for determining the spread around either peak can also be used.

The methods and systems disclosed herein may comprise detecting one or more labeled features within one or more regions on an array. In some embodiments, detecting a labeled feature within a region may comprise comparing the background corrected signal intensity for a feature with a dynamic signal intensity threshold derived through statistical analysis of the background corrected signal intensities for the complete set of features. When the background corrected signal intensity for a given feature is above the threshold, the feature may be classified as a labeled feature. Alternatively, if the background corrected signal intensity for a given feature is below the threshold, the feature may be classified as non-labeled. Application of a background corrected signal intensity threshold to the corrected signal intensity data for the complete set of features thus constitutes a binary transformation of the data to a digital output wherein features are classified as either labeled (“on”) or non-labeled (“off”). Those of skill in the art will recognize that there are many possible variations in the type and order of analysis steps that may be applied to achieve this binary transformation.

Calculation of the absolute number of target molecules in a sample. The absolute number of target molecules in a sample, wherein the target molecules have been labeled in a stochastic fashion as described previously, may be determined using arrays comprising feature sets comprising probes that are specific for the labels in the stochastic label set. Following hybridization or binding of the target molecules or labeled target molecules to the array, the array is imaged and processed as described above, and the number of target molecules, N, in the sample is determined from the number, k, of labeled features based on Poisson distribution statistics:

$N=-m*\log \left(1-\frac{k}{m}\right)$ where m is the total number of features (i.e. the total number of unique labels in the set of stochastic labels).

Quality metrics. In some embodiments, it is beneficial to include a numerical measure of the quality of the data, to help to gauge the success of an experiment. In some embodiment, this quality measurement may be based on statistics from the feature-by-feature intensity data. One simple quality measurement Q_Sep1 is simply the difference between the means of the positive and negative features intensities, after background correction and scaling, i.e. Q_Sep1=(mean intensity of features having an intensity above the signal intensity threshold)−(mean intensity of features having an intensity below the threshold). In some embodiments, this metric may also incorporate the spread in the intensities of the feature distribution(s) by scaling the difference between means by the standard deviation of each distribution, e.g. Q_Sep2=Q_Sep1/(standard deviation of intensities for feature having intensities below the threshold intensity). Other quality measurements can be constructed based on the separation and breadth of modelled distributions which are fitted to the experimental data. In some embodiments, deviance measurement may be used for a quality metric (FIG. 24); this is a calculation based on the degree of separation between two fitted normal distributions. In some embodiments, it is preferable to empirically determine a dynamic intensity threshold by setting the threshold to a value which maximizes a quality metric.

Confidence intervals. In some embodiments of the methods disclosed herein, it is beneficial to define confidence intervals (see Dube, et al. (2008), PLoS ONE 3(8): e2876 for a more complete description) when specifying estimates of the absolute number of target molecules detected in a sample using the techniques described above. The 95% confidence interval of the estimation of N from stochastic labeling experiments can be derived from k for a single reaction employing a single set of _m distinct labels. The 95% confidence interval for N ranges from N_low to N_high, where

$N_{\mathrm{low}}=-m\times \ln \left[1-\left(\frac{k}{m}-1.96\sqrt{\frac{\frac{k}{m}\left(1-\frac{k}{m}\right)}{m}}\right)\right],\mathrm{and}$ $N_{\mathrm{high}}=-m\times \ln \left[1-\left(\frac{k}{m}+1.96\sqrt{\frac{\frac{k}{m}\left(1-\frac{k}{m}\right)}{m}}\right)\right]$

Ratio of the number of copies of a target molecule in two samples. Frequently, researchers seek to compare the expression levels of genes in different samples, by calculating a ratio between gene expression levels in two or more samples. Using calculations such as those described above, it is possible to derive confidence intervals for such ratios where the number of target molecules in each sample are determined using the methods, systems, and platforms as disclosed herein.

Replicate experiments. The benefit of performing replicate experiments, and the proper calculation of associated uncertainties, is illustrated in FIG. 25. While results (blue points) from replicate experiments can simply be combined (blue error bars), calculating the uncertainty from Poisson statistics, wherein one considers the replicates as comprising a larger pool of labels, gives the smaller green error bars illustrated in the figure. The accuracy of this estimation will vary depending on the consistency between replicates, and there is a numerical simplification employed in considering the labels of replicate experiments to be a pool of diverse labels. Therefore, in some embodiments of the disclosed methods, different methods for calculating confidence intervals may be more appropriate at high ratios of k/m.

User Interface

The methods, software, systems, and platforms disclosed herein may comprise a user interface, or use of the same. The user interface may provide one or more inputs from a user. The input from the user interface may comprise instructions for counting the one or more labeled features in a real time mode. The input from the user interface may comprise instructions for counting the one or more features from one or more images. The one or more images may be archived images. The one or more images may be live captured images.

Different platform operators may have their own preferences about the timing to analyze images. One platform operator may want to run the image analyses while live capturing images. Another platform operator may run the image analyses after all the images have been collected. Or, another platform operator may run the image analyses on a set of archived images. These options can be selected via inputs to the user interface.

Digital Processing Device

The methods, software, systems, and platforms disclosed herein may comprise a digital processing device, or use of the same. The digital processing device may comprise one or more hardware central processing units (CPU) that carry out the device's functions. The digital processing device may comprise an operating system configured to perform executable instructions. The digital processing device may be connected to a computer network. The digital processing device may be connected to the Internet such that it accesses the World Wide Web. The digital processing device may be connected to a cloud computing infrastructure. The digital processing device may be connected to an intranet. The digital processing device may be connected to a data storage device.

Suitable digital processing devices may include, by way of non-limiting examples, server computers, desktop computers, laptop computers, notebook computers, sub-notebook computers, netbook computers, netpad computers, set-top computers, handheld computers, Internet appliances, mobile smartphones, tablet computers, personal digital assistants, video game consoles, and vehicles. In some instances, smartphones may be suitable for use in the system described herein. In some instances, select televisions, video players, and digital music players with optional computer network connectivity may be suitable for use in the system described herein. Suitable tablet computers may include those with booklet, slate, and convertible configurations, known to those of skill in the art.

The digital processing device may comprise an operating system configured to perform executable instructions. The operating system may be software, including programs and data, which manages the device's hardware and provides services for execution of applications. Suitable server operating systems may include, by way of non-limiting examples, FreeBSD, OpenBSD, NetBSD®, Linux, Apple® Mac OS X Server®, Oracle® Solaris®, Windows Server®, and Novell® NetWare®. Suitable personal computer operating systems may include, by way of non-limiting examples, Microsoft® Windows®, Apple® Mac OS X®, UNIX®, and UNIX-like operating systems such as GNU/Linux®. The operating system is provided by cloud computing. Suitable mobile smart phone operating systems may include, by way of non-limiting examples, Nokia® Symbian® OS, Apple® iOS®, Research In Motion® BlackBerry OS®, Google® Android®, Microsoft® Windows Phone® OS, Microsoft® Windows Mobile® OS, Linux®, and Palm® WebOS®.

The digital processing device may comprise a storage and/or memory device. The storage and/or memory device may be one or more physical apparatuses used to store data or programs on a temporary or permanent basis. The digital processing device may be a volatile memory and may require power to maintain stored information. The digital processing device may be a non-volatile memory and may retain stored information when the digital processing device is not powered. The non-volatile memory may comprise flash memory. The non-volatile memory may comprise dynamic random-access memory (DRAM). The non-volatile memory may comprise ferroelectric random access memory (FRAM). The non-volatile memory may comprise phase-change random access memory (PRAM). The storage device may include, by way of non-limiting examples, CD-ROMs, DVDs, flash memory devices, magnetic disk drives, magnetic tapes drives, optical disk drives, and cloud computing based storage. The storage and/or memory device may be a combination of devices such as those disclosed herein.

The digital processing device may comprise a display. The display may be used to send visual information to a user. The display may be a cathode ray tube (CRT). The display may be a liquid crystal display (LCD). The display may be a thin film transistor liquid crystal display (TFT-LCD). The display may be an organic light emitting diode (OLED) display. The OLED display may be a passive-matrix OLED (PMOLED) or active-matrix OLED (AMOLED) display. The display may be a plasma display. The display may be a video projector. The display may be a combination of devices such as those disclosed herein.

The digital processing device may comprise an input device to receive information from a user. The input device may be a keyboard. The input device may be a pointing device including, by way of non-limiting examples, a mouse, trackball, track pad, joystick, game controller, or stylus. The input device may be a touch screen or a multi-touch screen. The input device may be a microphone to capture voice or other sound input. The input device may be a video camera to capture motion or visual input. The input device may be a combination of devices such as those disclosed herein.

Non-Transitory Computer Readable Storage Medium

The methods, software, systems, and platforms disclosed herein may comprise one or more non-transitory computer readable storage media encoded with a program including instructions executable by the operating system of an optionally networked digital processing device. A computer readable storage medium may be a tangible component of a digital processing device. A computer readable storage medium may be optionally removable from a digital processing device. A computer readable storage medium may include, by way of non-limiting examples, CD-ROMs, DVDs, flash memory devices, solid state memory, magnetic disk drives, magnetic tape drives, optical disk drives, cloud computing systems and services, and the like. The program and instructions may be permanently, substantially permanently, semi-permanently, or non-transitorily encoded on the media.

Computer Programs (General)

The methods, software, systems, and platforms disclosed herein may comprise at least one computer processor, or use of the same. The computer processor may comprise a computer program. A computer program may include a sequence of instructions, executable in the digital processing device's CPU, written to perform a specified task. Computer readable instructions may be implemented as program modules, such as functions, features, Application Programming Interfaces (APIs), data structures, and the like, that perform particular tasks or implement particular abstract data types. A computer program may be written in various versions of various languages.

The functionality of the computer readable instructions may be combined or distributed as desired in various environments. A computer program may comprise one sequence of instructions. A computer program may comprise a plurality of sequences of instructions. A computer program may be provided from one location. A computer program may be provided from a plurality of locations. A computer program may include one or more software modules. A computer program may include, in part or in whole, one or more web applications, one or more mobile applications, one or more standalone applications, one or more web browser plug-ins, extensions, add-ins, or add-ons, or combinations thereof.

Web Applications

A computer program may include a web application. In light of the disclosure provided herein, those of skill in the art will recognize that a web application may utilize one or more software frameworks and one or more database systems. A web application may be created upon a software framework such as Microsoft®.NET or Ruby on Rails (RoR). A web application may utilize one or more database systems including, by way of non-limiting examples, relational, non-relational, feature oriented, associative, and XML database systems. Suitable relational database systems may include, by way of non-limiting examples, Microsoft® SQL Server, mySQL™, and Oracle®. Those of skill in the art will also recognize that a web application may be written in one or more versions of one or more languages. A web application may be written in one or more markup languages, presentation definition languages, client-side scripting languages, server-side coding languages, database query languages, or combinations thereof. A web application may be written to some extent in a markup language such as Hypertext Markup Language (HTML), Extensible Hypertext Markup Language (XHTML), or eXtensible Markup Language (XML). A web application may be written to some extent in a presentation definition language such as Cascading Style Sheets (CSS). A web application may be written to some extent in a client-side scripting language such as Asynchronous Javascript and XML (AJAX), Flash® Actionscript, Javascript, or Silverlight®. A web application may be written to some extent in a server-side coding language such as Active Server Pages (ASP), ColdFusion®, Perl, Java™, JavaServer Pages (JSP), Hypertext Preprocessor (PHP), Python™, Ruby, Tcl, Smalltalk, WebDNA®, or Groovy. A web application may be written to some extent in a database query language such as Structured Query Language (SQL). A web application may integrate enterprise server products such as IBM Lotus Domino. A web application may include a media player element. A media player element may utilize one or more of many suitable multimedia technologies including, by way of non-limiting examples, Adobe® Flash®, HTML 5, Apple® QuickTime®, Microsoft® Silverlight®, Java™, and Unity

Mobile Applications

A computer program may include a mobile application provided to a mobile digital processing device. The mobile application may be provided to a mobile digital processing device at the time it is manufactured. The mobile application may be provided to a mobile digital processing device via the computer network described herein.

A mobile application may be created by techniques known to those of skill in the art using hardware, languages, and development environments known to the art. Those of skill in the art will recognize that mobile applications may be written in several languages. Suitable programming languages include, by way of non-limiting examples, C, C++, C#, Featureive-C, Java™, Javascript, Pascal, Feature Pascal, Python™, Ruby, VB.NET, WML, and XHTML/HTML with or without CSS, or combinations thereof.

Suitable mobile application development environments may be available from several sources. Commercially available development environments include, by way of non-limiting examples, AirplaySDK, alcheMo, Appcelerator®, Celsius, Bedrock, Flash Lite, .NET Compact Framework, Rhomobile, and WorkLight Mobile Platform. Other development environments may be available without cost including, by way of non-limiting examples, Lazarus, MobiFlex, MoSync, and Phonegap. Also, mobile device manufacturers distribute software developer kits including, by way of non-limiting examples, iPhone and iPad (iOS) SDK, Android™ SDK, BlackBerry® SDK, BREW SDK, Palm® OS SDK, Symbian SDK, webOS SDK, and Windows® Mobile SDK.

Those of skill in the art will recognize that several commercial forums may be available for distribution of mobile applications including, by way of non-limiting examples, Apple® App Store, Android™ Market, BlackBerry® App World, App Store for Palm devices, App Catalog for webOS, Windows® Marketplace for Mobile, Ovi Store for Nokia® devices, Samsung® Apps, and Nintendo® DSi Shop.

Standalone Applications

A computer program may include a standalone application, which may be a program that may be run as an independent computer process, not an add-on to an existing process, e.g., not a plug-in. Those of skill in the art will recognize that standalone applications may be often compiled. A compiler may be a computer program(s) that transforms source code written in a programming language into binary feature code such as assembly language or machine code. Suitable compiled programming languages include, by way of non-limiting examples, C, C++, Featureive-C, COBOL, Delphi, Eiffel, Java™, Lisp, Python™, Visual Basic, and VB .NET, or combinations thereof. Compilation may be often performed, at least in part, to create an executable program. A computer program may include one or more executable complied applications.

Web Browser Plug-Ins

A computer program may include a web browser plug-in. In computing, a plug-in may be one or more software components that add specific functionality to a larger software application. Makers of software applications may support plug-ins to enable third-party developers to create abilities which extend an application, to support easily adding new features, and to reduce the size of an application. When supported, plug-ins may enable customizing the functionality of a software application. For example, plug-ins are commonly used in web browsers to play video, generate interactivity, scan for viruses, and display particular file types. Those of skill in the art will be familiar with several web browser plug-ins including, Adobe® Flash® Player, Microsoft® Silverlight®, and Apple® QuickTime®. The toolbar may comprise one or more web browser extensions, add-ins, or add-ons. The toolbar may comprise one or more explorer bars, tool bands, or desk bands.

In view of the disclosure provided herein, those of skill in the art will recognize that several plug-in frameworks may be available that enable development of plug-ins in various programming languages, including, by way of non-limiting examples, C++, Delphi, Java™, PHP, Python™, and VB .NET, or combinations thereof.

Web browsers (also called Internet browsers) may be software applications, designed for use with network-connected digital processing devices, for retrieving, presenting, and traversing information resources on the World Wide Web. Suitable web browsers include, by way of non-limiting examples, Microsoft® Internet Explorer®, Mozilla® Firefox®, Google® Chrome, Apple® Safari®, Opera Software® Opera®, and KDE Konqueror. The web browser may be a mobile web browser. Mobile web browsers (also called mircrobrowsers, mini-browsers, and wireless browsers) may be designed for use on mobile digital processing devices including, by way of non-limiting examples, handheld computers, tablet computers, netbook computers, subnotebook computers, smartphones, music players, personal digital assistants (PDAs), and handheld video game systems. Suitable mobile web browsers include, by way of non-limiting examples, Google® Android® browser, RIM BlackBerry® Browser, Apple® Safari®, Palm® Blazer, Palm® WebOS® Browser, Mozilla® Firefox® for mobile, Microsoft® Internet Explorer® Mobile, Amazon® Kindle® Basic Web, Nokia® Browser, Opera Software® Opera® Mobile, and Sony® PSP™ browser.

Software Modules (General)

The methods, software, systems, and platforms disclosed herein may comprise one or more softwares, servers, and database modules, or use of the same. In view of the disclosure provided herein, software modules may be created by techniques known to those of skill in the art using machines, software, and languages known to the art. The software modules disclosed herein may be implemented in a multitude of ways. A software module may comprise a file, a section of code, a programming feature, a programming structure, or combinations thereof. A software module may comprise a plurality of files, a plurality of sections of code, a plurality of programming features, a plurality of programming structures, or combinations thereof. The one or more software modules may comprise, by way of non-limiting examples, a web application, a mobile application, and a standalone application. Software modules may be in one computer program or application. Software modules may be in more than one computer program or application. Software modules may be hosted on one machine. Software modules may be hosted on more than one machine. Software modules may be hosted on cloud computing platforms. Software modules may be hosted on one or more machines in one location. Software modules may be hosted on one or more machines in more than one location.

Databases

The methods, software, systems, and platforms disclosed herein may comprise one or more databases, or use of the same. In view of the disclosure provided herein, those of skill in the art will recognize that many databases may be suitable for storage and retrieval of imaging information. Suitable databases may include, by way of non-limiting examples, relational databases, non-relational databases, feature oriented databases, feature databases, entity-relationship model databases, associative databases, and XML databases. A database may be internet-based. A database may be web-based. A database may be cloud computing-based. A database may be based on one or more local computer storage devices.

EXAMPLES

The following illustrative examples are representative of specific embodiments of the methods, systems, and platforms described herein, but are not meant to be limiting in any way.

Example 1

Optical Instrument

FIG. 1 shows one embodiment of the optical instrument. This embodiment was used for simultaneously imaging 2 adjacent wells of a 16-well microscope slide. Each well contained 1024 (32×32) features, also called spots, which may be labeled with fluorescence. A spot diameter was approximately 80 microns. Center-to-center distance between adjacent spots was 161 microns. The purpose of the instrument was to determine the brightness of each spot. FIG. 1E shows the translation stage system 105, imaging system 106, and illumination system 107. The translation stage system contained a single-axis translation stage which was constructed from a Misumi model SSELBW9-170 recirculating ball slide driven by a Haydon Kerk 26000-series linear actuator. A holder for a 16-well microscope slide was mounted on the translation stage. The linear actuator was controlled by a Peter Norberg Consulting model BC2D20-0700 motion controller. FIG. 1G shows a USB hub 112, custom circuit board 113, and a motion controller 114. The motion controller and the CCD camera were USB devices. The USB hub allowed communication between a computer and the instrument to take place over a single USB cable. The custom circuit board contained a Luxdrive model 3021-D-E-1000 LED driver. The custom circuit board contained a logic chip to synchronize the LED with the CCD camera (turn the LED on when the CCD camera starts an exposure and off when the CCD camera finishes an exposure) and to prevent the LED from turning on when the instrument's door is open.

Example 2

Imaging System

An embodiment of an imaging system is illustrated in FIG. 2. Light emitted by dye molecules on a surface of the support 205 was collimated by an achromatic cemented doublet lens 204, filtered by a bandpass filter 203, and focused by a camera lens 202 onto the sensor 201 of a CCD camera. Lens 204 (Edmund Optics model 47640) had a focal length of 85 mm and a diameter of 25 mm. Filter 203 (Semrock model FF01-593/40-25) was an emission bandpass filter for use with cy3 or phycoerythrin dye. Lens 202 (Fujinon model HF25HA-1B) has a focal length of 25 mm. Lens 202 was a multi-element lens, but it is shown in FIG. 2 as an infinitesimally thin single-element lens because the design details of the multi-element lens were proprietary to Fujinon. The adjustable aperture stop of lens 202 was set to 2.8. The CCD camera (Point Grey Research model CMLN-13S2M-CS) had 1296×964 pixels and a pixel size of 3.75 microns. In this embodiment only the central 1280×640 pixels were used. The camera's plastic housing was removed and the camera's circuit board was cooled by a small fan. Lenses 204 and 202 formed a finite-conjugate imaging system with a magnification of 0.3.

Example 3

Illumination System

An embodiment of an illumination system is illustrated in FIG. 3. Light emitted by light source 301 was collimated by lens 302 and then passed through aperture 303, bandpass filter 304, and lenses 305, 306, and 307, before reaching sample 308. Light source 301 (LedEngin model LZ4-40G100) was an LED with a peak emission wavelength of approximately 525 nm. Lens 302 (Thorlabs model ACL2520-A) was an aspheric lens with a diameter of 25 mm and a focal length of 20 mm. Aperture 303 (Fotofab custom part) was a rectangular hole (19 mm×7.5 mm) in a 25-mm-diameter steel disk. Filter 304 (Semrock model FF01-531/40-25) was an excitation bandpass filter for use with cy3 or phycoerythrin dye. Lenses 305, 306, and 307 were plano-convex lenses with diameters of 25 mm and focal lengths of 60 mm. Lenses 301 and 302 formed a finite-conjugate imaging system with a magnification of 3 and imaged light source 301 onto the pupils of lenses 306 and 307. Lenses 306 and 307 formed a finite-conjugate imaging system with a magnification of 1 and imaged aperture 303 onto sample 308. The illumination system was tilted at 45 degrees with respect to sample 308. The illuminated area was approximately 19 mm×10.6 mm, where 10.6 mm (=7.5 mm/cosine (45 degrees)).

Example 4

Reference Probe Preparation

The purpose of this experiment was to illustrate the use of an ad hoc method to count the number of hybridizations taking place on an array. This example used probes that the specific DNA sequences attached to an array. The 32×32 feature arrays used in this experiment contain 960 different measure spots along with 32 positive control probes and 32 negative control probes (see FIGS. 15 and 16). The 32 positive control probes were used to ensure actual binding can occur by using stock oligonucleotide, while the 32 negative control probes contained empty spots with no probes at all. In the image analysis step, we considered a probe to have intensity above the set intensity threshold, then we referred to the probe as a positive probe, while if the probe intensity was below the set intensity threshold, we referred to the probe as a negative feature. Positive probes were assumed to measure whether there was a significant amount of corresponding complementary oligonucleotide in the sample, while negative features represented absent oligonucleotides. The positive probes were otherwise referred to as labels, meaning we can count up to 960 unique labels or barcodes, to measure 960 copies of oligonucleotides in the sample, which can then be further generalized to predict the actual amount of oligonucleotides in the original sample. Depending on the experiment, out of the M total labels, or 960 total labels, we could calculate the total number of copies, N, of the oligonucleotides in the sample, by predicting N from the actual observed unique barcodes or number of positive probes, k.

Example 5

Threshold Computation

The purpose of this experiment was to demonstrate one method to compute a threshold for discriminating between labeled and non-labeled features on an array.

• • I. Set I_LL, intensity lower limit, I_UL, intensity upper limit, and w, window size.
  • II. Obtain a set of feasible threshold intensities, y={y_i:I_LL<I_i<I_UL}
  • III. Sort y in increasing order to obtain y*.
  • IV. Calculate d={d₁, d₂, d_i, . . . , d_m}, where d_i=y_i+1*−y_i*.
  • V. Calculate a gap statistic for each of the observed intensities:

$x_j=\frac{\sum _{i=j-w}^{j+w}{d}_i}{2w+1}$

• • VI. Identify the threshold c, such that c=max(x_j)
  • VII. Count the number of spots, k, above the threshold c, where k=Σ_i=1^mI[y_i>c].
  • VIII. Given a number of simulations desired, nsim, perform the following procedure nsim times: Randomly select m values with replacement from y={y₁, y₂, . . . , y_m} to obtain y_sim. Then repeat Step I-VII with y_sim to obtain a final count.
  • IX. Calculate {circumflex over (σ)}_k the standard deviation of the nsim simulated counts.
  • X. Calculate the 95% CI for the count as:[k−1.96{circumflex over (σ)}_k,k+1.96{circumflex over (σ)}_k].
  • Note that in order to obtain the true estimate of the molecule count in the sample, we needed to transform by:

$N=-m*\log \left(1-\frac{k}{m}\right)$

• • and similarly, for the 95% CI upper and lower values, where m is the total number of features on the array.

Example 6

Detection of Kan Genes

The purpose of this experiment was to determine the count of kan genes in a sample. The sample containing the kan genes was hybridized to an array. FIG. 4A displays a region of the array acquired from the imaging system. The bright intensity at a spot was correlated with a higher probability of a gene being present at the spot. The image analysis software examined the statistics of the intensity distribution, such as deviance, skewness, kurtosis, and median. These statistics provided guidance for the software to automatically choose the best method to detect the presence of kan genes. In this example, a mixture model algorithm was used to determine the intensity threshold to be 6.6, which optimally divided the intensity distribution into “on” and “off” domains, as shown in FIG. 4B.

Example 7

Titration Experiment

The purpose of this experiment was to detect the presence of molecular hybridization in a titration experiment. After obtaining the intensity measurements of a region, the intensity distribution was computed and is shown in FIG. 5. A person with ordinary skill can identify two modes in the distribution; however, it was very difficult to determine the precise value of the threshold. The invented software automated the task of determining the signal intensity threshold, and determined that an intensity value of 6.02 provides the optimal threshold for distinguishing between labeled and non-labeled features.

Example 8

Background Adjustment

The purpose of this experiment was to demonstrate use of one background subtraction method to process images. FIG. 6A shows an acquired image with pronounced artifacts. A systematic background subtraction was performed to reduce noise. We defined an analysis window centered on a spot. The software then calculated the mean spot intensity, S, spot standard deviation σ_S, number of spot pixels n_S, background mean B, background standard deviation σ_B, and number of background pixels n_B. Then, the software calculated the log 2 background subtracted intensity statistic for each spot:

$I=\frac{{\log }_2\left(16\stackrel{\_}{S}\right)-{\log }_2\left(16\stackrel{\_}{B}\right)}{\sqrt{\frac{{\tau }_S^2}{n_S}+\frac{{\tau }_B^2}{n_B}}}$ $\mathrm{where}$ ${\tau }_S={\log }_2\left(16\left({\sigma }_S+\stackrel{\_}{S}\right)\right)-{\log }_2\left(16{\sigma }_S\right)$ ${\tau }_B={\log }_2\left(16\left({\sigma }_B+\stackrel{\_}{B}\right)\right)-{\log }_2\left(16{\sigma }_B\right)$ FIGS. 6B and 6C show the intensity distributions before and after background adjustment, respectively, demonstrating that background correction enhances the ability of the software to correctly evaluate the presence of the labeled features on the array.

Example 9

Alternative Background Correction

The purpose of this experiment was an alternative way to adjust background. We defined an analysis window centered on a spot. The software then calculated the median spot intensity {tilde over (S)} and median local background intensity {tilde over (B)}. Then, the software calculated the log 2 background subtracted intensity statistic for each spot:I=log₂(16{tilde over (S)})−log₂(16{tilde over (S)}).

Example 10

Pixel 16 Cartridge and Custom Microarray

This example illustrated one embodiment of an array for use with the disclosed methods, systems, and platforms in performing stochastic labeling experiment.

The Pixel 16 cartridge consists of (i) an epoxysilane functionalized glass slide serving as an array substrate, (ii) 16 copies of the custom microarray described in FIGS. 14-16, printed on the functionalized surface of the slide, and (iii) a polymer well frame affixed to the printed side of the slide which serves to define 16 wells which are fluidically separate and in register with the array pattern. The well frame is affixed to the slide following array printing using a die-cut double-sided adhesive.

Custom DNA microarray layout. The microarray pattern consists of a 32×32 array of spots as shown in FIGS. 15 and 16. Fiducial spots in the top and bottom rows permit location of the array in the scanned images. Also, the fiducial spots are arranged in an asymmetric pattern whose orientation is readily identifiable: the top row has a distinctive pattern whose ends are distinct, and the bottom row is different from the top row. This permits easy manual and automatic identification of incorrect placement of the Pixel 16, and also facilitates detection of imaging problems. The remaining 960 spots are each associated with one of the unique probe sequences listed in Table 1.

Oligonucleotide sequences and solution components. Oligonucleotide solutions are provided for preparation of printing solutions in 96-well microplates. Concentration as supplied is 100 μM in H2O. Dilution prior to printing is performed using the Tecan GenMate. Dilution is 880 μL of stock oligo+1320 μL of buffer. The dilution buffer used is 250 mM sodium phosphate with 0.00833% sarcosyl. Buffer is filtered using a 0.2 μm filter. Three sets of plates are prepared in each probe preparation operation. Tips are discarded after each source plate. The final dispensed solution is 40 μM DNA in 150 mM sodium phosphate with 0.005% sarcosyl. The fiducial oligo is supplied at 500 μM in H2O. The fiducial oligonucleotide sequence is: 5′-/5AmMC6/TCC TGA ACG GTA GCATCT TGA CGA C-3′ (SEQ ID NO: 1), 25 bases, 5′ Amino Modifier C6, standard desalting; supplied at 500 μM in H₂O. The fiducial is diluted by mixing 176 μL of fiducial, 704 μL of water, and 1320 μL of buffer. The final fiducial mixture is 40 μM in 150 mM sodium phosphate with 0.005% sarcosyl.

Table of oligonucleotide sequences. The oligonucleotide sequences for the 960 probe sequences (i.e. the sequences that are complementary to the set of stochastic labeling sequences used in molecular counting experiments) are listed in Table 1.

TABLE 1
Oligonucleotide Probe Sequences for Custom Microarray
(Table 1 discloses SEQ ID NOS 2-961, respectively, in order of appearance)
						Calc'd
Plate	IDT	Well	Seq		IDT Mfg	Molec.
Name	Ref. #	Pos.	Name	Sequence	ID	Weight	Tm
AJ_P1	85652789	A01	AJ_1	/5AmMC6/CCC AAA GGG TAC CAG	106534039	8795	61.7
				AGC TTA AGG TCA A
AJ_P1	85652790	A02	AJ_2	/5AmMC6/CCC AAA GCG TTA AGG	106534040	8727	59.7
				TTT CTT GTC ACA A
AJ_P1	85652791	A03	AJ_3	/5AmMC6/CCC AAG TCG TAC GAA	106534041	8675	61.8
				CTC ACC ACA TGA A
AJ_P1	85652792	A04	AJ_4	/5AmMC6/CCC AAA CTT GTT CCC TTG	106534042	8672	60.3
				AGA CCA GTA A
AJ_P1	85652793	A05	AJ_5	/5AmMC6/CCC AAG ACT TCT ACC	106534043	8657	60.7
				CTA GGT TCC AGA A
AJ_P1	85652794	A06	AJ_6	/5AmMC6/CCC AAC CAG ACT TGG	106534044	8755	62.3
				GTA CGT GAA ACA A
AJ_P1	85652795	A07	AJ_7	/5AmMC6/CCC AAC GAC TGG TTC	106534045	8786	62.3
				TGA AGT GGA ACA A
AJ_P1	85652796	A08	AJ_8	/5AmMC6/CCC AAT TTA GCT TCG	106534046	8712	60.4
				TGA GTC AGA CCA A
AJ_P1	85652797	A09	AJ_9	/5AmMC6/CCC AAC TCG AAG AGT	106534047	8752	59.8
				GGT CAG TCT TTA A
AJ_P1	85652798	A10	AJ_10	/5AmMC6/CCC AAT CGC AAG GAG	106534048	8745	58.4
				ACA TAG TCT TTA A
AJ_P1	85652799	A11	AJ_11	/5AmMC6/CCC AAG TCC TAG TGA	106534049	8761	60
				GAG CAA CGT TTA A
AJ_P1	85652800	A12	AJ_12	/5AmMC6/CCC AAG GAA CCT ACT	106534050	8697	61.4
				GTC CTT GTC AGA A
AJ_P1	85652801	B01	AJ_13	/5AmMC6/CCC AAA CTA GAA GAC	106534051	8779	59.7
				GAG TTC GAG TCA A
AJ_P1	85652802	B02	AJ_14	/5AmMC6/CCC AAG GAC ATA CTC	106534052	8699	60
				AAC GTA GCT CAA A
AJ_P1	85652803	B03	AJ_15	/5AmMC6/CCC AAG GCA TTT GCA	106534053	8690	61.9
				ACC TCA CAT GAA A
AJ_P1	85652804	B04	AJ_16	/5AmMC6/CCC AAG TAC CCA TCC	106534054	8666	61.3
				ACT GTC GAG TAA A
AJ_P1	85652805	B05	AJ_17	/5AmMC6/CCC AAA GCG TTT GTG	106534055	8745	59.4
				TAA CAG ACC ATA A
AJ_P1	85652806	B06	AJ_18	/5AmMC6/CCC AAA TGG TCT GGT	106534056	8737	62.3
				TCG ACA GTC ACA A
AJ_P1	85652807	B07	AJ_19	/5AmMC6/CCC AAG AGG TAC AAC	106534057	8795	60.6
				GAC TCT AGG GTA A
AJ_P1	85652808	B08	AJ_20	/5AmMC6/CCC AAG AAC TTC TAC	106534058	8687	58.9
				TTG CTT CGT GAA A
AJ_P1	85652809	B09	AJ_21	/5AmMC6/CCC AAG CAC TTT CTG	106534059	8687	58.8
				TTA ACT AGC TGA A
AJ_P1	85652810	B10	AJ_22	/5AmMC6/CCC AAG AAC CTC TCT	106534060	8672	58.6
				CTA GTG CTA GTA A
AJ_P1	85652811	B11	AJ_23	/5AmMC6/CCC AAG CCT TTA AGC	106534061	8681	60.4
				CTA AAG TCC TGA A
AJ_P1	85652812	B12	AJ_24	/5AmMC6/CCC AAT CTG GTA GCT	106534062	8672	60.3
				CAA CAT CCT TGA A
AJ_P1	85652813	C01	AJ_25	/5AmMC6/CCC AAA GGA CTC CAT	106534063	8795	61.8
				GGA GAA GTG TCA A
AJ_P1	85652814	C02	AJ_26	/5AmMC6/CCC AAG AAC CCT TTC	106534064	8657	62.4
				TGG AAG CTT CCA A
AJ_P1	85652815	C03	AJ_27	/5AmMC6/CCC AAA TTC GCT TCC	106534065	8712	60.1
				TAG TAG TGG ACA A
AJ_P1	85652816	C04	AJ_28	/5AmMC6/CCC AAC CGT ACG AAG	106534066	8681	59.4
				ACC TAG TTT CTA A
AJ_P1	85652817	C05	AJ_29	/5AmMC6/CCC AAT CAC GAA GAG	106534067	8745	58.4
				AGT CAC TGT TTA A
AJ_P1	85652818	C06	AJ_30	/5AmMC6/CCC AAG AAA CAT AAA	106534068	8763	59.3
				CTC GAG TTG CGA A
AJ_P1	85652819	C07	AJ_31	/5AmMC6/CCC AAC CAG TTA CGT	106534069	8752	60.7
				GAG TGT TGC TAA A
AJ_P1	85652820	C08	AJ_32	/5AmMC6/CCC AAA CTC GTG ACT	106534070	8672	60.5
				CCT GTT TCA GAA A
AJ_P1	85652821	C09	AJ_33	/5AmMC6/CCC AAC GGT TGA AGA	106534071	8779	60.6
				GAC TCC TGA AAA A
AJ_P1	85652822	C10	AJ_34	/5AmMC6/CCC AAA TTG CTC TGG	106534072	8705	59.8
				TCA CAT CGA AAA A
AJ_P1	85652823	C11	AJ_35	/5AmMC6/CCC AAC AGG ACT TGT	106534073	8752	60.7
				GCT ACG TGT TAA A
AJ_P1	85652824	C12	AJ_36	/5AmMC6/CCC AAA TTT CGT GTG	106534074	8672	61.9
				TCA ACC ATG CCA A
AJ_P1	85652825	D01	AJ_37	/5AmMC6/CCC AAC GTG AAG GCT	106534075	8754	59.7
				TAA CAA CAT TGA A
AJ_P1	85652826	D02	AJ_38	/5AmMC6/CCC AAT GAA CAC AAC	106534076	8723	59
				TAC GAA GCT GTA A
AJ_P1	85652827	D03	AJ_39	/5AmMC6/CCC AAA CTT CCG TTG	106534077	8687	58.7
				TTA CTA GTC GAA A
AJ_P1	85652828	D04	AJ_40	/5AmMC6/CCC AAG GAG TAC AAG	106534078	8786	61
				CTT CCT AGG GTA A
AJ_P1	85652829	D05	AJ_41	/5AmMC6/CCC AAG TGC TAA ACT	106534079	8687	58.8
				GCT CTT TAC GTA A
AJ_P1	85652830	D06	AJ_42	/5AmMC6/CCC AAA GAA ACT GCA	106534080	8705	59.5
				TCT CCT TTG GAA A
AJ_P1	85652831	D07	AJ_43	/5AmMC6/CCC AAG GAC TAA GTT	106534081	8666	61.4
				CCA CTC ACC TGA A
AJ_P1	85652832	D08	AJ_44	/5AmMC6/CCC AAA GTT GTC TGG	106534082	8752	60.5
				TTC ACT CGA GAA A
AJ_P1	85652833	D09	AJ_45	/5AmMC6/CCC AAC GTT CTA AGT	106534083	8727	59.2
				TTG CTT CGA AGA A
AJ_P1	85652834	D10	AJ_46	/5AmMC6/CCC AAC TAA AGG TTG	106534084	8730	61.5
				TGC ATC CAA GCA A
AJ_P1	85652835	D11	AJ_47	/5AmMC6/CCC AAA GGC TTC ACG	106534085	8696	59.4
				ACA TGT CAT TTA A
AJ_P1	85652836	D12	AJ_48	/5AmMC6/CCC AAC TGC TAG GTT	106534086	8681	59.7
				CCT ACA CAA GTA A
AJ_P1	85652837	E01	AJ_49	/5AmMC6/CCC AAA TCA GTA GCT	106534087	8699	59.8
				ACA CCA CAG GTA A
AJ_P1	85652838	E02	AJ_50	/5AmMC6/CCC AAG ACT GCA AGC	106534088	8690	60.6
				TCA CTA CAT TGA A
AJ_P1	85652839	E03	AJ_51	/5AmMC6/CCC AAG CTA CTC CTC	106534089	8690	58.9
				TAA GAG CAT AGA A
AJ_P1	85652840	E04	AJ_52	/5AmMC6/CCC AAT GGA ACG CTA	106534090	8779	60.9
				AGG TGT AAA CCA A
AJ_P1	85652841	E05	AJ_53	/5AmMC6/CCC AAG AAA CTA ACC	106534091	8690	61.5
				TTG GCT TGC CAA A
AJ_P1	85652842	E06	AJ_54	/5AmMC6/CCC AAC CAT TAG ACC	106534092	8672	61.3
				TTG TGT TGC CAA A
AJ_P1	85652843	E07	AJ_55	/5AmMC6/CCC AAG GTC TGA CAG	106534093	8777	61.7
				TAG GTG TTC CAA A
AJ_P1	85652844	E08	AJ_56	/5AmMC6/CCC AAT TTC GCA AGC	106534094	8696	59.7
				CTT GGT ACA TAA A
AJ_P1	85652845	E09	AJ_57	/5AmMC6/CCC AAG TTT CTA GCC	106534095	8657	61.2
				TAC CAC TAC GGA A
AJ_P1	85652846	E10	AJ_58	/5AmMC6/CCC AAA TAG ACC TAA	106534096	8779	60.1
				CGG AAG CTG TGA A
AJ_P1	85652847	E11	AJ_59	/5AmMC6/CCC AAG GAG TCA TCC	106534097	8712	60.7
				ATG CAT CTT TGA A
AJ_P1	85652848	E12	AJ_60	/5AmMC6/CCC AAC CGT ACT AGC	106534098	8721	60.5
				TTG GGT TAA ACA A
AJ_P1	85652849	F01	AJ_61	/5AmMC6/CCC AAA AAG GCT AGC	106534099	8696	59
				CTT CTG ACT TTA A
AJ_P1	85652850	F02	AJ_62	/5AmMC6/CCC AAA GAG CTC TGC	106534100	8705	58.8
				ACT ACA AGT TTA A
AJ_P1	85652851	F03	AJ_63	/5AmMC6/CCC AAC AGC TAA CGG	106534101	8779	60
				TAG TAA AGG TCA A
AJ_P1	85652852	F04	AJ_64	/5AmMC6/CCC AAA GCT TTC CGT TTC	106534102	8696	60
				AAA GTG ACA A
AJ_P1	85652853	F05	AJ_65	/5AmMC6/CCC AAG TCC ATG CTT	106534103	8690	61.3
				CCA GTG ACA AAA A
AJ_P1	85652854	F06	AJ_66	/5AmMC6/CCC AAG TAG CTT TGC	106534104	8687	58.4
				TCT ACT CGT AAA A
AJ_P1	85652855	F07	AJ_67	/5AmMC6/CCC AAC TTC GAA CTA	106534105	8779	59.7
				AGG AGT AGA GCA A
AJ_P1	85652856	F08	AJ_68	/5AmMC6/CCC AAT TCA GTC CTA	106534106	8770	58.5
				GAG GAG AGA CTA A
AJ_P1	85652857	F09	AJ_69	/5AmMC6/CCC AAT AGG TCT GTC	106534107	8672	59.6
				TTA CCC AAC GTA A
AJ_P1	85652858	F10	AJ_70	/5AmMC6/CCC AAC GTG AGG AAA	106534108	8770	60.7
				GTT CTG CTA ACA A
AJ_P1	85652859	F11	AJ_71	/5AmMC6/CCC AAG TTG GCA ACT	106534109	8712	60.7
				TGC TCT CTA AGA A
AJ_P1	85652860	F12	AJ_72	/5AmMC6/CCC AAG ACA TCT CTC	106534110	8690	59
				TCA GAG CTA GAA A
AJ_P1	85652861	G01	AJ_73	/5AmMC6/CCC AAT TTC GCA TGT CTC	106534111	8672	60.9
				ATC AGG ACA A
AJ_P1	85652862	G02	AJ_74	/5AmMC6/CCC AAA GCC TTC CTT	106534112	8721	60.6
				GGT ACT GAA AGA A
AJ_P1	85652863	G03	AJ_75	/5AmMC6/CCC AAA CTT GCC TTG	106534113	8696	59.9
				CGT ACT GTA AAA A
AJ_P1	85652864	G04	AJ_76	/5AmMC6/CCC AAC ACT TTG TAC	106534114	8761	59.7
				GGT AGA GAC GTA A
AJ_P1	85652865	G05	AJ_77	/5AmMC6/CCC AAG TTT CCA TCA	106534115	8681	61.2
				ACC GAA GCT TGA A
AJ_P1	85652866	G06	AJ_78	/5AmMC6/CCC AAG CAT TAC CAA	106534116	8699	61
				ACT GGA ACC TGA A
AJ_P1	85652867	G07	AJ_79	/5AmMC6/CCC AAC CGT ACA ACT	106534117	8687	59.7
				TGT TCG TTT GAA A
AJ_P1	85652868	G08	AJ_80	/5AmMC6/CCC AAC AGC TAG TAG	106534118	8690	60.7
				CAC ACC ATT TGA A
AJ_P1	85652869	G09	AJ_81	/5AmMC6/CCC AAC CTC ACG AAA	106534119	8690	61.2
				GCA TCA TTG TGA A
AJ_P1	85652870	G10	AJ_82	/5AmMC6/CCC AAA CAA AGT GAG	106534120	8739	60.5
				GTC ATC TCG ACA A
AJ_P1	85652871	G11	AJ_83	/5AmMC6/CCC AAG AAA CCT TCT	106534121	8721	59.9
				TGT AGG ACT CGA A
AJ_P1	85652872	G12	AJ_84	/5AmMC6/CCC AAA AGC CTA AGC	106534122	8696	58.9
				TCT GTC AGT TTA A
AJ_P1	85652873	H01	AJ_85	/5AmMC6/CCC AAA CGT TCC CTT	106534123	8681	60.9
				CAT GTC GAA AGA A
AJ_P1	85652874	H02	AJ_86	/5AmMC6/CCC AAG TAG CAC TGA	106534124	8699	60.7
				CAC CAA GCA TTA A
AJ_P1	85652875	H03	AJ_87	/5AmMC6/CCC AAG TTT GAC TCC	106534125	8657	62.2
				AAG CCT ACG TCA A
AJ_P1	85652876	H04	AJ_88	/5AmMC6/CCC AAA CCG TTG GTG	106534126	8770	61.2
				AAG CCT TAA AGA A
AJ_P1	85652877	H05	AJ_89	/5AmMC6/CCC AAG CCT ACA CCT	106534127	8675	61.9
				TCA GTG AAC AGA A
AJ_P1	85652878	H06	AJ_90	/5AmMC6/CCC AAC AGC TCA AGC	106534128	8723	59.2
				AGT TAG TAA ACA A
AJ_P1	85652879	H07	AJ_91	/5AmMC6/CCC AAT ACG CAA GCA	106534129	8745	59.3
				TGT AGG TTT ACA A
AJ_P1	85652880	H08	AJ_92	/5AmMC6/CCC AAC ACG AGT CGT	106534130	8727	59.3
				TAG TTG TTT CAA A
AJ_P1	85652881	H09	AJ_93	/5AmMC6/CCC AAT TCG GAA GAC	106534131	8690	59.6
				CTA CTA ACC TGA A
AJ_P1	85652882	H10	AJ_94	/5AmMC6/CCC AAA GGT CTC TAC	106534132	8763	58.2
				GAA AGG AAC ATA A
AJ_P1	85652883	H11	AJ_95	/5AmMC6/CCC AAG TGC TAG ACG	106534133	8761	60.6
				TCT GTG TCA AAA A
AJ_P1	85652884	H12	AJ_96	/5AmMC6/CCC AAA CCA GTG GAC	106534134	8657	61
				TTC TCT CCT AGA A
AJ_P2	85652886	A01	AJ_97	/5AmMC6/CCC AAC ATG TAG GAG	106534135	8746	61.3
				ACG TAG TTC CCA A
AJ_P2	85652887	A02	AJ_98	/5AmMC6/CCC AAG AAC TCT CTG	106534136	8752	60.2
				GTT AGG CTT GAA A
AJ_P2	85652888	A03	AJ_99	/5AmMC6/CCC AAG GAC ATC CAC	106534137	8675	62.1
				ATC GTC TGA CAA A
AJ_P2	85652889	A04	AJ_100	/5AmMC6/CCC AAA CTT GTT GGG	106534138	8736	59.8
				TTC AGC TAA CAA A
AJ_P2	85652890	A05	AJ_101	/5AmMC6/CCC AAC ACG TGT CCT	106534139	8681	61.2
				GTC ATG TCA AAA A
AJ_P2	85652891	A06	AJ_102	/5AmMC6/CCC AAT CGG AAA CCA	106534140	8705	59.4
				ACG TTA GCT TTA A
AJ_P2	85652892	A07	AJ_103	/5AmMC6/CCC AAG GAC TTA GGT	106534141	8777	61.4
				ACC TGT TCG GAA A
AJ_P2	85652893	A08	AJ_104	/5AmMC6/CCC AAG ACT TAA CAA	106534142	8739	60.1
				CCT GTG ACG AGA A
AJ_P2	85652894	A09	AJ_105	/5AmMC6/CCC AAG TTA ACA TGC	106534143	8779	60.7
				AGA CGA ACG GTA A
AJ_P2	85652895	A10	AJ_106	/5AmMC6/CCC AAG CGT ACA ACT	106534144	8687	58.9
				CTT GTC AGT TTA A
AJ_P2	85652896	A11	AJ_107	/5AmMC6/CCC AAG TAA CAC CTT	106534145	8746	61.9
				CTG AGC AGT GGA A
AJ_P2	85652897	A12	AJ_108	/5AmMC6/CCC AAG ACC TAC CTC	106534146	8675	60.7
				TCA GGA ACA GTA A
AJ_P2	85652898	B01	AJ_109	/5AmMC6/CCC AAA CCT GAC CTT	106534147	8739	59.9
				AGG AAG AGC ATA A
AJ_P2	85652899	B02	AJ_110	/5AmMC6/CCC AAC AAA GTT TGT	106534148	8736	59.3
				CTC AGT TAG CGA A
AJ_P2	85652900	B03	AJ_111	/5AmMC6/CCC AAC GGT AGC ATT	106534149	8752	60.5
				GTT CCT GTA GAA A
AJ_P2	85652901	B04	AJ_112	/5AmMC6/CCC AAC TAG GTT TGT TCT	106534150	8727	58
				AGA CAG CTA A
AJ_P2	85652902	B05	AJ_113	/5AmMC6/CCC AAG TCT CTA CGT	106534151	8681	59.8
				TCC ATC GAA AGA A
AJ_P2	85652903	B06	AJ_114	/5AmMC6/CCC AAA CCT TCG TTC TTG	106534152	8672	60.7
				AGT ACA GCA A
AJ_P2	85652904	B07	AJ_115	/5AmMC6/CCC AAG AAC ACT CCT	106534153	8666	62.2
				CAT GTG ACT GCA A
AJ_P2	85652905	B08	AJ_116	/5AmMC6/CCC AAA CGC TTG GTA	106534154	8763	59.3
				ACA AAG ACA GTA A
AJ_P2	85652906	B09	AJ_117	/5AmMC6/CCC AAC CCT AGA GTA	106534155	8721	58.4
				GTA CTA CGG TTA A
AJ_P2	85652907	B10	AJ_118	/5AmMC6/CCC AAC CTG AGG TAG	106534156	8779	60.3
				TGA CTG AAA CAA A
AJ_P2	85652908	B11	AJ_119	/5AmMC6/CCC AAG CTA CGA ACT	106534157	8727	59.7
				TGG TTG TTT CAA A
AJ_P2	85652909	B12	AJ_120	/5AmMC6/CCC AAG CAA GTC CTA	106534158	8752	60.8
				GGT TGT GTT CAA A
AJ_P2	85652910	C01	AJ_121	/5AmMC6/CCC AAC TCC ATG TCA	106534159	8755	61.9
				AGG AAG GGT ACA A
AJ_P2	85652911	C02	AJ_122	/5AmMC6/CCC AAT CCG AAC ACG	106534160	8723	58.7
				AAG TAC AAG TTA A
AJ_P2	85652912	C03	AJ_123	/5AmMC6/CCC AAC ACG TTG ACA	106534161	8727	60.1
				TTG TTG GCT TAA A
AJ_P2	85652913	C04	AJ_124	/5AmMC6/CCC AAC CTC TAG GAA	106534162	8675	60.9
				CGT AGT ACA CCA A
AJ_P2	85652914	C05	AJ_125	/5AmMC6/CCC AAT AGG ACA CCA	106534163	8699	60.3
				CAG TTC ATC GAA A
AJ_P2	85652915	C06	AJ_126	/5AmMC6/CCC AAA TGT CGT TCG	106534164	8736	59.7
				GTT AGC TCA AAA A
AJ_P2	85652916	C07	AJ_127	/5AmMC6/CCC AAA TCG GTT GTG	106534165	8736	59.4
				TCT AGC TCA AAA A
AJ_P2	85652917	C08	AJ_128	/5AmMC6/CCC AAT AAG AAC GAA	106534166	8723	59.3
				ACG TAC CTT GCA A
AJ_P2	85652918	C09	AJ_129	/5AmMC6/CCC AAT CGC AAG AAC	106534167	8723	59.7
				CGT TAG TCA AAA A
AJ_P2	85652919	C10	AJ_130	/5AmMC6/CCC AAG TCA CAC GTC	106534168	8657	61.9
				TCC ACA GGT TTA A
AJ_P2	85652920	C11	AJ_131	/5AmMC6/CCC AAG AGC TTA CAT	106534169	8752	59.4
				CGT TCT AGG GTA A
AJ_P2	85652921	C12	AJ_132	/5AmMC6/CCC AAG ACC TTC TCC	106534170	8697	61
				TTG ACA GAG GTA A
AJ_P2	85652922	D01	AJ_133	/5AmMC6/CCC AAA AGG CTT AGC	106534171	8687	58.6
				TCT CTT TAC TGA A
AJ_P2	85652923	D02	AJ_134	/5AmMC6/CCC AAG TCG TAA CAG	106534172	8755	61.9
				AGG TGT CCA CAA A
AJ_P2	85652924	D03	AJ_135	/5AmMC6/CCC AAA CTA CTG CAA	106534173	8761	60.5
				GTG GTA GGT TCA A
AJ_P2	85652925	D04	AJ_136	/5AmMC6/CCC AAT TTC GGA ACC	106534174	8746	62.3
				AGT ACC ATG GGA A
AJ_P2	85652926	D05	AJ_137	/5AmMC6/CCC AAT CGA GAA GCA	106534175	8705	59.1
				ACT TCC TTG TAA A
AJ_P2	85652927	D06	AJ_138	/5AmMC6/CCC AAT GGA GAC TTC	106534176	8752	60.3
				CGT ACT GTT GAA A
AJ_P2	85652928	D07	AJ_139	/5AmMC6/CCC AAA CAT GCG TTT	106534177	8687	59.6
				CGT AGT CTT CAA A
AJ_P2	85652929	D08	AJ_140	/5AmMC6/CCC AAG AAC CTC AGC	106534178	8690	60.4
				TCT TTC GAA AGA A
AJ_P2	85652930	D09	AJ_141	/5AmMC6/CCC AAG TCC TTA AGC	106534179	8752	59.7
				TGT TCG AGA GTA A
AJ_P2	85652931	D10	AJ_142	/5AmMC6/CCC AAT CTC GAA ACT	106534180	8672	60.5
				CTT GTG TGA CCA A
AJ_P2	85652932	D11	AJ_143	/5AmMC6/CCC AAC CAT TAG AGG	106534181	8763	57.7
				AAC TAA GAG CTA A
AJ_P2	85652933	D12	AJ_144	/5AmMC6/CCC AAC CCT AGA GTG	106534182	8755	60.7
				AGT CAG GAA CTA A
AJ_P2	85652934	E01	AJ_145	/5AmMC6/CCC AAT GAA CCA TAA	106534183	8763	59.1
				GAG CAA CGG TTA A
AJ_P2	85652935	E02	AJ_146	/5AmMC6/CCC AAG AAC CTT CCC	106534184	8672	60.3
				TTA GTC GTT GAA A
AJ_P2	85652936	E03	AJ_147	/5AmMC6/CCC AAG TGG TCA GTA	106534185	8697	62.1
				ACC CTT TCC GAA A
AJ_P2	85652937	E04	AJ_148	/5AmMC6/CCC AAA GCA TGT ACG	106534186	8681	59.3
				TCT CCT ACT AGA A
AJ_P2	85652938	E05	AJ_149	/5AmMC6/CCC AAG GAC TTC ACC	106534187	8666	61.9
				TAC GTT CGA ACA A
AJ_P2	85652939	E06	AJ_150	/5AmMC6/CCC AAC GAA CTT TAC	106534188	8672	60.7
				CTT GTC CAT GGA A
AJ_P2	85652940	E07	AJ_151	/5AmMC6/CCC AAC AGG TTC TTA	106534189	8690	61.1
				CGC AAC ACA TGA A
AJ_P2	85652941	E08	AJ_152	/5AmMC6/CCC AAC TTG TTA GGG	106534190	8752	60.1
				TAG CTG ACT CAA A
AJ_P2	85652942	E09	AJ_153	/5AmMC6/CCC AAC TGG AGA AGA	106534191	8770	59.2
				GAC TAC CTG TTA A
AJ_P2	85652943	E10	AJ_154	/5AmMC6/CCC AAC TAA GGT TTG	106534192	8752	60.3
				GTC AGT CCT GAA A
AJ_P2	85652944	E11	AJ_155	/5AmMC6/CCC AAG CAC ACT AGC	106534193	8690	60.7
				CTT TCT GAA AGA A
AJ_P2	85652945	E12	AJ_156	/5AmMC6/CCC AAG TCC TGA CGA	106534194	8777	61.6
				GAG TTT GGT ACA A
AJ_P2	85652946	F01	AJ_157	/5AmMC6/CCC AAT CCC AAG AGT	106534195	8697	61.8
				CTC TGG TTG ACA A
AJ_P2	85652947	F02	AJ_158	/5AmMC6/CCC AAG GCA TTC AGC	106534196	8687	59.8
				ATT CAT TCT TGA A
AJ_P2	85652948	F03	AJ_159	/5AmMC6/CCC AAG TTT GAC TAC	106534197	8690	61.3
				CAA GCA ACT GCA A
AJ_P2	85652949	F04	AJ_160	/5AmMC6/CCC AAC CTT AAG CTA	106534198	8770	60
				AGT GTG AGA CGA A
AJ_P2	85652950	F05	AJ_161	/5AmMC6/CCC AAC TTA CAG CTA	106534199	8736	59.2
				GTT TGA AGT GCA A
AJ_P2	85652951	F06	AJ_162	/5AmMC6/CCC AAC TAG TCT CTT	106534200	8727	58.3
				AGA GTT TGG CAA A
AJ_P2	85652952	F07	AJ_163	/5AmMC6/CCC AAT AAA GCT CTA	106534201	8763	58
				GGA GAA CAC GTA A
AJ_P2	85652953	F08	AJ_164	/5AmMC6/CCC AAA GCG TAG TAG	106534202	8779	59.8
				TGA CTA ACG ACA A
AJ_P2	85652954	F09	AJ_165	/5AmMC6/CCC AAG ACG TAA ACG	106534203	8681	59.9
				CTT CCT TCT AGA A
AJ_P2	85652955	F10	AJ_166	/5AmMC6/CCC AAA GCT GTA GTA	106534204	8672	59.5
				CCC TTT CCT AGA A
AJ_P2	85652956	F11	AJ_167	/5AmMC6/CCC AAC TCG TAC AGC	106534205	8699	59.3
				ATA CCT AGA AGA A
AJ_P2	85652957	F12	AJ_168	/5AmMC6/CCC AAT CGC TAC ATA	106534206	8723	58.9
				GCA ACT GAA AGA A
AJ_P2	85652958	G01	AJ_169	/5AmMC6/CCC AAC TTG GCA ACG	106534207	8761	61
				TGT GTA GTA CAA A
AJ_P2	85652959	G02	AJ_170	/5AmMC6/CCC AAA CCT GTT ACG	106534208	8696	59.9
				CTT GTG CTA AAA A
AJ_P2	85652960	G03	AJ_171	/5AmMC6/CCC AAA GCT TGG TTG	106534209	8727	59.4
				TAA CTT TAC CGA A
AJ_P2	85652961	G04	AJ_172	/5AmMC6/CCC AAG AGA CCT TAG	106534210	8699	60.5
				CAA CAA CCT TGA A
AJ_P2	85652962	G05	AJ_173	/5AmMC6/CCC AAT ACC GAA GAG	106534211	8761	60.1
				TGC TAG GTT TCA A
AJ_P2	85652963	G06	AJ_174	/5AmMC6/CCC AAG ACA TAG TAC	106534212	8666	61.6
				CGT TGC TAC CCA A
AJ_P2	85652964	G07	AJ_175	/5AmMC6/CCC AAG GTC TAG TAA	106534213	8739	59.7
				CGA AGC AAC CTA A
AJ_P2	85652965	G08	AJ_176	/5AmMC6/CCC AAT AAG CAA CAA	106534214	8723	60.1
				AGG TCA TTG CCA A
AJ_P2	85652966	G09	AJ_177	/5AmMC6/CCC AAC TGA GTG AGA	106534215	8779	59.5
				AGT CAG AAC CTA A
AJ_P2	85652967	G10	AJ_178	/5AmMC6/CCC AAC TTC GAG TGA	106534216	8723	58.8
				AAC AAG AAC CTA A
AJ_P2	85652968	G11	AJ_179	/5AmMC6/CCC AAA GCG TTC ATG	106534217	8727	59.4
				GTT CTG TCA TAA A
AJ_P2	85652969	G12	AJ_180	/5AmMC6/CCC AAG AGG TCT AGG	106534218	8752	59.7
				CTT TCG TCT AAA A
AJ_P2	85652970	H01	AJ_181	/5AmMC6/CCC AAA GCC ATT AGT	106534219	8712	60.7
				CGT GTC GTT ACA A
AJ_P2	85652971	H02	AJ_182	/5AmMC6/CCC AAG GTC TTA CGT	106534220	8777	61.9
				AGG TTG AAG CCA A
AJ_P2	85652972	H03	AJ_183	/5AmMC6/CCC AAG AGC TTA GCG	106534221	8739	60.2
				AAC TTA GAA CCA A
AJ_P2	85652973	H04	AJ_184	/5AmMC6/CCC AAT GGA ACC CTA	106534222	8777	61.9
				GGG TTG AGT TCA A
AJ_P2	85652974	H05	AJ_185	/5AmMC6/CCC AAG AAC ACT TGA	106534223	8730	61
				GCA GAC GTT TCA A
AJ_P2	85652975	H06	AJ_186	/5AmMC6/CCC AAT CGA AGG AAA	106534224	8763	58.8
				GCA TGA CTC TAA A
AJ_P2	85652976	H07	AJ_187	/5AmMC6/CCC AAC TTA GTG AGA	106534225	8761	59.3
				GTG CTA CTC AGA A
AJ_P2	85652977	H08	AJ_188	/5AmMC6/CCC AAA CTT GTT GAA	106534226	8736	59.7
				GTG CTT CAC AGA A
AJ_P2	85652978	H09	AJ_189	/5AmMC6/CCC AAG TGC TAA CAC	106534227	8672	60.5
				TGT TCT CCA TGA A
AJ_P2	85652979	H10	AJ_190	/5AmMC6/CCC AAC CCT TAG ACC	106534228	8666	61.7
				TGA ACA TCG TGA A
AJ_P2	85652980	H11	AJ_191	/5AmMC6/CCC AAC TTA AAG GGT	106534229	8770	59.2
				AGA CCT AGT CGA A
AJ_P2	85652981	H12	AJ_192	/5AmMC6/CCC AAG GCA TAG ACC	106534230	8712	60.1
				TGT CGT TCT TAA A
AJ_P3	85652983	A01	AJ_193	/5AmMC6/CCC AAA GCG TTT CTA	106534231	8761	60.1
				GGG TAG TAA CCA A
AJ_P3	85652984	A02	AJ_194	/5AmMC6/CCC AAG CAA ACT TTC	106534232	8705	59.7
				CAA GAC GTT GTA A
AJ_P3	85652985	A03	AJ_195	/5AmMC6/CCC AAT CTG GTA ACT	106534233	8672	60.8
				GCT TTC GAA CCA A
AJ_P3	85652986	A04	AJ_196	/5AmMC6/CCC AAT CAG GAG AGC	106534234	8779	59.4
				AAG TAC TAG TCA A
AJ_P3	85652987	A05	AJ_197	/5AmMC6/CCC AAA CAT TGT GTC	106534235	8687	59.9
				GTT AAC GCT TCA A
AJ_P3	85652988	A06	AJ_198	/5AmMC6/CCC AAG AGG TAC TTA	106534236	8770	59.5
				GGC ATA ACC GTA A
AJ_P3	85652989	A07	AJ_199	/5AmMC6/CCC AAA AAC GGT TTG	106534237	8739	62.2
				GCA AAC TGA CCA A
AJ_P3	85652990	A08	AJ_200	/5AmMC6/CCC AAC ATA AGG CAA	106534238	8755	62.1
				GGG TAC TGT CCA A
AJ_P3	85652991	A09	AJ_201	/5AmMC6/CCC AAA TGA CGA CAG	106534239	8795	61.6
				GAG TAG TGT CCA A
AJ_P3	85652992	A10	AJ_202	/5AmMC6/CCC AAG ACC TTT GCG	106534240	8712	60.4
				TTT ACA GGA CTA A
AJ_P3	85652993	A11	AJ_203	/5AmMC6/CCC AAG TCT AGA GTC	106534241	8699	59.6
				AAC ACA GCA CTA A
AJ_P3	85652994	A12	AJ_204	/5AmMC6/CCC AAG AGA GCT TAA	106534242	8715	61.5
				CCA GAC TGT CCA A
AJ_P3	85652995	B01	AJ_205	/5AmMC6/CCC AAG ACC ATA CTG	106534243	8690	60.1
				CAC ATT AGG CTA A
AJ_P3	85652996	B02	AJ_206	/5AmMC6/CCC AAG CCA ACT ACG	106534244	8706	61.8
				TCA TAG TGG TCA A
AJ_P3	85652997	B03	AJ_207	/5AmMC6/CCC AAT GTC GAA CGT	106534245	8699	60.2
				ACC AAG ACC ATA A
AJ_P3	85652998	B04	AJ_208	/5AmMC6/CCC AAC GTG TAG GAA	106534246	8761	60
				GTT CGT ACT CAA A
AJ_P3	85652999	B05	AJ_209	/5AmMC6/CCC AAA AAC CGT AAG	106534247	8730	61.3
				CCT TCA TGG TGA A
AJ_P3	85653000	B06	AJ_210	/5AmMC6/CCC AAT CGG AAA CGC	106534248	8745	59.7
				AAG TTC ATG TTA A
AJ_P3	85653001	B07	AJ_211	/5AmMC6/CCC AAT CGG TAA CTA	106534249	8763	58.3
				GAA AGC ACA GTA A
AJ_P3	85653002	B08	AJ_212	/5AmMC6/CCC AAG TCG AAG TAG	106534250	8779	60.1
				GCT AAA GTC CAA A
AJ_P3	85653003	B09	AJ_213	/5AmMC6/CCC AAA CGG TAG TAC	106534251	8712	59.8
				CTT GTC GTC ATA A
AJ_P3	85653004	B10	AJ_214	/5AmMC6/CCC AAC ATT TGG AAG	106534252	8727	59.6
				TTG CAT CCT GTA A
AJ_P3	85653005	B11	AJ_215	/5AmMC6/CCC AAC GAA GTG TTG	106534253	8746	62.6
				GTC AAG TCC ACA A
AJ_P3	85653006	B12	AJ_216	/5AmMC6/CCC AAT CAA GGA AAG	106534254	8779	60.5
				GAC TAG TTC GCA A
AJ_P3	85653007	C01	AJ_217	/5AmMC6/CCC AAC GAA ACT TAC	106534255	8723	58.3
				AAC GTA GGA CTA A
AJ_P3	85653008	C02	AJ_218	/5AmMC6/CCC AAG GCA TGC TTA	106534256	8727	59
				GTC TGA ACT TTA A
AJ_P3	85653009	C03	AJ_219	/5AmMC6/CCC AAG AAC CGT TCC	106534257	8672	60.5
				CAT GTA GCT TTA A
AJ_P3	85653010	C04	AJ_220	/5AmMC6/CCC AAG GCA TAA AGT	106534258	8745	59.1
				GTT CTC TCG AAA A
AJ_P3	85653011	C05	AJ_221	/5AmMC6/CCC AAG GCT ACC CTT	106534259	8739	59.6
				AAA GAG GAC ATA A
AJ_P3	85653012	C06	AJ_222	/5AmMC6/CCC AAG TCC TAG ACT	106534260	8712	59.8
				TCG GTT CGT AAA A
AJ_P3	85653013	C07	AJ_223	/5AmMC6/CCC AAG GAA CCT TGT	106534261	8699	60.2
				ACA ACA CGA CTA A
AJ_P3	85653014	C08	AJ_224	/5AmMC6/CCC AAC ACG TTG TAG	106534262	8779	59.3
				AGA CAG AGA CTA A
AJ_P3	85653015	C09	AJ_225	/5AmMC6/CCC AAT CCA AGC ACA	106534263	8730	61
				AGG TAG GTT TCA A
AJ_P3	85653016	C10	AJ_226	/5AmMC6/CCC AAA GCC ATA CTA	106534264	8736	59
				GTT GTT GTC GAA A
AJ_P3	85653017	C11	AJ_227	/5AmMC6/CCC AAC GAG TAC CAT	106534265	8779	59.2
				AGT GAA GGA CTA A
AJ_P3	85653018	C12	AJ_228	/5AmMC6/CCC AAC ATT TGC CAA	106534266	8770	60.3
				GGG TAG AGA CTA A
AJ_P3	85653019	D01	AJ_229	/5AmMC6/CCC AAC GAC TGT TTC	106534267	8687	59.3
				CGT AAA GCT TTA A
AJ_P3	85653020	D02	AJ_230	/5AmMC6/CCC AAG GAG TAC GAG	106534268	8779	59.7
				ACA TCA AGC TTA A
AJ_P3	85653021	D03	AJ_231	/5AmMC6/CCC AAT GGA CTG TCT	106534269	8777	61.6
				GGA GTA ACG TCA A
AJ_P3	85653022	D04	AJ_232	/5AmMC6/CCC AAA CCG TTA CAG	106534270	8752	60.5
				GTT TAG TGT CGA A
AJ_P3	85653023	D05	AJ_233	/5AmMC6/CCC AAT GAC AAA GAG	106534271	8763	58.6
				TAC GAA CTG CTA A
AJ_P3	85653024	D06	AJ_234	/5AmMC6/CCC AAT CAC AAG TGA	106534272	8723	59
				CAA AGT ACG CTA A
AJ_P3	85653025	D07	AJ_235	/5AmMC6/CCC AAC TGT AAA GAG	106534273	8736	58.1
				TTG CTA GCT CTA A
AJ_P3	85653026	D08	AJ_236	/5AmMC6/CCC AAT GGG AAC ACT	106534274	8795	62.3
				GTG AAG TCG ACA A
AJ_P3	85653027	D09	AJ_237	/5AmMC6/CCC AAA TTG CGT TTG	106534275	8752	61.9
				GTC AAC TGG ACA A
AJ_P3	85653028	D10	AJ_238	/5AmMC6/CCC AAC GAA GGT TCA	106534276	8746	62
				GGT TAG TCC ACA A
AJ_P3	85653029	D11	AJ_239	/5AmMC6/CCC AAA TGC TGT GTT	106534277	8687	59.7
				AAC CTT TAG CCA A
AJ_P3	85653030	D12	AJ_240	/5AmMC6/CCC AAC CAC TTG TAG	106534278	8712	59.5
				TAC TAG GTT CGA A
AJ_P3	85653031	E01	AJ_241	/5AmMC6/CCC AAC CCA TAG AGG	106534279	8712	60.3
				TTT CAC GTT GTA A
AJ_P3	85653032	E02	AJ_242	/5AmMC6/CCC AAC TAG GAA AGA	106534280	8763	58.7
				GTT CAA CGC ATA A
AJ_P3	85653033	E03	AJ_243	/5AmMC6/CCC AAT CCG AAG AAA	106534281	8779	59.6
				GGT CTA CAG GTA A
AJ_P3	85653034	E04	AJ_244	/5AmMC6/CCC AAT GGA AAC CCT	106534282	8714	58.9
				TAA GAA CTG CTA A
AJ_P3	85653035	E05	AJ_245	/5AmMC6/CCC AAG CAA CAT AAC	106534283	8699	60.5
				CTT GAC TCA GGA A
AJ_P3	85653036	E06	AJ_246	/5AmMC6/CCC AAT AGA ACC ACA	106534284	8723	58.4
				GAC TTT AGC AGA A
AJ_P3	85653037	E07	AJ_247	/5AmMC6/CCC AAT CAC AAG AGG	106534285	8763	59.1
				TTC GTA CGA AAA A
AJ_P3	85653038	E08	AJ_248	/5AmMC6/CCC AAA GCT TTG TCT	106534286	8705	59.3
				CCA GTA CGA AAA A
AJ_P3	85653039	E09	AJ_249	/5AmMC6/CCC AAT CGG AAG GTG	106534287	8770	60.7
				TTC AGT AAA CCA A
AJ_P3	85653040	E10	AJ_250	/5AmMC6/CCC AAA GTG CAT TCC	106534288	8723	59.4
				AAG AAA CGA CTA A
AJ_P3	85653041	E11	AJ_251	/5AmMC6/CCC AAG ACG TAA CCA	106534289	8690	60.1
				TCG AAC TCG TTA A
AJ_P3	85653042	E12	AJ_252	/5AmMC6/CCC AAC CGT AGA ACG	106534290	8687	59.3
				TTC TTT GCT TAA A
AJ_P3	85653043	F01	AJ_253	/5AmMC6/CCC AAG AGC TCA AGG	106534291	8755	61.6
				GTT CTA GAA CCA A
AJ_P3	85653044	F02	AJ_254	/5AmMC6/CCC AAT CGG TAG TTA	106534292	8770	60
				CGA GTA AAG CCA A
AJ_P3	85653045	F03	AJ_255	/5AmMC6/CCC AAG ACA ACT AGC	106534293	8666	61.5
				TCT TGG ACT CCA A
AJ_P3	85653046	F04	AJ_256	/5AmMC6/CCC AAT GAC GAA GGA	106534294	8739	59.5
				CAC TTA GAC CTA A
AJ_P3	85653047	F05	AJ_257	/5AmMC6/CCC AAC CGT AGA ACA	106534295	8714	59.1
				TTT GAA GCC ATA A
AJ_P3	85653048	F06	AJ_258	/5AmMC6/CCC AAC CAC TCG AAC	106534296	8675	62.2
				ATG GTA ACG TCA A
AJ_P3	85653049	F07	AJ_259	/5AmMC6/CCC AAT CGA ACC GTA	106534297	8690	60.7
				ACC ATT TCA GGA A
AJ_P3	85653050	F08	AJ_260	/5AmMC6/CCC AAC TAG TGG TTG	106534298	8761	60.5
				GAA CAT GCA CTA A
AJ_P3	85653051	F09	AJ_261	/5AmMC6/CCC AAG TGC TTA CTG	106534299	8721	60.8
				TCC ATC GGA AAA A
AJ_P3	85653052	F10	AJ_262	/5AmMC6/CCC AAT GAG TCT GCA	106534300	8687	58.8
				TCT CTT TCA AGA A
AJ_P3	85653053	F11	AJ_263	/5AmMC6/CCC AAT AGG ACA AAG	106534301	8739	59.8
				ACG TCT TAC CGA A
AJ_P3	85653054	F12	AJ_264	/5AmMC6/CCC AAT CAT AGG CTA	106534302	8779	59.3
				AGG GAA GAC CTA A
AJ_P3	85653055	G01	AJ_265	/5AmMC6/CCC AAC AGA GGT AAA	106534303	8795	61.7
				GTC CAG TGG TCA A
AJ_P3	85653056	G02	AJ_266	/5AmMC6/CCC AAG ACC ACT ACA	106534304	8690	60.7
				ACG TTG CAT GTA A
AJ_P3	85653057	G03	AJ_267	/5AmMC6/CCC AAT AGA CCA CAA	106534305	8739	60.2
				GCA TCG TTA GGA A
AJ_P3	85653058	G04	AJ_268	/5AmMC6/CCC AAG TCA CTC ACC	106534306	8681	59.8
				TAA GTT CGG TAA A
AJ_P3	85653059	G05	AJ_269	/5AmMC6/CCC AAG CTT TCA AGT	106534307	8690	60.3
				ACC ACA CGA GTA A
AJ_P3	85653060	G06	AJ_270	/5AmMC6/CCC AAG TCA CAT CCT	106534308	8697	61.4
				CTA GGG TTC GAA A
AJ_P3	85653061	G07	AJ_271	/5AmMC6/CCC AAA AAC GTT CAT	106534309	8736	59.8
				TTG GTC TGA CGA A
AJ_P3	85653062	G08	AJ_272	/5AmMC6/CCC AAC TGT CCA TTC	106534310	8730	61.2
				GGA ACG TGA AAA A
AJ_P3	85653063	G09	AJ_273	/5AmMC6/CCC AAA GTT CTT TCT TCA	106534311	8727	59.1
				GCA AGG GTA A
AJ_P3	85653064	G10	AJ_274	/5AmMC6/CCC AAT AGT CCT GTC	106534312	8712	59.5
				GTT AGA ACC GTA A
AJ_P3	85653065	G11	AJ_275	/5AmMC6/CCC AAC GTA CAT CCC	106534313	8690	60.2
				TTA GAA ACG TGA A
AJ_P3	85653066	G12	AJ_276	/5AmMC6/CCC AAC GGT TCA GCA	106534314	8687	59.7
				CTT TAC ATT TGA A
AJ_P3	85653067	H01	AJ_277	/5AmMC6/CCC AAT GCG TAA ACT	106534315	8672	60.7
				CGT TGT CCT ACA A
AJ_P3	85653068	H02	AJ_278	/5AmMC6/CCC AAT CGG TAA ACC	106534316	8687	59.4
				TGT TTC GCT TAA A
AJ_P3	85653069	H03	AJ_279	/5AmMC6/CCC AAG TGC AAG CAC	106534317	8770	61.4
				AGG TGA CAT TTA A
AJ_P3	85653070	H04	AJ_280	/5AmMC6/CCC AAG GGT ACA GAC	106534318	8795	60.9
				GAG TAA CTC TGA A
AJ_P3	85653071	H05	AJ_281	/5AmMC6/CCC AAA CCC TAG TAG	106534319	8672	59.1
				TTC TAC TCG TGA A
AJ_P3	85653072	H06	AJ_282	/5AmMC6/CCC AAG TAA CCC TTC	106534320	8706	61
				CGT AGG ACA GTA A
AJ_P3	85653073	H07	AJ_283	/5AmMC6/CCC AAT TTA GTC ACT	106534321	8672	60.3
				CTG GTC AAC CGA A
AJ_P3	85653074	H08	AJ_284	/5AmMC6/CCC AAG TAC ACA ACC	106534322	8715	61.6
				TCT GGT AAC GGA A
AJ_P3	85653075	H09	AJ_285	/5AmMC6/CCC AAC ACA AGT TCA	106534323	8795	62.2
				GGT AGG AGT GCA A
AJ_P3	85653076	H10	AJ_286	/5AmMC6/CCC AAC TAA AGG TGT	106534324	8696	59.4
				TTA CGC TTC CAA A
AJ_P3	85653077	H11	AJ_287	/5AmMC6/CCC AAC TGA AGT TGG	106534325	8777	61.4
				TCT ACC TGA GGA A
AJ_P3	85653078	H12	AJ_288	/5AmMC6/CCC AAT GTC GTA AGT	106534326	8672	60.8
				TCC TCA ACT GCA A
AJ_P4	85653080	A01	AJ_289	/5AmMC6/CCC AAA CCT GAG ACC	106534327	8712	60.5
				TGT GTT TCG TAA A
AJ_P4	85653081	A02	AJ_290	/5AmMC6/CCC AAT AGG CTA GCT	106534328	8723	58.4
				CAA CCA TAA AGA A
AJ_P4	85653082	A03	AJ_291	/5AmMC6/CCC AAG TTG ACA ACG	106534329	8675	61.8
				CTA CCC TAG ACA A
AJ_P4	85653083	A04	AJ_292	/5AmMC6/CCC AAT CAC GAA GTG	106534330	8754	59.7
				AGC TTG TCA AAA A
AJ_P4	85653084	A05	AJ_293	/5AmMC6/CCC AAT GAA ACC GTA	106534331	8690	61.2
				ACT CAC TTG GCA A
AJ_P4	85653085	A06	AJ_294	/5AmMC6/CCC AAC TTA GCA CAA	106534332	8763	59.2
				AGT GTA GAA GCA A
AJ_P4	85653086	A07	AJ_295	/5AmMC6/CCC AAT GCG TAG AAC	106534333	8763	59.1
				CAT GTA CAA AGA A
AJ_P4	85653087	A08	AJ_296	/5AmMC6/CCC AAG AGT TGC TTC	106534334	8761	60.4
				GGT ACT CAA AGA A
AJ_P4	85653088	A09	AJ_297	/5AmMC6/CCC AAG CGT AGT TCG	106534335	8779	60.3
				GAA ACA CTA AGA A
AJ_P4	85653089	A10	AJ_298	/5AmMC6/CCC AAA AGA GTC TTA	106534336	8690	59.4
				CCG TAC TAC CGA A
AJ_P4	85653090	A11	AJ_299	/5AmMC6/CCC AAA AAC GGT AGG	106534337	8706	61.7
				TCT CTG ACT CCA A
AJ_P4	85653091	A12	AJ_300	/5AmMC6/CCC AAG GTC AGT TAA	106534338	8706	62.4
				GCC AAC CCT TGA A
AJ_P4	85653092	B01	AJ_301	/5AmMC6/CCC AAA CCA GTC TCT	106534339	8672	60.1
				CAG TTT ACG TGA A
AJ_P4	85653093	B02	AJ_302	/5AmMC6/CCC AAT AAG ACA AGG	106534340	8699	60.7
				ACT TCC ATG CCA A
AJ_P4	85653094	B03	AJ_303	/5AmMC6/CCC AAG TCG AGA ACA	106534341	8770	59.9
				TGG AAG TCC TTA A
AJ_P4	85653095	B04	AJ_304	/5AmMC6/CCC AAT GCA GAG AAA	106534342	8763	58.4
				GTA CAT ACC GTA A
AJ_P4	85653096	B05	AJ_305	/5AmMC6/CCC AAG TGC ACT TAA	106534343	8779	60.5
				GGA CAA CAG GTA A
AJ_P4	85653097	B06	AJ_306	/5AmMC6/CCC AAA CCT GTC TTA	106534344	8721	60.2
				AGG CAT ACG GTA A
AJ_P4	85653098	B07	AJ_307	/5AmMC6/CCC AAG TCT CTA AGT	106534345	8752	59.6
				AGG CAT GCT GTA A
AJ_P4	85653099	B08	AJ_308	/5AmMC6/CCC AAC GTC TGA CAT	106534346	8770	59.8
				TGG AGA GAA CTA A
AJ_P4	85653100	B09	AJ_309	/5AmMC6/CCC AAA AAG CTC ACG	106534347	8696	59.3
				TCT TGG TCT TAA A
AJ_P4	85653101	B10	AJ_310	/5AmMC6/CCC AAG GGT AAC AGA	106534348	8770	60
				CAC TTT AGC GTA A
AJ_P4	85653102	B11	AJ_311	/5AmMC6/CCC AAT GAC CTA CGA	106534349	8795	60.9
				GTG GAG AGT ACA A
AJ_P4	85653103	B12	AJ_312	/5AmMC6/CCC AAA GCT TGC GAA	106534350	8723	59.2
				ACC TAA CTA AGA A
AJ_P4	85653104	C01	AJ_313	/5AmMC6/CCC AAT GTC GAC AGA	106534351	8699	59.6
				CCA TAC CTA AGA A
AJ_P4	85653105	C02	AJ_314	/5AmMC6/CCC AAG GTC AAC AAG	106534352	8675	62.6
				CCA TAC GTT CCA A
AJ_P4	85653106	C03	AJ_315	/5AmMC6/CCC AAC TGG TTA CTA	106534353	8770	59.5
				CGA ACA GGA GTA A
AJ_P4	85653107	C04	AJ_316	/5AmMC6/CCC AAT AGA GAC GTT	106534354	8690	59.1
				ACT CCT AAC CGA A
AJ_P4	85653108	C05	AJ_317	/5AmMC6/CCC AAA GAC AGT TGA	106534355	8690	60.1
				CAC CTT AGC CTA A
AJ_P4	85653109	C06	AJ_318	/5AmMC6/CCC AAA TCG AGA GTT	106534356	8690	59.8
				ACA CCT TAC CGA A
AJ_P4	85653110	C07	AJ_319	/5AmMC6/CCC AAA CAG GTT TCC	106534357	8779	60.4
				AAG AAC TAG GGA A
AJ_P4	85653111	C08	AJ_320	/5AmMC6/CCC AAG ACA GGT AGG	106534358	8777	61.2
				TCT TGC TAG TCA A
AJ_P4	85653112	C09	AJ_321	/5AmMC6/CCC AAG GAG TCT CAA	106534359	8715	61.7
				CCG TTA ACC AGA A
AJ_P4	85653113	C10	AJ_322	/5AmMC6/CCC AAG AAA CGT ACG	106534360	8681	60.7
				CTT CTC CAT TGA A
AJ_P4	85653114	C11	AJ_323	/5AmMC6/CCC AAC TTA GGA AGC	106534361	8675	61.5
				ACT ACG TAC CCA A
AJ_P4	85653115	C12	AJ_324	/5AmMC6/CCC AAG TAA GCT ACG	106586457	8657	61.5
				TTC CTG TAC CCA A
AJ_P4	85653116	D01	AJ_325	/5AmMC6/CCC AAC CAA GTA AGT	106534363	8795	62.1
				GGA CAC TGG TGA A
AJ_P4	85653117	D02	AJ_326	/5AmMC6/CCC AAC TGT TTA CAG	106534364	8761	60
				AGG TCA GCA GTA A
AJ_P4	85653118	D03	AJ_327	/5AmMC6/CCC AAC ACG TCT TAA	106534365	8723	58.6
				AGC AGA GAA CTA A
AJ_P4	85653119	D04	AJ_328	/5AmMC6/CCC AAG AGG ACT GTC	106534366	8697	61.1
				CTA CTT CCA TGA A
AJ_P4	85653120	D05	AJ_329	/5AmMC6/CCC AAG AAC ATC TCC	106534367	8666	61.6
				ACT GGT CAC GTA A
AJ_P4	85653121	D06	AJ_330	/5AmMC6/CCC AAT GAA GCA ACA	106534368	8739	60.9
				AGT GGT ACT CCA A
AJ_P4	85653122	D07	AJ_331	/5AmMC6/CCC AAT CCG TAA CAG	106534369	8779	59.5
				TAG GAG AAC GTA A
AJ_P4	85653123	D08	AJ_332	/5AmMC6/CCC AAA CCG TAG GAA	106534370	8690	60
				CTA CCA TTC TGA A
AJ_P4	85653124	D09	AJ_333	/5AmMC6/CCC AAC CAG TTC GTT	106534371	8690	60.8
				CAA ACA GAC TGA A
AJ_P4	85653125	D10	AJ_334	/5AmMC6/CCC AAG TTA AAC ATC	106534372	8699	60.4
				CAG AGC TCA CGA A
AJ_P4	85653126	D11	AJ_335	/5AmMC6/CCC AAG TCA CAC AAC	106534373	8715	61.9
				CTA GAG CTT GGA A
AJ_P4	85653127	D12	AJ_336	/5AmMC6/CCC AAC ATG TTA GGG	106534374	8752	61
				TTA CCT TGG CAA A
AJ_P4	85653128	E01	AJ_337	/5AmMC6/CCC AAG TCA AAG GTA	106534375	8666	61.7
				CTC CAC TTC CGA A
AJ_P4	85653129	E02	AJ_338	/5AmMC6/CCC AAG TAG AAC GTC	106534376	8699	60
				AAC CAC TTA CGA A
AJ_P4	85653130	E03	AJ_339	/5AmMC6/CCC AAG GAG ACT TGT	106534377	8697	60.5
				CCT ACT CTA CGA A
AJ_P4	85653131	E04	AJ_340	/5AmMC6/CCC AAT TTC GTA GTA	106534378	8687	58.9
				CTC ACT TGC GAA A
AJ_P4	85653132	E05	AJ_341	/5AmMC6/CCC AAC CTT GTA CTA	106534379	8770	59.5
				GGA AGG AAG CTA A
AJ_P4	85653133	E06	AJ_342	/5AmMC6/CCC AAG TCG TAG TTG	106534380	8697	62.3
				TCA CAC TGC ACA A
AJ_P4	85653134	E07	AJ_343	/5AmMC6/CCC AAC GAA GTT ACG	106534381	8672	61
				TCT TTC ATG CCA A
AJ_P4	85653135	E08	AJ_344	/5AmMC6/CCC AAA AGG CAT AAG	106534382	8730	61.3
				GCT TGT CAT CCA A
AJ_P4	85653136	E09	AJ_345	/5AmMC6/CCC AAG TGT CCA TAC	106534383	8681	60.8
				GCT TTA CCG AAA A
AJ_P4	85653137	E10	AJ_346	/5AmMC6/CCC AAC GGT TGA CAC	106534384	8715	62.3
				CAG TTA CCA AGA A
AJ_P4	85653138	E11	AJ_347	/5AmMC6/CCC AAG TGT GCA ACC	106534385	8697	62.2
				AGT TAC TCC TGA A
AJ_P4	85653139	E12	AJ_348	/5AmMC6/CCC AAG CTG ACA GAC	106534386	8672	60.1
				TCT CTT TCA TGA A
AJ_P4	85653140	F01	AJ_349	/5AmMC6/CCC AAG AAA GCT GTA	106534387	8681	59.4
				CCC TTC TCT AGA A
AJ_P4	85653141	F02	AJ_350	/5AmMC6/CCC AAA TGT TGC TAC	106534388	8714	59
				AAG ACT AAC CGA A
AJ_P4	85653142	F03	AJ_351	/5AmMC6/CCC AAG TCT GGA AGT	106534389	8777	61.4
				GCT AGT ACG TCA A
AJ_P4	85653143	F04	AJ_352	/5AmMC6/CCC AAT CGC AAC TTC	106534390	8687	59.4
				GGT ACA TTT GTA A
AJ_P4	85653144	F05	AJ_353	/5AmMC6/CCC AAC CTG TAA CAT	106534391	8754	59
				TGA AGA AGC GTA A
AJ_P4	85653145	F06	AJ_354	/5AmMC6/CCC AAA CTG TTG GAA	106534392	8754	59.4
				AGC TGA ACA CTA A
AJ_P4	85653146	F07	AJ_355	/5AmMC6/CCC AAG ACG TAG CTT	106534393	8779	58.9
				AGA GAG AAC CTA A
AJ_P4	85653147	F08	AJ_356	/5AmMC6/CCC AAC ATT GTT GTG	106534394	8761	60.7
				GAA CCT CAG AGA A
AJ_P4	85653148	F09	AJ_357	/5AmMC6/CCC AAG TGG ACT AGC	106534395	8697	61.2
				TTC CTA CAC TGA A
AJ_P4	85653149	F10	AJ_358	/5AmMC6/CCC AAA GGA ACT GAC	106534396	8723	59.2
				ATT CAA CAC GTA A
AJ_P4	85653150	F11	AJ_359	/5AmMC6/CCC AAT GTT CGA GTC	106534397	8690	60.2
				CAC AAC TAC AGA A
AJ_P4	85653151	F12	AJ_360	/5AmMC6/CCC AAG TAA CTA CTC	106534398	8739	59
				ACA GAG CTA GGA A
AJ_P4	85653152	G01	AJ_361	/5AmMC6/CCC AAG AGG ACT CAC	106534399	8706	61.2
				CAG TAC TTT CGA A
AJ_P4	85653153	G02	AJ_362	/5AmMC6/CCC AAT AGC GTT GTT TCT	106534400	8687	59
				AAC CAC TGA A
AJ_P4	85653154	G03	AJ_363	/5AmMC6/CCC AAC ATT TGT TAG	106534401	8736	59
				TAG CAG TCA CGA A
AJ_P4	85653155	G04	AJ_364	/5AmMC6/CCC AAT AAC AGC AAG	106534402	8699	60.8
				ACC TTG TAG CCA A
AJ_P4	85653156	G05	AJ_365	/5AmMC6/CCC AAG ACT CTC CAC	106534403	8675	61.9
				ACG TTG AAG ACA A
AJ_P4	85653157	G06	AJ_366	/5AmMC6/CCC AAG AAC TCC ATC	106534404	8666	61.7
				CTG TTC GAC AGA A
AJ_P4	85653158	G07	AJ_367	/5AmMC6/CCC AAG GTT CTA GTT	106534405	8681	60.4
				CCA ACT AAC GCA A
AJ_P4	85653159	G08	AJ_368	/5AmMC6/CCC AAA GTT GCG TTT	106534406	8727	59
				GTC ATA GAC CTA A
AJ_P4	85653160	G09	AJ_369	/5AmMC6/CCC AAC GCT TGA GGT	106534407	8763	59
				AAA CTA AAC AGA A
AJ_P4	85653161	G10	AJ_370	/5AmMC6/CCC AAT AAC GAG TAG	106534408	8739	59
				AGC TCT AGA CCA A
AJ_P4	85653162	G11	AJ_371	/5AmMC6/CCC AAG TGA GTC ATA	106534409	8739	60.5
				GCC ATA AGC CAA A
AJ_P4	85653163	G12	AJ_372	/5AmMC6/CCC AAC TTA CGT GAC	106534410	8672	60.3
				TTC CAT TCA GGA A
AJ_P4	85653164	H01	AJ_373	/5AmMC6/CCC AAA TCA GTG ACT	106534411	8672	60.1
				GTC TCT TCA CGA A
AJ_P4	85653165	H02	AJ_374	/5AmMC6/CCC AAA GGT ACT GAC	106534412	8657	61.4
				TTC CAC TCC TGA A
AJ_P4	85653166	H03	AJ_375	/5AmMC6/CCC AAT CGA CAT TAC	106534413	8779	59.9
				AGG AAG TAC GGA A
AJ_P4	85653167	H04	AJ_376	/5AmMC6/CCC AAC CAC TGG TTA	106534414	8739	60.9
				AAC GTA AAC GGA A
AJ_P4	85653168	H05	AJ_377	/5AmMC6/CCC AAG TTC ATT CCC	106534415	8672	60.7
				TAA GCC TTG GAA A
AJ_P4	85653169	H06	AJ_378	/5AmMC6/CCC AAG AAA CTA CTC	106534416	8730	60.1
				CAT GGT TAG CGA A
AJ_P4	85653170	H07	AJ_379	/5AmMC6/CCC AAC TAA GGG TTA	106534417	8745	58.4
				AAG CTT ACC GTA A
AJ_P4	85653171	H08	AJ_380	/5AmMC6/CCC AAG AGA CCT GTC	106534418	8690	60.1
				ACA CTT TAA CGA A
AJ_P4	85653172	H09	AJ_381	/5AmMC6/CCC AAT GAA CAA CAA	106534419	8723	59.8
				CAT GCT TAC GGA A
AJ_P4	85653173	H10	AJ_382	/5AmMC6/CCC AAT CAG AAA GCA	106534420	8763	58.9
				ACA TTC TAG GGA A
AJ_P4	85653174	H11	AJ_383	/5AmMC6/CCC AAT AGG CTT GAC	106534421	8705	58.8
				TCA TTA AAC CGA A
AJ_P4	85653175	H12	AJ_384	/5AmMC6/CCC AAA CTG GTT TGT	106534422	8712	60.3
				AGT CCT ACC GAA A
AJ_P5	85653177	A01	AJ_385	/5AmMC6/CCC AAA CCT GAC AGC	106534423	8687	59
				TTG TTT CTT AGA A
AJ_P5	85653178	A02	AJ_386	/5AmMC6/CCC AAC TTG CTA CAT	106534424	8770	59.9
				AGA GAG AGT GCA A
AJ_P5	85653179	A03	AJ_387	/5AmMC6/CCC AAG GTA AAC CTT	106534425	8666	61.5
				CCA GTC TCC AGA A
AJ_P5	85653180	A04	AJ_388	/5AmMC6/CCC AAT ACC AAG TAC	106534426	8739	60.8
				GCA AAC TGT GGA A
AJ_P5	85653181	A05	AJ_389	/5AmMC6/CCC AAC CGT AAA CCT	106534427	8730	60.5
				TAA GGT GTA GCA A
AJ_P5	85653182	A06	AJ_390	/5AmMC6/CCC AAC ATT GTT TCC	106534428	8681	61.6
				CAA GGC ATA GCA A
AJ_P5	85653183	A07	AJ_391	/5AmMC6/CCC AAG GTC ATC CTA	106534429	8657	61.9
				CTA GCA TTG CCA A
AJ_P5	85653184	A08	AJ_392	/5AmMC6/CCC AAG TTC AAC ATC	106534430	8681	60.4
				ACT GCT ACG GTA A
AJ_P5	85653185	A09	AJ_393	/5AmMC6/CCC AAT TCG CAT GCA	106534431	8727	60.1
				TTT AAG GTG TCA A
AJ_P5	85653186	A10	AJ_394	/5AmMC6/CCC AAC TTA GCA CTA	106534432	8779	59.4
				GAG AAG GAG TCA A
AJ_P5	85653187	A11	AJ_395	/5AmMC6/CCC AAG CTC AGG ACA	106534433	8777	62.2
				GTT GAG TGT TCA A
AJ_P5	85653188	A12	AJ_396	/5AmMC6/CCC AAG TCC TAG CTA	106534434	8761	59.7
				AGA GTG TGT CAA A
AJ_P5	85653189	B01	AJ_397	/5AmMC6/CCC AAG CTA CAA GCA	106534435	8754	59.3
				TAA GTG GTT CAA A
AJ_P5	85653190	B02	AJ_398	/5AmMC6/CCC AAG TCA TAC CAA	106534436	8739	60
				AGC TGA GAC GTA A
AJ_P5	85653191	B03	AJ_399	/5AmMC6/CCC AAT TTA GCA TAG	106534437	8754	58
				ACG AGA GAC TCA A
AJ_P5	85653192	B04	AJ_400	/5AmMC6/CCC AAT TTC ATG TAA	106534438	8745	59.4
				CGA CAG TGA GCA A
AJ_P5	85653193	B05	AJ_401	/5AmMC6/CCC AAT GCA CTT CGT	106534439	8754	58.6
				AGA GTA AGA ACA A
AJ_P5	85653194	B06	AJ_402	/5AmMC6/CCC AAA CGT TGT CTC	106534440	8752	60.5
				TGT AGT GGA ACA A
AJ_P5	85653195	B07	AJ_403	/5AmMC6/CCC AAC CGA AGT TAG	106534441	8699	60.8
				CAA ACC TCA TGA A
AJ_P5	85653196	B08	AJ_404	/5AmMC6/CCC AAC ATT TAG AAG	106534442	8754	58.8
				GAC TTC GAA CGA A
AJ_P5	85653197	B09	AJ_405	/5AmMC6/CCC AAG TTC CAA CAC	106534443	8675	62
				TCA GAC AGG TCA A
AJ_P5	85653198	B10	AJ_406	/5AmMC6/CCC AAT GAC AAC CTC	106534444	8706	61.7
				TCA GAG TGG TCA A
AJ_P5	85653199	B11	AJ_407	/5AmMC6/CCC AAG CCT AGG TAG	106534445	8777	61
				GTT CTG GAA CTA A
AJ_P5	85653200	B12	AJ_408	/5AmMC6/CCC AAT CGA ACA CAC	106534446	8690	60.6
				CAT GTT ACT GGA A
AJ_P5	85653201	C01	AJ_409	/5AmMC6/CCC AAT AGT CTA ACT	106534447	8727	59.3
				GTT GGC TTG CAA A
AJ_P5	85653202	C02	AJ_410	/5AmMC6/CCC AAA AGC TAG GTA	106534448	8681	59.8
				CCT TCT TAC CGA A
AJ_P5	85653203	C03	AJ_411	/5AmMC6/CCC AAC TCA GAG TAC	106534449	8770	60
				AGA GAG TTT GCA A
AJ_P5	85653204	C04	AJ_412	/5AmMC6/CCC AAG ACA CGT CAT	106534450	8795	61.4
				AGG AGT GTA GCA A
AJ_P5	85653205	C05	AJ_413	/5AmMC6/CCC AAT TAA GCA TAA	106534451	8763	59.2
				CGA GAC AGT GCA A
AJ_P5	85653206	C06	AJ_414	/5AmMC6/CCC AAG TGT CCA CAT	106534452	8795	62.6
				GAG GTG AAA GCA A
AJ_P5	85653207	C07	AJ_415	/5AmMC6/CCC AAC TAA AGG GTT	106534453	8770	60.6
				GAA CGT TCC AGA A
AJ_P5	85653208	C08	AJ_416	/5AmMC6/CCC AAA TCG CTT TCT TTA	106534454	8727	59.1
				GTG GAG ACA A
AJ_P5	85653209	C09	AJ_417	/5AmMC6/CCC AAA GGT CTT CAC	106534455	8696	60.1
				TTT GTG CAC AAA A
AJ_P5	85653210	C10	AJ_418	/5AmMC6/CCC AAG GCT TAA GGT	106534456	8755	62.3
				GAA CCA TCG ACA A
AJ_P5	85653211	C11	AJ_419	/5AmMC6/CCC AAC TGT AGA GCT	106534457	8699	59.3
				ACC AAC ACT AGA A
AJ_P5	85653212	C12	AJ_420	/5AmMC6/CCC AAC TAA GGG TTG	106534458	8752	60.5
				TTA CGT TAG CCA A
AJ_P5	85653213	D01	AJ_421	/5AmMC6/CCC AAG TGG TAC TCA	106534459	8697	61.4
				GCT ACA TCG TCA A
AJ_P5	85653214	D02	AJ_422	/5AmMC6/CCC AAG TCC AAA CAC	106534460	8675	62.3
				CTT GAG AGC TCA A
AJ_P5	85653215	D03	AJ_423	/5AmMC6/CCC AAT CAC AAG CTT	106534461	8779	60.1
				AGA GTG GAG ACA A
AJ_P5	85653216	D04	AJ_424	/5AmMC6/CCC AAC TTT GAC TTT	106534462	8752	60.9
				GGC AAC TAG GGA A
AJ_P5	85653217	D05	AJ_425	/5AmMC6/CCC AAC CTC AGT CTA	106534463	8737	61
				AGG GTA GTG TCA A
AJ_P5	85653218	D06	AJ_426	/5AmMC6/CCC AAA CAC CTG TCC	106534464	8715	61.6
				AGA GAG TGT ACA A
AJ_P5	85653219	D07	AJ_427	/5AmMC6/CCC AAC ATA GTT GTG	106534465	8745	59.2
				AAG CAT CGC TAA A
AJ_P5	85653220	D08	AJ_428	/5AmMC6/CCC AAA CGT GTT GTT	106534466	8752	60.6
				GTA CCC TAG GAA A
AJ_P5	85653221	D09	AJ_429	/5AmMC6/CCC AAA CTT TGG TAG	106534467	8754	59.4
				AAA CGT AGC CAA A
AJ_P5	85653222	D10	AJ_430	/5AmMC6/CCC AAC TCA GTT GCA	106534468	8736	59.9
				TTA AAG TGT GCA A
AJ_P5	85653223	D11	AJ_431	/5AmMC6/CCC AAA CTA CTG TTC	106534469	8712	60.2
				TGG ACT TCG GAA A
AJ_P5	85653224	D12	AJ_432	/5AmMC6/CCC AAA GAG CAT TAG	106534470	8779	60
				GAC TGT ACG ACA A
AJ_P5	85653225	E01	AJ_433	/5AmMC6/CCC AAC ACC ATG CTG	106534471	8746	62.3
				AGT GGT AAG TCA A
AJ_P5	85653226	E02	AJ_434	/5AmMC6/CCC AAC TGG AAC ACG	106534472	8795	62.2
				TGT GGT AGA ACA A
AJ_P5	85653227	E03	AJ_435	/5AmMC6/CCC AAC CTC AGA ACT	106534473	8657	62
				CGT TGG TTA CCA A
AJ_P5	85653228	E04	AJ_436	/5AmMC6/CCC AAT GCC ATA ACG	106534474	8687	59.3
				CTT GTA CTT GTA A
AJ_P5	85653229	E05	AJ_437	/5AmMC6/CCC AAA ACC TTG TAG	106534475	8763	59
				ACA AGA AGC GTA A
AJ_P5	85653230	E06	AJ_438	/5AmMC6/CCC AAC ACA TGT TAG	106534476	8779	59.8
				AGA CGA CAG GTA A
AJ_P5	85653231	E07	AJ_439	/5AmMC6/CCC AAG GTA CTC TAA	106534477	8672	59.4
				CTT GCA GTC CTA A
AJ_P5	85653232	E08	AJ_440	/5AmMC6/CCC AAT GCC AAC CTC	106534478	8699	60.9
				AAG AAG TGT ACA A
AJ_P5	85653233	E09	AJ_441	/5AmMC6/CCC AAC TAA AGT TGG	106534479	8739	61.2
				GAA CGC ATC ACA A
AJ_P5	85653234	E10	AJ_442	/5AmMC6/CCC AAG GAC TAC TCC	106534480	8666	61
				ACT GTC ATC AGA A
AJ_P5	85653235	E11	AJ_443	/5AmMC6/CCC AAG AAC CGT AGT	106534481	8657	61
				TCC TTC CCT AGA A
AJ_P5	85653236	E12	AJ_444	/5AmMC6/CCC AAC TTT GAG GTG	106534482	8752	60.1
				AGA CTC GTT ACA A
AJ_P5	85653237	F01	AJ_445	/5AmMC6/CCC AAT CAG AGA AGA	106534483	8739	60.1
				GTT CGT CAC ACA A
AJ_P5	85653238	F02	AJ_446	/5AmMC6/CCC AAG TTT CAT TCC TCA	106534484	8672	60.5
				GAG CTG ACA A
AJ_P5	85653239	F03	AJ_447	/5AmMC6/CCC AAG TTG TCA CTC	106534485	8657	61.8
				CTG AGC ACT ACA A
AJ_P5	85653240	F04	AJ_448	/5AmMC6/CCC AAA AGG TTC ATC	106534486	8681	61.5
				GCT TTG ACC ACA A
AJ_P5	85653241	F05	AJ_449	/5AmMC6/CCC AAT GCC AAG ACT	106534487	8752	61.2
				TGT GGT GTT ACA A
AJ_P5	85653242	F06	AJ_450	/5AmMC6/CCC AAA GGC TTC GGT	106534488	8739	60.4
				AAC ACT AAC AGA A
AJ_P5	85653243	F07	AJ_451	/5AmMC6/CCC AAC AGC TAG CAT	106534489	8752	61.1
				GGT TTG GTT ACA A
AJ_P5	85653244	F08	AJ_452	/5AmMC6/CCC AAG CCA TTA GCC	106534490	8657	62.2
				TAG TTG TCC ACA A
AJ_P5	85653245	F09	AJ_453	/5AmMC6/CCC AAC GGT ACA ACG	106534491	8777	62.7
				GTT GGG TTT ACA A
AJ_P5	85653246	F10	AJ_454	/5AmMC6/CCC AAC ACC AGT TGG	106534492	8715	62.5
				ACA GGA CAT TCA A
AJ_P5	85653247	F11	AJ_455	/5AmMC6/CCC AAT CTC AGA CTG	106534493	8786	61.8
				GAA GGG TTG ACA A
AJ_P5	85653248	F12	AJ_456	/5AmMC6/CCC AAG TGT GAC GAA	106534494	8739	60.9
				CCT CAA ACA TGA A
AJ_P5	85653249	G01	AJ_457	/5AmMC6/CCC AAT GCG TAC AGG	106534495	8779	60.1
				TAC ATA GGA CAA A
AJ_P5	85653250	G02	AJ_458	/5AmMC6/CCC AAC AGT TAA AGG	106534496	8763	59.1
				ACA TGA GCT CAA A
AJ_P5	85653251	G03	AJ_459	/5AmMC6/CCC AAT CCG AAA GGG	106534497	8770	60.3
				TTA CAG TTA CGA A
AJ_P5	85653252	G04	AJ_460	/5AmMC6/CCC AAC ATT GTG AAA	106534498	8721	61.6
				GTG CAG TTC CCA A
AJ_P5	85653253	G05	AJ_461	/5AmMC6/CCC AAA ACC ATG AGG	106534499	8675	62.5
				TCA CGT TAC CCA A
AJ_P5	85653254	G06	AJ_462	/5AmMC6/CCC AAT CAA GGA GAA	106534500	8739	60.3
				ACG TGT ACC TCA A
AJ_P5	85653255	G07	AJ_463	/5AmMC6/CCC AAT CAG GAG ACG	106534501	8795	60.6
				ACT AGT AGG TCA A
AJ_P5	85653256	G08	AJ_464	/5AmMC6/CCC AAG GAC TAG GTC	106534502	8706	61
				ACA CAT CTC TGA A
AJ_P5	85653257	G09	AJ_465	/5AmMC6/CCC AAC ATA GAG AGG	106534503	8739	59.5
				ACA TCT TCG ACA A
AJ_P5	85653258	G10	AJ_466	/5AmMC6/CCC AAC GAA CTC ATC	106534504	8666	62.3
				CTT GTG GAC ACA A
AJ_P5	85653259	G11	AJ_467	/5AmMC6/CCC AAC AGT TGG TGA	106534505	8761	61.5
				GTT CAT GCA CAA A
AJ_P5	85653260	G12	AJ_468	/5AmMC6/CCC AAC ATA GGA CAG	106534506	8795	62.3
				GAG TGT TGC ACA A
AJ_P5	85653261	H01	AJ_469	/5AmMC6/CCC AAC TAG TAG AAG	106534507	8779	59.8
				ACT GCA TGG ACA A
AJ_P5	85653262	H02	AJ_470	/5AmMC6/CCC AAT AGA GCA AGA	106534508	8779	60.2
				ACC TCA GTT GGA A
AJ_P5	85653263	H03	AJ_471	/5AmMC6/CCC AAC CAT GTG GAG	106534509	8777	62.1
				TTT CTG AGG ACA A
AJ_P5	85653264	H04	AJ_472	/5AmMC6/CCC AAT AGA CAG GAC	106534510	8755	61.9
				AGG TGT TCC CAA A
AJ_P5	85653265	H05	AJ_473	/5AmMC6/CCC AAT TCG GAA GCC	106534511	8687	58.8
				ATT TCT CTT AGA A
AJ_P5	85653266	H06	AJ_474	/5AmMC6/CCC AAT CGG AAC AGT	106534512	8672	60.3
				TCC TCA TTC TGA A
AJ_P5	85653267	H07	AJ_475	/5AmMC6/CCC AAT GAA GCA GTT	106534513	8696	59.2
				CCA TCA TTC TGA A
AJ_P5	85653268	H08	AJ_476	/5AmMC6/CCC AAC ATG TGT CAA	106534514	8737	61.9
				GGG TAG CTC TCA A
AJ_P5	85653269	H09	AJ_477	/5AmMC6/CCC AAG CCT TTA CAC	106534515	8666	62.8
				CAT GTG GAA CCA A
AJ_P5	85653270	H10	AJ_478	/5AmMC6/CCC AAC TAA CTG CTG	106534516	8786	61.5
				AGG TGA GGT ACA A
AJ_P5	85653271	H11	AJ_479	/5AmMC6/CCC AAC TCC AAG TCG	106534517	8746	61.9
				AGT GAG TTG ACA A
AJ_P5	85653272	H12	AJ_480	/5AmMC6/CCC AAC GAG TTG AGA	106534518	8779	60.3
				AGC TAC ATG ACA A
AJ_P6	85653274	A01	AJ_481	/5AmMC6/CCC AAT TTC TGA GTG	106534519	8721	60.2
				AGC AAC CCT AGA A
AJ_P6	85653275	A02	AJ_482	/5AmMC6/CCC AAG AGT ACA GCT	106534520	8675	60.8
				ACC TCT CCA AGA A
AJ_P6	85653276	A03	AJ_483	/5AmMC6/CCC AAG AGC ACT CCA	106534521	8699	60.4
				CTT GTA CAA AGA A
AJ_P6	85653277	A04	AJ_484	/5AmMC6/CCC AAG CTA CAT TTC	106534522	8687	58.3
				TTG AGT CGA CTA A
AJ_P6	85653278	A05	AJ_485	/5AmMC6/CCC AAA CCG TAG GAC	106534523	8699	60.2
				TAC AAC ACT TGA A
AJ_P6	85653279	A06	AJ_486	/5AmMC6/CCC AAA TTC CTG TTG	106534524	8752	60.8
				TGA CGA AGT CGA A
AJ_P6	85653280	A07	AJ_487	/5AmMC6/CCC AAA GTT CTG TGG	106534525	8752	60.8
				TTC ACA AGT CGA A
AJ_P6	85653281	A08	AJ_488	/5AmMC6/CCC AAG TAC TCG AGT	106534526	8672	59.8
				TCC CTT TAA CGA A
AJ_P6	85653282	A09	AJ_489	/5AmMC6/CCC AAG CTG AAG GTT	106534527	8763	59.3
				AAC AAC AAG CTA A
AJ_P6	85653283	A10	AJ_490	/5AmMC6/CCC AAT CGC ATG GTA	106534528	8723	59.8
				AAC AAA CAC TGA A
AJ_P6	85653284	A11	AJ_491	/5AmMC6/CCC AAC TGG TAC TAA	106534529	8699	61
				AGC CAA ACT GCA A
AJ_P6	85653285	A12	AJ_492	/5AmMC6/CCC AAC GTT AAG AAG	106534530	8730	59.4
				GTA CCT AGC CTA A
AJ_P6	85653286	B01	AJ_493	/5AmMC6/CCC AAC AGT GAA AGT	106534531	8721	60.6
				TGT CCT TCC AGA A
AJ_P6	85653287	B02	AJ_494	/5AmMC6/CCC AAC AGG AGT TGG	106534532	8786	61.4
				GTA CCA GTC TAA A
AJ_P6	85653288	B03	AJ_495	/5AmMC6/CCC AAG AAA CTG TGC	106534533	8699	61.1
				AAA CAC TCC TGA A
AJ_P6	85653289	B04	AJ_496	/5AmMC6/CCC AAT CGT AGT TCG	106534534	8690	60.1
				ACA AAC TCC AGA A
AJ_P6	85653290	B05	AJ_497	/5AmMC6/CCC AAC AGG TTA GTT	106534535	8666	62.1
				CAC ACC ATC CGA A
AJ_P6	85653291	B06	AJ_498	/5AmMC6/CCC AAG GTT TAC GTC	106534536	8657	61.7
				ACT CCA TCC AGA A
AJ_P6	85653292	B07	AJ_499	/5AmMC6/CCC AAG TTT AAC CTC	106534537	8687	59.3
				ATG CTT TAG CGA A
AJ_P6	85653293	B08	AJ_500	/5AmMC6/CCC AAT TTG TAC GTT	106534538	8672	60.9
				CCA ACC TAG GCA A
AJ_P6	85653294	B09	AJ_501	/5AmMC6/CCC AAA TCG TTT GTT TCC	106534539	8727	59.8
				AGT AGG CAA A
AJ_P6	85653295	B10	AJ_502	/5AmMC6/CCC AAG CAT CCT TGT CTT	106586458	8672	60.7
				AAC TGC AGA A
AJ_P6	85653296	B11	AJ_503	/5AmMC6/CCC AAA CTG GTA AGT	106534541	8697	62
				CTT GGC TAC CCA A
AJ_P6	85653297	B12	AJ_504	/5AmMC6/CCC AAG TCC ATG TGC	106534542	8675	63
				AAC ACC AAC TGA A
AJ_P6	85653298	C01	AJ_505	/5AmMC6/CCC AAG TCA CAG GAC	106534543	8675	61.7
				TCC TCA ACA TGA A
AJ_P6	85653299	C02	AJ_506	/5AmMC6/CCC AAG TAC TCT CAT TCT	106577185	8672	60.1
				GTG CAG ACA A
AJ_P6	85653300	C03	AJ_507	/5AmMC6/CCC AAG GTT CCA CAC	106577186	8657	62.6
				TTT GTC ACG ACA A
AJ_P6	85653301	C04	AJ_508	/5AmMC6/CCC AAA CTC GTC TGT	106586459	8681	60.1
				CCA TAA AGT CGA A
AJ_P6	85653302	C05	AJ_509	/5AmMC6/CCC AAC AAG GTG TGT	106577187	8712	60.6
				TCT ACC ATT CGA A
AJ_P6	85653303	C06	AJ_510	/5AmMC6/CCC AAA CTC GTG TTG	106577188	8736	58.6
				TAC TTA GAA CGA A
AJ_P6	85653304	C07	AJ_511	/5AmMC6/CCC AAA GGC ATT GTC	106534549	8723	59.9
				AAC AAA CCA GTA A
AJ_P6	85653305	C08	AJ_512	/5AmMC6/CCC AAC AGT AGT TGT	106577189	8736	58.5
				TAA CGA CTG CTA A
AJ_P6	85653306	C09	AJ_513	/5AmMC6/CCC AAT GCT CAG GTC	106534551	8723	59.1
				AAA CAA ACT AGA A
AJ_P6	85653307	C10	AJ_514	/5AmMC6/CCC AAT GTC GTA CTT	106586460	8727	58.3
				TGA GTA AGC CTA A
AJ_P6	85653308	C11	AJ_515	/5AmMC6/CCC AAG GCT AGA CGA	106534553	8739	60.2
				ACA TTA CCA TGA A
AJ_P6	85653309	C12	AJ_516	/5AmMC6/CCC AAC GAG TGT TCT	106586461	8752	60
				AGT GTT ACA CGA A
AJ_P6	85653310	D01	AJ_517	/5AmMC6/CCC AAC AGG TTT ACG	106534555	8752	60.3
				TGT GTA CAG CTA A
AJ_P6	85653311	D02	AJ_518	/5AmMC6/CCC AAA GGT TCC TTC	106534556	8672	60.6
				CAT GTA AGC TCA A
AJ_P6	85653312	D03	AJ_519	/5AmMC6/CCC AAA GGC TTT GCT	106534557	8727	59.3
				GTT ACT TAG ACA A
AJ_P6	85653313	D04	AJ_520	/5AmMC6/CCC AAC AAA GTA ACT	106534558	8745	59.9
				GTT CGT TGC GAA A
AJ_P6	85653314	D05	AJ_521	/5AmMC6/CCC AAA TGC TTG GAA	106534559	8696	59.1
				CTT CTA ACT CGA A
AJ_P6	85653315	D06	AJ_522	/5AmMC6/CCC AAC CTG AGT ACT	106534560	8721	60.4
				GTG CTC TGA AAA A
AJ_P6	85653316	D07	AJ_523	/5AmMC6/CCC AAG GAC TCA AGT	106534561	8657	61.6
				CTT CCT TCA CGA A
AJ_P6	85653317	D08	AJ_524	/5AmMC6/CCC AAA GGG TTC CGT	106534562	8721	60.7
				TCA CTA ACA TGA A
AJ_P6	85653318	D09	AJ_525	/5AmMC6/CCC AAC CAG TAC TGC	106534563	8712	60.5
				ATT TCT TGG AGA A
AJ_P6	85653319	D10	AJ_526	/5AmMC6/CCC AAC AAG CCT AGT	106534564	8712	60.5
				TCT GGT TGT ACA A
AJ_P6	85653320	D11	AJ_527	/5AmMC6/CCC AAC AGA CCT ACC	106534565	8672	60.5
				TTT GTT GTA GCA A
AJ_P6	85653321	D12	AJ_528	/5AmMC6/CCC AAG AAC CCT TCT	106534566	8681	60.9
				TTG ACT GCA AGA A
AJ_P6	85653322	E01	AJ_529	/5AmMC6/CCC AAA GTC GTT TAG	106534567	8672	60.2
				TCC TCT GAC CAA A
AJ_P6	85653323	E02	AJ_530	/5AmMC6/CCC AAA GTC TCT TCG TTC	106534568	8712	60.2
				AAC TGG AGA A
AJ_P6	85653324	E03	AJ_531	/5AmMC6/CCC AAC GCA TTC TTA	106534569	8714	58.6
				ACA GAG ACA GTA A
AJ_P6	85653325	E04	AJ_532	/5AmMC6/CCC AAC GAG TCT CTT	106534570	8770	59.4
				GAG AGG AAA CTA A
AJ_P6	85653326	E05	AJ_533	/5AmMC6/CCC AAC GTA GTG AGT	106534571	8755	61
				AGA CGT ACA CCA A
AJ_P6	85653327	E06	AJ_534	/5AmMC6/CCC AAA AAG CTT GTT	106534572	8696	59.6
				ACC TTC TGC AGA A
AJ_P6	85653328	E07	AJ_535	/5AmMC6/CCC AAA CTT TGT ACT	106534573	8745	59.1
				GGA GTA GCC AAA A
AJ_P6	85653329	E08	AJ_536	/5AmMC6/CCC AAG CTT ACC TCT	106534574	8705	58.9
				TAA GTG CAA GAA A
AJ_P6	85653330	E09	AJ_537	/5AmMC6/CCC AAG AAC CTC TTA	106534575	8723	58.9
				AAG CTA AGC GAA A
AJ_P6	85653331	E10	AJ_538	/5AmMC6/CCC AAG ACC TAA ACA	106534576	8739	60.4
				AGC TTG AGT CGA A
AJ_P6	85653332	E11	AJ_539	/5AmMC6/CCC AAT TTG CAT AGG	106534577	8687	59.5
				TTC TTC CAA CGA A
AJ_P6	85653333	E12	AJ_540	/5AmMC6/CCC AAG CAA GTT GCA	106534578	8672	61.1
				TTC CTC TCA TGA A
AJ_P6	85653334	F01	AJ_541	/5AmMC6/CCC AAT CGG TAC ACG	106534579	8739	59.9
				ACA TAC ATG AGA A
AJ_P6	85653335	F02	AJ_542	/5AmMC6/CCC AAA CCT CTG TTT CTG	106534580	8712	60.2
				AGT CGA AGA A
AJ_P6	85653336	F03	AJ_543	/5AmMC6/CCC AAA CAC GTG TTG	106534581	8745	59.3
				GCT AGT CTA AAA A
AJ_P6	85653337	F04	AJ_544	/5AmMC6/CCC AAC GGT TTA AGC	106534582	8672	61.3
				CTT TCA CCA TGA A
AJ_P6	85653338	F05	AJ_545	/5AmMC6/CCC AAC GGT TCA TGG	106534583	8786	61.7
				ACT AAC TGA GGA A
AJ_P6	85653339	F06	AJ_546	/5AmMC6/CCC AAA CCG TTC AGT	106534584	8721	61.4
				TTC ACA TGG GAA A
AJ_P6	85653340	F07	AJ_547	/5AmMC6/CCC AAG ACC TCT CCA	106534585	8657	61
				CTT GAC TGT AGA A
AJ_P6	85653341	F08	AJ_548	/5AmMC6/CCC AAG TCT TTA CCT	106534586	8712	59.7
				CAG TGT AGC AGA A
AJ_P6	85653342	F09	AJ_549	/5AmMC6/CCC AAA CAG CTG AGT	106534587	8690	60.2
				CCT TCC ATA AGA A
AJ_P6	85653343	F10	AJ_550	/5AmMC6/CCC AAA ACT GTC ATT	106534588	8672	60.6
				GCC TTC CTA GGA A
AJ_P6	85653344	F11	AJ_551	/5AmMC6/CCC AAG TCC ATT CAT	106534589	8712	60.6
				TCG TTC GAA GGA A
AJ_P6	85653345	F12	AJ_552	/5AmMC6/CCC AAG TCA CCT CTT	106534590	8737	61.6
				GGT AGT AAG GCA A
AJ_P6	85653346	G01	AJ_553	/5AmMC6/CCC AAC CAT CAG CTT	106534591	8712	60.9
				TAG TTG GTG ACA A
AJ_P6	85653347	G02	AJ_554	/5AmMC6/CCC AAG TTA CCT GAC	106534592	8666	61.7
				TCC ACT GGA CAA A
AJ_P6	85653348	G03	AJ_555	/5AmMC6/CCC AAA GTT GGC ATC	106534593	8727	60.1
				TTT GTC GTC AAA A
AJ_P6	85653349	G04	AJ_556	/5AmMC6/CCC AAA CGT TGT GTC TTT	106534594	8687	59.4
				AAC ATC CGA A
AJ_P6	85653350	G05	AJ_557	/5AmMC6/CCC AAC AGT TTG GCT	106534595	8712	61.5
				TTG ACA TCA CGA A
AJ_P6	85653351	G06	AJ_558	/5AmMC6/CCC AAA CGG TTT GCA	106534596	8687	60.1
				ACT CAT TCT TGA A
AJ_P6	85653352	G07	AJ_559	/5AmMC6/CCC AAG ACG ACT GTT	106534597	8672	59.8
				TAC TTC CTC AGA A
AJ_P6	85653353	G08	AJ_560	/5AmMC6/CCC AAG GAC TCC ATT	106534598	8657	61.9
				TCG ACT TCG ACA A
AJ_P6	85653354	G09	AJ_561	/5AmMC6/CCC AAA TCA AGT CTA	106534599	8763	58
				GAC AGA AGG CTA A
AJ_P6	85653355	G10	AJ_562	/5AmMC6/CCC AAG TCG TCA TCA	106534600	8699	60.4
				GCA AGA AAC CTA A
AJ_P6	85653356	G11	AJ_563	/5AmMC6/CCC AAT CGT GTA CAT	106534601	8754	59.1
				GGA AAG CAC ATA A
AJ_P6	85653357	G12	AJ_564	/5AmMC6/CCC AAC TTT GAA GCA	106534602	8754	59.1
				TGG AGA ACA CTA A
AJ_P6	85653358	H01	AJ_565	/5AmMC6/CCC AAA AGT CCT CTG	106534603	8727	58.6
				TTT AGT TAG CGA A
AJ_P6	85653359	H02	AJ_566	/5AmMC6/CCC AAG TAA CCA AAC	106534604	8699	60.5
				CAT GCT AGT CGA A
AJ_P6	85653360	H03	AJ_567	/5AmMC6/CCC AAG GAC ATT GAC	106534605	8675	62.4
				TCA CCA TCA GCA A
AJ_P6	85653361	H04	AJ_568	/5AmMC6/CCC AAT GGG TAC TGC	106534606	8730	60
				ATA CAC CAT AGA A
AJ_P6	85653362	H05	AJ_569	/5AmMC6/CCC AAA GAA CTC GTC	106534607	8696	58.8
				TTC ATT TAC GGA A
AJ_P6	85653363	H06	AJ_570	/5AmMC6/CCC AAA GGT CTT TGT	106534608	8752	59.5
				CCT AGT ACG AGA A
AJ_P6	85653364	H07	AJ_571	/5AmMC6/CCC AAC ATG GTT AAG	106534609	8770	60.3
				GTC AAC TCG AGA A
AJ_P6	85653365	H08	AJ_572	/5AmMC6/CCC AAG CTT GTA ACG	106534610	8672	59.9
				ACT TAC TCT CGA A
AJ_P6	85653366	H09	AJ_573	/5AmMC6/CCC AAG ACC ACT CTC	106534611	8657	61.7
				CTA GCA TTT GGA A
AJ_P6	85653367	H10	AJ_574	/5AmMC6/CCC AAG TCC ATT CCC	106534612	8697	62.2
				ATT GGT AGC AGA A
AJ_P6	85653368	H11	AJ_575	/5AmMC6/CCC AAC ACT CTG TGT	106534613	8737	61.3
				CGT ACA TAG GGA A
AJ_P6	85653369	H12	AJ_576	/5AmMC6/CCC AAA CTT GTG TGG	106534614	8706	62.8
				AAA CCG TAC CCA A
AJ_P7	85653371	A01	AJ_577	/5AmMC6/CCC AAA TGC CTT GGT	106534711	8761	61
				GTC ATA CAG GAA A
AJ_P7	85653372	A02	AJ_578	/5AmMC6/CCC AAT CGG AAG TCA	106534712	8763	57.8
				GAC TAG AAA CTA A
AJ_P7	85653373	A03	AJ_579	/5AmMC6/CCC AAC CAG TAC CAG	106534713	8755	61
				AGG TGA AGT CTA A
AJ_P7	85653374	A04	AJ_580	/5AmMC6/CCC AAC ATA AAG GGA	106534714	8763	58.4
				AAC TGA GCT CTA A
AJ_P7	85653375	A05	AJ_581	/5AmMC6/CCC AAC TAA GAG GAG	106534715	8779	59.6
				AAC TCC AGT TGA A
AJ_P7	85653376	A06	AJ_582	/5AmMC6/CCC AAC TAG GAA GTT	106534716	8681	59.5
				TAC TCC ACT CGA A
AJ_P7	85653377	A07	AJ_583	/5AmMC6/CCC AAC AAC GTC TGC	106534717	8730	60.3
				TAA AGT AGG TCA A
AJ_P7	85653378	A08	AJ_584	/5AmMC6/CCC AAC GTC ATC AAC	106534718	8714	58.4
				ATA GTA GGC TAA A
AJ_P7	85653379	A09	AJ_585	/5AmMC6/CCC AAA TCG TCA CTA	106534719	8763	57.3
				GAG AGA GAA CTA A
AJ_P7	85653380	A10	AJ_586	/5AmMC6/CCC AAC TTG TCA CAT	106534720	8730	60
				GAA GGA GAC CTA A
AJ_P7	85653381	A11	AJ_587	/5AmMC6/CCC AAG GAG ACT CTA	106534721	8739	59.5
				GAA ACT TCC GAA A
AJ_P7	85653382	A12	AJ_588	/5AmMC6/CCC AAG AGT TAC GCT	106534722	8672	59.7
				TCT ACT TCC AGA A
AJ_P7	85653383	B01	AJ_589	/5AmMC6/CCC AAA CCA GTC CTT	106534723	8746	61.5
				AAG GGT AGG TCA A
AJ_P7	85653384	B02	AJ_590	/5AmMC6/CCC AAA AGC CTA GAA	106534724	8723	58.8
				CAT TAC ATC GGA A
AJ_P7	85653385	B03	AJ_591	/5AmMC6/CCC AAG CTG AAA GCA	106534725	8690	61.1
				CTC CAT CAT TGA A
AJ_P7	85653386	B04	AJ_592	/5AmMC6/CCC AAT CAG TGT GAC	106534726	8657	61.1
				TCC ATC CCT AGA A
AJ_P7	85653387	B05	AJ_593	/5AmMC6/CCC AAG CTA CTT AAC	106534727	8687	58.6
				TCT GTT TCG GAA A
AJ_P7	85653388	B06	AJ_594	/5AmMC6/CCC AAA TGC TTT CAC	106534728	8752	60.6
				TGG TCT AGG GAA A
AJ_P7	85653389	B07	AJ_595	/5AmMC6/CCC AAC AGT TGT TCG	106534729	8712	60.9
				TTC ATG ACC AGA A
AJ_P7	85653390	B08	AJ_596	/5AmMC6/CCC AAT CAC GAA ACG	106534730	8739	59.8
				ACT ACT TAG GGA A
AJ_P7	85653391	B09	AJ_597	/5AmMC6/CCC AAC ATT GTT TGG TTC	106534731	8727	60.1
				ATC AAG CGA A
AJ_P7	85653392	B10	AJ_598	/5AmMC6/CCC AAA TTC TTG TGG	106534732	8736	59.8
				TAC AAC ATG CGA A
AJ_P7	85653393	B11	AJ_599	/5AmMC6/CCC AAC CTG ACC AAC	106534733	8672	61
				GGT TCA TTT GTA A
AJ_P7	85653394	B12	AJ_600	/5AmMC6/CCC AAG ACC ATT ACG	106534734	8672	60.8
				TCT TGC CTT GAA A
AJ_P7	85653395	C01	AJ_601	/5AmMC6/CCC AAG CCA TAC CTC	106534735	8672	60.8
				ATT GAG CTT TGA A
AJ_P7	85653396	C02	AJ_602	/5AmMC6/CCC AAA GGA CTC TTC	106534736	8657	61.7
				CGT AAC CTG TCA A
AJ_P7	85653397	C03	AJ_603	/5AmMC6/CCC AAG GAG TGC ATT	106534737	8761	60.8
				TCG TAA CCT GAA A
AJ_P7	85653398	C04	AJ_604	/5AmMC6/CCC AAT CAC AAG CGA	106534738	8754	58.6
				AAG TAG TGT CTA A
AJ_P7	85653399	C05	AJ_605	/5AmMC6/CCC AAT CGA AGA GAC	106534739	8770	60.1
				GAC TTG AGT TCA A
AJ_P7	85653400	C06	AJ_606	/5AmMC6/CCC AAA TGG CTT TGG	106534740	8761	61.2
				TAC AAC TGA CGA A
AJ_P7	85653401	C07	AJ_607	/5AmMC6/CCC AAG AGA CGT TGG	106534741	8755	61.6
				AAC ACC TAC TGA A
AJ_P7	85653402	C08	AJ_608	/5AmMC6/CCC AAG AAA GCT GTT	106534742	8699	61.1
				CAA ACC TCA CGA A
AJ_P7	85653403	C09	AJ_609	/5AmMC6/CCC AAG TGA GTC TTC	106534743	8761	60.4
				GAA ACT TCG GAA A
AJ_P7	85653404	C10	AJ_610	/5AmMC6/CCC AAC CAG TGT TAA	106534744	8786	62.4
				CGG AAC TTG GGA A
AJ_P7	85653405	C11	AJ_611	/5AmMC6/CCC AAC AGG TGT ACT	106534745	8777	61.3
				TGG TAC TAC GGA A
AJ_P7	85653406	C12	AJ_612	/5AmMC6/CCC AAG TAC CAT CCT	106534746	8672	59.8
				TAC GTA GCT TGA A
AJ_P7	85653407	D01	AJ_613	/5AmMC6/CCC AAG CTA CTT CCA	106534747	8706	60.9
				CTA GGT ACA GGA A
AJ_P7	85653408	D02	AJ_614	/5AmMC6/CCC AAG TAC CTC AAC	106534748	8699	60.1
				AAG TCA AGG CTA A
AJ_P7	85653409	D03	AJ_615	/5AmMC6/CCC AAG TAC CCA AGA	106534749	8739	59.8
				GAC TAA GCT TGA A
AJ_P7	85653410	D04	AJ_616	/5AmMC6/CCC AAT GAA CCA AAC	106534750	8699	61
				ACT GAC CTG TGA A
AJ_P7	85653411	D05	AJ_617	/5AmMC6/CCC AAG TGC ACA TCG	106534751	8699	60.8
				AAC CAA CTT AGA A
AJ_P7	85653412	D06	AJ_618	/5AmMC6/CCC AAT GCT TAG CGT	106534752	8696	58.2
				ACT ACC ATT AGA A
AJ_P7	85653413	D07	AJ_619	/5AmMC6/CCC AAG TTT GAC GTT	106534753	8681	61.2
				CAA CCA TCA CGA A
AJ_P7	85653414	D08	AJ_620	/5AmMC6/CCC AAT TTA GCT TGT	106534754	8712	60.2
				CCA CTC AGA GGA A
AJ_P7	85653415	D09	AJ_621	/5AmMC6/CCC AAC GCT ACT TTC TTA	106534755	8687	58.4
				GTT AGA GCA A
AJ_P7	85653416	D10	AJ_622	/5AmMC6/CCC AAA AGC CTT TCC	106534756	8672	60.9
				ACT GTT ACT GGA A
AJ_P7	85653417	D11	AJ_623	/5AmMC6/CCC AAC CTG TTA CCT	106534757	8697	61.9
				CAG ACA TTG GGA A
AJ_P7	85653418	D12	AJ_624	/5AmMC6/CCC AAC GTC ATT TAG	106534758	8752	59.6
				GTC TCT AAG GGA A
AJ_P7	85653419	E01	AJ_625	/5AmMC6/CCC AAA CGT CTT GGG	106534759	8712	60.2
				TTA CAC TAC TGA A
AJ_P7	85653420	E02	AJ_626	/5AmMC6/CCC AAT CAC AGA ACC	106534760	8690	60.8
				AGT CAG CTT TGA A
AJ_P7	85653421	E03	AJ_627	/5AmMC6/CCC AAG TGG TAC TCT	106534761	8697	60.9
				CGT AAC TCC AGA A
AJ_P7	85653422	E04	AJ_628	/5AmMC6/CCC AAG AAC TCC TAC	106534762	8675	61.2
				CAA GAC TCG TGA A
AJ_P7	85653423	E05	AJ_629	/5AmMC6/CCC AAT TTG ACT TGA	106534763	8705	59.7
				ACG CAT AAC CGA A
AJ_P7	85653424	E06	AJ_630	/5AmMC6/CCC AAT TGA GAC CTC	106534764	8699	59.8
				ACG AGA ACA CTA A
AJ_P7	85653425	E07	AJ_631	/5AmMC6/CCC AAA CAA AGT CAT	106534765	8745	59.8
				TGG GTT CGC TAA A
AJ_P7	85653426	E08	AJ_632	/5AmMC6/CCC AAT CGA ACA AAC	106534766	8699	60.4
				CTA GAG TGC TCA A
AJ_P7	85653427	E09	AJ_633	/5AmMC6/CCC AAG GTC TTA GCT	106534767	8681	59.8
				ACA ACC TCA TGA A
AJ_P7	85653428	E10	AJ_634	/5AmMC6/CCC AAG CTT TGA AGC	106534768	8681	60.7
				CTT CCA ACT AGA A
AJ_P7	85653429	E11	AJ_635	/5AmMC6/CCC AAT ACA GGT GTC	106534769	8699	60.5
				ACA AAC TCA CGA A
AJ_P7	85653430	E12	AJ_636	/5AmMC6/CCC AAC CGT TCA TAA	106534770	8699	60.4
				CAA GGG AAC CTA A
AJ_P7	85653431	F01	AJ 637	/5AmMC6/CCC AAA GTA CCC AAA	106534771	8739	61
				GCA TGT CTG GAA A
AJ_P7	85653432	F02	AJ_638	/5AmMC6/CCC AAA TGT TCT CTT TAC	106534772	8727	58.8
				GCT AGG GAA A
AJ_P7	85653433	F03	AJ_639	/5AmMC6/CCC AAT TTG ACT TCA	106534773	8745	59.3
				GAC GAA AGC TGA A
AJ_P7	85653434	F04	AJ_640	/5AmMC6/CCC AAT ACA GAA ACG	106534774	8723	59
				ACA TAC GCT TGA A
AJ_P7	85653435	F05	AJ_641	/5AmMC6/CCC AAT CAC CAG AAG	106534775	8699	60
				AAC TAC CTG TGA A
AJ_P7	85653436	F06	AJ_642	/5AmMC6/CCC AAT ACG AAC GAC	106534776	8770	60.2
				AGG TCA TGG TTA A
AJ_P7	85653437	F07	AJ_643	/5AmMC6/CCC AAG AAC TCC AAC	106534777	8690	59.9
				CAT GTA GTC GTA A
AJ_P7	85653438	F08	AJ_644	/5AmMC6/CCC AAA TTG CGT TCT	106534778	8696	59.6
				TCA GTA CAC GAA A
AJ_P7	85653439	F09	AJ_645	/5AmMC6/CCC AAA TCT GCT TCC TGT	106534779	8672	60
				AGT ACA CGA A
AJ_P7	85653440	F10	AJ_646	/5AmMC6/CCC AAG GTC ACT TGC	106534780	8675	62.3
				AAC CTA GAA CCA A
AJ_P7	85653441	F11	AJ_647	/5AmMC6/CCC AAG GCT TAG TAC	106534781	8715	61.5
				GAC AGT AAC CCA A
AJ_P7	85653442	F12	AJ_648	/5AmMC6/CCC AAC AAG TGA AGT	106534782	8795	62
				GGT CTG ACC AGA A
AJ_P7	85653443	G01	AJ_649	/5AmMC6/CCC AAC AGA GTA GTG	106534783	8770	59.3
				TGA CTA GCC TAA A
AJ_P7	85653444	G02	AJ_650	/5AmMC6/CCC AAT CAC AAG GAG	106534784	8754	59.1
				TAG CAA CTT TGA A
AJ_P7	85653445	G03	AJ_651	/5AmMC6/CCC AAC CTG TAA GTG	106534785	8779	60.5
				AAA CGA CTG GAA A
AJ_P7	85653446	G04	AJ_652	/5AmMC6/CCC AAC CCT AGT TGA	106534786	8755	61.8
				GGA CAA ACT GGA A
AJ_P7	85653447	G05	AJ_653	/5AmMC6/CCC AAG GCA TCA CAC	106534787	8690	60.8
				CTA GCA AGT TTA A
AJ_P7	85653448	G06	AJ_654	/5AmMC6/CCC AAG ACC TAC CCT	106534788	8666	60.9
				ACA GAG CTT GTA A
AJ_P7	85653449	G07	AJ_655	/5AmMC6/CCC AAT TTC GTA ACA	106534789	8736	59.1
				AGT TGG ACT CGA A
AJ_P7	85653450	G08	AJ_656	/5AmMC6/CCC AAT CAA AGA AAC	106534790	8763	59.8
				AGG TTG CAC TGA A
AJ_P7	85653451	G09	AJ_657	/5AmMC6/CCC AAC GTC TTA GAG	106534791	8657	61.7
				TCC TTG AAC CCA A
AJ_P7	85653452	G10	AJ_658	/5AmMC6/CCC AAT GCT GAA ACG	106534792	8687	59.8
				TTT CCC TTG TAA A
AJ_P7	85653453	G11	AJ_659	/5AmMC6/CCC AAC AGG TTT GTT	106534793	8752	60.8
				TGA CTC AGA CGA A
AJ_P7	85653454	G12	AJ_660	/5AmMC6/CCC AAC CTT CGA CAT	106534794	8723	59
				AAA GAA AGC GTA A
AJ_P7	85653455	H01	AJ_661	/5AmMC6/CCC AAT GAA CCA TTA	106534795	8763	59.4
				GCA AGC AAG GTA A
AJ_P7	85653456	H02	AJ_662	/5AmMC6/CCC AAT GAA CCT TGA	106534796	8739	61.3
				GCA CAA ACT GGA A
AJ_P7	85653457	H03	AJ_663	/5AmMC6/CCC AAA GGG TTC TTG	106534797	8737	61.8
				GAC AGT ACC TCA A
AJ_P7	85653458	H04	AJ_664	/5AmMC6/CCC AAC TGT AAA GGA	106534798	8721	59.5
				GTT CGT ACC CTA A
AJ_P7	85653459	H05	AJ_665	/5AmMC6/CCC AAT CGA GAA GGA	106534799	8779	59.8
				AGT CAC ACT GTA A
AJ_P7	85653460	H06	AJ_666	/5AmMC6/CCC AAC TAA AGG AAG	106534800	8770	60.4
				TGT CAG CTG TCA A
AJ_P7	85653461	H07	AJ_667	/5AmMC6/CCC AAG CAC ATA AGG	106534801	8779	61.1
				TCA AAC GTG TGA A
AJ_P7	85653462	H08	AJ_668	/5AmMC6/CCC AAC GTT GAA GGA	106534802	8779	61
				ACA TTC ACA GGA A
AJ_P7	85653463	H09	AJ_669	/5AmMC6/CCC AAT GTG AGC TGA	106534803	8763	59.8
				CAA ACA ACA TGA A
AJ_P7	85653464	H10	AJ_670	/5AmMC6/CCC AAG CTA CTC TAA	106534804	8675	61.4
				CAC GAC TGG ACA A
AJ_P7	85653465	H11	AJ_671	/5AmMC6/CCC AAG CCT AAC CTT	106534805	8681	60.8
				CAA GTG CAT GTA A
AJ_P7	85653466	H12	AJ_672	/5AmMC6/CCC AAG TAA ACA CCT	106534806	8730	59.9
				CTA GGT TCG GAA A
AJ_P8	85653468	A01	AJ_673	/5AmMC6/CCC AAG TCT TGA CTC	106534807	8681	60
				TCG ACT CGA AAA A
AJ_P8	85653469	A02	AJ_674	/5AmMC6/CCC AAC TGC AGA GTG	106534808	8779	61.1
				GAC TTG ACA AAA A
AJ_P8	85653470	A03	AJ_675	/5AmMC6/CCC AAC AGC TCT GGT	106534809	8721	60
				GTA CTT AAG ACA A
AJ_P8	85653471	A04	AJ_676	/5AmMC6/CCC AAT ACG AGA GAG	106534810	8779	59.6
				ACG TTT ACG ACA A
AJ_P8	85653472	A05	AJ_677	/5AmMC6/CCC AAG TAC CCT ACT	106534811	8666	60.9
				CTC GTC AAG GAA A
AJ_P8	85653473	A06	AJ_678	/5AmMC6/CCC AAT AAC GAC ACA	106534812	8699	60.5
				ACT GGT TAC CGA A
AJ_P8	85653474	A07	AJ_679	/5AmMC6/CCC AAC ACG TCA TAA	106534813	8675	61.5
				CGG TAG ACC TCA A
AJ_P8	85653475	A08	AJ_680	/5AmMC6/CCC AAT CCC AAG CAA	106534814	8699	60.2
				CAG TCA GTA GTA A
AJ_P8	85653476	A09	AJ_681	/5AmMC6/CCC AAT AAA CGA ACA	106534815	8699	60.8
				CCT GTG AGC TCA A
AJ_P8	85653477	A10	AJ_682	/5AmMC6/CCC AAG TTA CCA GAC	106534816	8699	60.2
				TCA ACA ACG GTA A
AJ_P8	85653478	A11	AJ_683	/5AmMC6/CCC AAG TTA GCT TGA	106534817	8690	60.8
				CCA ACC AAC GTA A
AJ_P8	85653479	A12	AJ_684	/5AmMC6/CCC AAG ACC ATC ACT	106534818	8675	60.7
				ACA GGA GTC CTA A
AJ_P8	85653480	B01	AJ_685	/5AmMC6/CCC AAG TAC TCT TCT TAC	106534819	8712	59.3
				GGT AGC AGA A
AJ_P8	85653481	B02	AJ_686	/5AmMC6/CCC AAT TTG CCA TCG	106534820	8714	60.4
				ACA ACG TGA AAA A
AJ_P8	85653482	B03	AJ_687	/5AmMC6/CCC AAA GTC TCT TGG	106534821	8752	60.2
				GTA CAA CGT GTA A
AJ_P8	85653483	B04	AJ_688	/5AmMC6/CCC AAT GAC CTT CTC GTT	106534822	8672	60.2
				ACA ACG GTA A
AJ_P8	85653484	B05	AJ_689	/5AmMC6/CCC AAT ACC GTT CTG	106534823	8736	58.6
				TTA AGA AGC GTA A
AJ_P8	85653485	B06	AJ_690	/5AmMC6/CCC AAA GTC CTT CCT	106534824	8672	59.6
				CTA GTT ACG GAA A
AJ_P8	85653486	B07	AJ_691	/5AmMC6/CCC AAG CCA TAC AAC	106534825	8730	60.6
				ATT GGA CTG GTA A
AJ_P8	85653487	B08	AJ_692	/5AmMC6/CCC AAC CTG AGA GGT	106534826	8761	59.8
				AAG CTT GAC TTA A
AJ_P8	85653488	B09	AJ_693	/5AmMC6/CCC AAC ACC TAG TAG	106534827	8746	61.2
				TCG TTG GAC AGA A
AJ_P8	85653489	B10	AJ_694	/5AmMC6/CCC AAG TAC ACT AAA	106534828	8723	59.6
				CCG TTG CGA AAA A
AJ_P8	85653490	B11	AJ_695	/5AmMC6/CCC AAC CAC TGG TAC	106534829	8730	60.8
				GGA AAG CTT TAA A
AJ_P8	85653491	B12	AJ_696	/5AmMC6/CCC AAG ACC ACT CTT	106534830	8746	61.2
				TGA GGA GTA CGA A
AJ_P8	85653492	C01	AJ_697	/5AmMC6/CCC AAG ACT GAC CTT	106534831	8795	62
				GGA AAG TAG GCA A
AJ_P8	85653493	C02	AJ_698	/5AmMC6/CCC AAC TCA CGT TAC	106534832	8739	60
				GAA ACA GAG GTA A
AJ_P8	85653494	C03	AJ_699	/5AmMC6/CCC AAG CGT AAC GTC	106534833	8687	59.2
				ATT TAC TTT CGA A
AJ_P8	85653495	C04	AJ_700	/5AmMC6/CCC AAC GAA CGT GTC	106534834	8687	59.7
				ATT TCA CTT TGA A
AJ_P8	85653496	C05	AJ_701	/5AmMC6/CCC AAA TCT CTG GTG	106534835	8657	62.1
				TCC ATC CGA ACA A
AJ_P8	85653497	C06	AJ_702	/5AmMC6/CCC AAA GCT TTG GAG	106534836	8761	61.1
				TCT GTG ACA ACA A
AJ_P8	85653498	C07	AJ_703	/5AmMC6/CCC AAG GGT ACT AGG	106534837	8786	61.9
				CTT GTG ACA ACA A
AJ_P8	85653499	C08	AJ_704	/5AmMC6/CCC AAT AGC GAA CAC	106534838	8699	60
				CTA GTT ACG ACA A
AJ_P8	85653500	C09	AJ_705	/5AmMC6/CCC AAT CAC GAG TCC	106534839	8675	61.7
				AAG AGT TAC CCA A
AJ_P8	85653501	C10	AJ_706	/5AmMC6/CCC AAT GAG AAC AAA	106534840	8763	59.1
				GGC TAA CCG TTA A
AJ_P8	85653502	C11	AJ_707	/5AmMC6/CCC AAC TCG TCA TAG	106534841	8699	59.9
				AAC ACC AAG GTA A
AJ_P8	85653503	C12	AJ_708	/5AmMC6/CCC AAC TCC ATG CAA	106534842	8723	59
				GTA AAG AAC GTA A
AJ_P8	85653504	D01	AJ_709	/5AmMC6/CCC AAA TGT GAC TAC	106534843	8705	59.3
				CGA AAC GCT TTA A
AJ_P8	85653505	D02	AJ_710	/5AmMC6/CCC AAG ACA AGT TGA	106534844	8699	60.8
				CCA ACG CAT CTA A
AJ_P8	85653506	D03	AJ_711	/5AmMC6/CCC AAC TGC ACA GTT	106534845	8690	60.5
				TAC AAC CTA GGA A
AJ_P8	85653507	D04	AJ_712	/5AmMC6/CCC AAG CTG ACT GTC	106534846	8672	59.8
				TTA ACC CTT AGA A
AJ_P8	85653508	D05	AJ_713	/5AmMC6/CCC AAG GTC AAG TCG	106534847	8739	60
				ACA AGC TAA CTA A
AJ_P8	85653509	D06	AJ_714	/5AmMC6/CCC AAC ACG TGA GTT	106534848	8675	62
				CCA ACC CTA AGA A
AJ_P8	85653510	D07	AJ_715	/5AmMC6/CCC AAG CCA TAA CCA	106534849	8690	59.9
				TCA GTC TGA GTA A
AJ_P8	85653511	D08	AJ_716	/5AmMC6/CCC AAG TCA ACA CAC	106534850	8699	60
				TCA GCA GTA GTA A
AJ_P8	85653512	D09	AJ_717	/5AmMC6/CCC AAG TAC CTA CTC	106534851	8672	60
				ATG CTT GCA GTA A
AJ_P8	85653513	D10	AJ_718	/5AmMC6/CCC AAA TGT ACG TAA	106534852	8723	59.2
				AGC ACA AGC CTA A
AJ_P8	85653514	D11	AJ_719	/5AmMC6/CCC AAC GTG TAA AGG	106534853	8779	60
				AAC TAG GCT ACA A
AJ_P8	85653515	D12	AJ_720	/5AmMC6/CCC AAG GTC ACT AAC	106534854	8675	61.4
				TCA GGA ACT CCA A
AJ_P8	85653516	E01	AJ_721	/5AmMC6/CCC AAT TCG AAG TAA	106534855	8723	59.4
				GCA ACA CCA TGA A
AJ_P8	85653517	E02	AJ_722	/5AmMC6/CCC AAT CGG AAG TGT	106534856	8779	60.6
				AAA CTG GAC ACA A
AJ_P8	85653518	E03	AJ_723	/5AmMC6/CCC AAG ACT CAC AAA	106534857	8690	60.2
				CCG TAC TTG GTA A
AJ_P8	85653519	E04	AJ_724	/5AmMC6/CCC AAC ATT CTG CAT	106534858	8770	60.2
				AGG AGA CAG TGA A
AJ_P8	85653520	E05	AJ_725	/5AmMC6/CCC AAA CCC ATG CAC	106534859	8699	61.3
				ATT GAG AAC TGA A
AJ_P8	85653521	E06	AJ_726	/5AmMC6/CCC AAT GGT CAG GAC	106534860	8739	59.7
				TAA ACT ACC AGA A
AJ_P8	85653522	E07	AJ_727	/5AmMC6/CCC AAG CTT CCA GAA	106534861	8712	60.6
				CTT TAC TTG GGA A
AJ_P8	85653523	E08	AJ_728	/5AmMC6/CCC AAG TTC AAC TCC	106534862	8675	62.3
				AAC GTC AGG ACA A
AJ_P8	85653524	E09	AJ_729	/5AmMC6/CCC AAG TTA CTA CCA	106534863	8681	59.4
				TAC GAC TCG TGA A
AJ_P8	85653525	E10	AJ_730	/5AmMC6/CCC AAC AGA CAT GCA	106534864	8699	60.5
				CTT AAC TCA GGA A
AJ_P8	85653526	E11	AJ_731	/5AmMC6/CCC AAC TTG AAC CTA	106534865	8779	60.2
				GAA AGG GTA GCA A
AJ_P8	85653527	E12	AJ_732	/5AmMC6/CCC AAG TCC TAC CTT	106534866	8730	58.8
				AAG AGA CGA GTA A
AJ_P8	85653528	F01	AJ_733	/5AmMC6/CCC AAC AGT TAG GGA	106534867	8761	61.1
				AGC TTT GCA TCA A
AJ_P8	85653529	F02	AJ_734	/5AmMC6/CCC AAC GTC TAG CTA	106534868	8745	58.2
				GAA GAA GTT TCA A
AJ_P8	85653530	F03	AJ_735	/5AmMC6/CCC AAT TTA GTC ACC TCT	106534869	8672	60
				GGA ACC GTA A
AJ_P8	85653531	F04	AJ_736	/5AmMC6/CCC AAC AGT GAA GGA	106534870	8730	60.9
				ACC TTT CGT CAA A
AJ_P8	85653532	F05	AJ_737	/5AmMC6/CCC AAA GGC TTC CTT	106534871	8687	59.1
				TCA GAC AGT TTA A
AJ_P8	85653533	F06	AJ_738	/5AmMC6/CCC AAA CGG TTG TTG	106534872	8761	60.9
				AGT CGA ACC ATA A
AJ_P8	85653534	F07	AJ_739	/5AmMC6/CCC AAA CCT CTG AGT	106534873	8730	60.5
				TGG CTA AAC AGA A
AJ_P8	85653535	F08	AJ_740	/5AmMC6/CCC AAG CAG TTG TAA	106534874	8779	60.3
				GAC CAA GAC GTA A
AJ_P8	85653536	F09	AJ_741	/5AmMC6/CCC AAG AGA GCT ACC	106534875	8727	58.6
				GTT TCT TTG TAA A
AJ_P8	85653537	F10	AJ_742	/5AmMC6/CCC AAA GGG TTC TCC	106534876	8761	60.4
				AAG TTT ACA GGA A
AJ_P8	85653538	F11	AJ_743	/5AmMC6/CCC AAC GTT AGT GTG	106534877	8727	59.6
				TTC AAG CTT CAA A
AJ_P8	85653539	F12	AJ_744	/5AmMC6/CCC AAC TCA CTG CAA	106534878	8739	60.8
				AGG TAA AGG TCA A
AJ_P8	85653540	G01	AJ_745	/5AmMC6/CCC AAG AGC TCA CAA	106534879	8786	61.9
				GGT GTT AGG TCA A
AJ_P8	85653541	G02	AJ_746	/5AmMC6/CCC AAC TGT CTA CTG	106534880	8752	60.4
				AAG GAG TTT GCA A
AJ_P8	85653542	G03	AJ_747	/5AmMC6/CCC AAA GCT TCC TTT ACT	106534881	8687	58.3
				GAC TAG TGA A
AJ_P8	85653543	G04	AJ_748	/5AmMC6/CCC AAC TGC TAC CCT	106534882	8681	60.1
				TGA GTA AAG TCA A
AJ_P8	85653544	G05	AJ_749	/5AmMC6/CCC AAG CTC ATT CCC	106534883	8681	60.2
				TTG AAC AGA GTA A
AJ_P8	85653545	G06	AJ_750	/5AmMC6/CCC AAG AGA CTG TGC	106534884	8715	61.9
				ACA ACC CTT AGA A
AJ_P8	85653546	G07	AJ_751	/5AmMC6/CCC AAC GGT TAA CCT	106534885	8714	59.4
				CAA GTG CTA AAA A
AJ_P8	85653547	G08	AJ_752	/5AmMC6/CCC AAA CCC TTG GGT	106534886	8755	61.7
				AAG CTA GAG ACA A
AJ_P8	85653548	G09	AJ_753	/5AmMC6/CCC AAA TTG CTC ACG	106534887	8672	61.2
				TTC TCA TGG ACA A
AJ_P8	85653549	G10	AJ_754	/5AmMC6/CCC AAC CCT AGG AAG	106534888	8690	60.3
				CCA TCA GTT TAA A
AJ_P8	85653550	G11	AJ_755	/5AmMC6/CCC AAA CCG TTT GAA	106534889	8672	61.3
				CCT TCT GGT CAA A
AJ_P8	85653551	G12	AJ_756	/5AmMC6/CCC AAT CCG AAG GAG	106534890	8739	60.7
				AAC TTT GAC CAA A
AJ_P8	85653552	H01	AJ_757	/5AmMC6/CCC AAT TGA GTC TGA	106534891	8754	59.1
				AGC AAC CAA GTA A
AJ_P8	85653553	H02	AJ_758	/5AmMC6/CCC AAC TGT TTA GAG	106534892	8727	58.7
				TGA CAT TGC CTA A
AJ_P8	85653554	H03	AJ_759	/5AmMC6/CCC AAT ACT GTT AAG	106534893	8705	58.5
				GCT ACA ACG CTA A
AJ_P8	85653555	H04	AJ_760	/5AmMC6/CCC AAA TCG GTT CGT	106534894	8672	60.1
				TCA CTA CTC AGA A
AJ_P8	85653556	H05	AJ_761	/5AmMC6/CCC AAC CAA GGT TGG	106534895	8737	62
				CTT AGT AGT CCA A
AJ_P8	85653557	H06	AJ_762	/5AmMC6/CCC AAG GCT ACA GAC	106534896	8672	60.6
				TTT CCC ATT TGA A
AJ_P8	85653558	H07	AJ_763	/5AmMC6/CCC AAG AAC CTC ACG	106534897	8712	61
				TGT GCT TGT TAA A
AJ_P8	85653559	H08	AJ_764	/5AmMC6/CCC AAG ACA TCC ACT	106534898	8672	60.5
				CTT GTT TGA CGA A
AJ_P8	85653560	H09	AJ_765	/5AmMC6/CCC AAG GTA CAC ACC	106534899	8657	62.2
				TTT GCC TTA CGA A
AJ_P8	85653561	H10	AJ_766	/5AmMC6/CCC AAC GAG TTG GAG	106534900	8779	60.1
				TAA CAT ACG ACA A
AJ_P8	85653562	H11	AJ_767	/5AmMC6/CCC AAA CGG TTG TGG	106534901	8761	60.3
				TAA CAT CCT AGA A
AJ_P8	85653563	H12	AJ_768	/5AmMC6/CCC AAG ACC TTG ACT	106586462	8795	61.7
				GGA GAA ACG GTA A
AJ_P9	85653565	A01	AJ_769	/5AmMC6/CCC AAG CTC ACT ACC	106534903	8672	60.6
				ATT GTC ATT GGA A
AJ_P9	85653566	A02	AJ_770	/5AmMC6/CCC AAT CCG TTA CGT	106534904	8770	60.6
				GAA GGG TAA ACA A
AJ_P9	85653567	A03	AJ_771	/5AmMC6/CCC AAT ACA GAC TGC	106534905	8699	60.1
				ACA CTC AGG TAA A
AJ_P9	85653568	A04	AJ_772	/5AmMC6/CCC AAT TTA CGT AGT	106534906	8696	59.3
				CCA ACT TGC GAA A
AJ_P9	85653569	A05	AJ_773	/5AmMC6/CCC AAG ACC TTA CTA	106534907	8690	59.4
				CCT GAA GCA GTA A
AJ_P9	85653570	A06	AJ_774	/5AmMC6/CCC AAC ATT GTT TCT CTG	106534908	8687	59.4
				ACA AGC TGA A
AJ_P9	85653571	A07	AJ_775	/5AmMC6/CCC AAC AGC AGT TTA	106534909	8739	61
				GCC AAG AAG TCA A
AJ_P9	85653572	A08	AJ_776	/5AmMC6/CCC AAG ACC TTG GAC	106534910	8657	60.9
				TCT CTC TAA CGA A
AJ_P9	85653573	A09	AJ_777	/5AmMC6/CCC AAG TAC TTT CTT CCA	106534911	8672	60.1
				GTC AGA GCA A
AJ_P9	85653574	A10	AJ_778	/5AmMC6/CCC AAT CAG ACA ACC	106534912	8681	60.7
				TTG TTC ATC GGA A
AJ_P9	85653575	A11	AJ_779	/5AmMC6/CCC AAT CAC CTG TTG	106534913	8712	60.7
				CAT TCA TAG GGA A
AJ_P9	85653576	A12	AJ_780	/5AmMC6/CCC AAT TTG CAG TGA	106534914	8714	59.8
				ACA CCA ACA GTA A
AJ_P9	85653577	B01	AJ_781	/5AmMC6/CCC AAG TCT GCA GTA	106534915	8699	60.4
				ACA CAC CAA GTA A
AJ_P9	85653578	B02	AJ_782	/5AmMC6/CCC AAT GTC TCA GTC	106534916	8672	59.7
				TCC ACA TTA GGA A
AJ_P9	85653579	B03	AJ_783	/5AmMC6/CCC AAG TAC ACC ATT	106534917	8672	61.2
				TCG CAT TTC GGA A
AJ_P9	85653580	B04	AJ_784	/5AmMC6/CCC AAG CTA CCA CTT	106534918	8730	60
				TAG AAG TAG GCA A
AJ_P9	85653581	B05	AJ_785	/5AmMC6/CCC AAT CAC AAG GTT	106534919	8739	60
				ACC ACA GGA GTA A
AJ_P9	85653582	B06	AJ_786	/5AmMC6/CCC AAC ACC ATG GAC	106534920	8715	61.9
				ACT TCT AAG GGA A
AJ_P9	85653583	B07	AJ_787	/5AmMC6/CCC AAC CTG AAA GAG	106534921	8736	59.3
				TTT CTT GCG TAA A
AJ_P9	85653584	B08	AJ_788	/5AmMC6/CCC AAG AGA CGT GTC	106534922	8706	61.3
				ATC TCA TCC AGA A
AJ_P9	85653585	B09	AJ_789	/5AmMC6/CCC AAT AGC GTA GAC	106534923	8723	59.2
				AAC TTC AAA GCA A
AJ_P9	85653586	B10	AJ_790	/5AmMC6/CCC AAA GTT CTC TCG TTC	106534924	8687	58.6
				ATA GCT GAA A
AJ_P9	85653587	B11	AJ_791	/5AmMC6/CCC AAA TTG GTC TTC	106534925	8736	59.7
				TGC ATA AAG CGA A
AJ_P9	85653588	B12	AJ_792	/5AmMC6/CCC AAT CGA AGG AGT	106534926	8761	58.8
				AGT CTA CCT GTA A
AJ_P9	85653589	C01	AJ_793	/5AmMC6/CCC AAT CAG GAC TAC	106534927	8715	61.8
				GGA AAG TTC CCA A
AJ_P9	85653590	C02	AJ_794	/5AmMC6/CCC AAC CGT AAC ATC	106534928	8675	62
				CAT GAG ACG TCA A
AJ_P9	85653591	C03	AJ_795	/5AmMC6/CCC AAT GCG AAA GAG	106534929	8770	60.8
				GTA CCG TTT ACA A
AJ_P9	85653592	C04	AJ_796	/5AmMC6/CCC AAG ACA CAT CCA	106534930	8675	62.1
				ACT GGT GAC TCA A
AJ_P9	85653593	C05	AJ_797	/5AmMC6/CCC AAG ACC ATC CTT	106534931	8675	61.7
				CAA GAG ACG TCA A
AJ_P9	85653594	C06	AJ_798	/5AmMC6/CCC AAG CTC TCA AGT	106534932	8690	60.6
				CTA AAC AGT GCA A
AJ_P9	85653595	C07	AJ_799	/5AmMC6/CCC AAC AAA GTA GAA	106534933	8763	58.6
				ACT CGT AGC TGA A
AJ_P9	85653596	C08	AJ_800	/5AmMC6/CCC AAC CAG AGT GTG	106534934	8795	61.3
				AAC ACT AGG GTA A
AJ_P9	85653597	C09	AJ_801	/5AmMC6/CCC AAC CTC ATG AAG	106534935	8715	61.5
				ACT CCA AGG GTA A
AJ_P9	85653598	C10	AJ_802	/5AmMC6/CCC AAA CCT GTG GAC	106534936	8666	62.1
				ACT ACA CCT TGA A
AJ_P9	85653599	C11	AJ_803	/5AmMC6/CCC AAA GTT CAG AGT	106534937	8672	59.9
				TCT CTC CAC TGA A
AJ_P9	85653600	C12	AJ_804	/5AmMC6/CCC AAG CTA CTT TCA	106534938	8721	60.4
				ACT GAC AGT GGA A
AJ_P9	85653601	D01	AJ_805	/5AmMC6/CCC AAG CCA TCT TCT	106534939	8681	60.1
				ACT GAA CGG TAA A
AJ_P9	85653602	D02	AJ_806	/5AmMC6/CCC AAT GTT TCA GTC	106534940	8687	59.4
				CAT TGA ACG CTA A
AJ_P9	85653603	D03	AJ_807	/5AmMC6/CCC AAA TTG CTT CTC	106534941	8687	59.1
				ACG TCA TTA GGA A
AJ_P9	85653604	D04	AJ_808	/5AmMC6/CCC AAT GGG AAC TCT	106534942	8739	60.8
				GAA ACA TCC GAA A
AJ_P9	85653605	D05	AJ_809	/5AmMC6/CCC AAT CGT AGA GTC	106534943	8723	58.4
				AAA CCA CAA GTA A
AJ_P9	85653606	D06	AJ_810	/5AmMC6/CCC AAC AGG TGT CGT	106534944	8786	62.5
				GTG AAA CAG TCA A
AJ_P9	85653607	D07	AJ_811	/5AmMC6/CCC AAG GTC ATT AAG	106534945	8657	62
				CCT TCG ACT CCA A
AJ_P9	85653608	D08	AJ_812	/5AmMC6/CCC AAC TTG AAG TGA	106534946	8779	61.3
				AGG CAA CCA TGA A
AJ_P9	85653609	D09	AJ_813	/5AmMC6/CCC AAC AAC TAG GAG	106534947	8761	60.1
				TGC TCT GGT TAA A
AJ_P9	85653610	D10	AJ_814	/5AmMC6/CCC AAG ACC ATA GCA	106534948	8675	61.9
				TCC AAG TCG TCA A
AJ_P9	85653611	D11	AJ_815	/5AmMC6/CCC AAT CGA GAA ACA	106534949	8723	58.4
				CCT GTA CAA GTA A
AJ_P9	85653612	D12	AJ_816	/5AmMC6/CCC AAC AGT CTT TAA	106534950	8754	58.3
				GCA GAA GGA CTA A
AJ_P9	85653613	E01	AJ_817	/5AmMC6/CCC AAC GTC AAC TAC	106534951	8699	59.8
				ACA GAA GGT CTA A
AJ_P9	85653614	E02	AJ_818	/5AmMC6/CCC AAG TCG ACA ACA	106534952	8730	60
				GCA TTA GGT CTA A
AJ_P9	85653615	E03	AJ_819	/5AmMC6/CCC AAT TGG TCA GAA	106534953	8687	59.9
				CTT TCC TTG CAA A
AJ_P9	85653616	E04	AJ_820	/5AmMC6/CCC AAC CTA GGT CAA	106534954	8752	60.5
				GTT TAG GTT GCA A
AJ_P9	85653617	E05	AJ_821	/5AmMC6/CCC AAG TCA TCT GCA	106534955	8666	61.6
				TCC ACA CTA GGA A
AJ_P9	85653618	E06	AJ_822	/5AmMC6/CCC AAA TCG CTT GAA	106534956	8690	61
				CCA TAC CAT GGA A
AJ_P9	85653619	E07	AJ_823	/5AmMC6/CCC AAA TCT GAA CTG	106534957	8763	58.8
				AGG AAC AAG CTA A
AJ_P9	85653620	E08	AJ_824	/5AmMC6/CCC AAC GTG AGC ATC	106534958	8770	61.2
				AGG AAC ATT TGA A
AJ_P9	85653621	E09	AJ_825	/5AmMC6/CCC AAT CCC TAG TTC	106534959	8697	61.2
				CAG TCA TGA GGA A
AJ_P9	85653622	E10	AJ_826	/5AmMC6/CCC AAC TCC TAG TCC	106534960	8657	60.7
				TGT AGT CCA GAA A
AJ_P9	85653623	E11	AJ_827	/5AmMC6/CCC AAG AGT CAA CTC	106534961	8699	60.2
				CAT GAA AGC CTA A
AJ_P9	85653624	E12	AJ_828	/5AmMC6/CCC AAG GTA GTC TCA	106534962	8755	61
				GAG AAC ACC TGA A
AJ_P9	85653625	F01	AJ_829	/5AmMC6/CCC AAG CTG TAG GAC	106534963	8779	59.8
				ATA AGA ACC GTA A
AJ_P9	85653626	F02	AJ_830	/5AmMC6/CCC AAG TCC AAC TGA	106534964	8739	60.4
				AAC AGA GCT GTA A
AJ_P9	85653627	F03	AJ_831	/5AmMC6/CCC AAG TGC AAC TAC	106534965	8795	62.1
				AGG ACA GTG TGA A
AJ_P9	85653628	F04	AJ_832	/5AmMC6/CCC AAT GAA ACA GAC	106534966	8763	59.3
				AAG TAG CGT TCA A
AJ_P9	85653629	F05	AJ_833	/5AmMC6/CCC AAA AAC TGT AGC	106534967	8696	59.5
				TTT CCC TTG GAA A
AJ_P9	85653630	F06	AJ_834	/5AmMC6/CCC AAT CCG TAG AGC	106534968	8761	60
				AGT GAG TTT ACA A
AJ_P9	85653631	F07	AJ_835	/5AmMC6/CCC AAG GTT CAT GCA	106534969	8672	60.6
				TCC TCT TCA AGA A
AJ_P9	85653632	F08	AJ_836	/5AmMC6/CCC AAA CCT TTG TGG	106534970	8761	61.3
				AGT CAA GCA TGA A
AJ_P9	85653633	F09	AJ_837	/5AmMC6/CCC AAA CCT TTG TGA	106534971	8736	59.7
				GCA GAG CAT TTA A
AJ_P9	85653634	F10	AJ_838	/5AmMC6/CCC AAA CTG TTT CCC TTA	106534972	8672	60.5
				GAG CAG TCA A
AJ_P9	85653635	F11	AJ_839	/5AmMC6/CCC AAG CTG TAG GAG	106534973	8752	59.4
				TTA CAT CTC TGA A
AJ_P9	85653636	F12	AJ_840	/5AmMC6/CCC AAG TGG ACA CTC	106534974	8706	61.3
				CAG AAC TCT GTA A
AJ_P9	85653637	G01	AJ_841	/5AmMC6/CCC AAC GTC ATC TGA	106534975	8699	59.8
				CAG AAC AGA CTA A
AJ_P9	85653638	G02	AJ_842	/5AmMC6/CCC AAG TCC AAC GAA	106534976	8699	60.8
				GCA TGA CAC TTA A
AJ_P9	85653639	G03	AJ_843	/5AmMC6/CCC AAA GCC TAA AGC	106534977	8721	61.2
				CTT TGG GTT ACA A
AJ_P9	85653640	G04	AJ_844	/5AmMC6/CCC AAC CGT TCA AAC	106534978	8699	60.4
				GAC TAA GAG TCA A
AJ_P9	85653641	G05	AJ_845	/5AmMC6/CCC AAT CGG AAC ACC	106534979	8672	61.5
				TTT GGT TTC CAA A
AJ_P9	85653642	G06	AJ_846	/5AmMC6/CCC AAT GAC CAT CAT	106534980	8687	60
				GTT TGG CTT CAA A
AJ_P9	85653643	G07	AJ_847	/5AmMC6/CCC AAG ACC ATG AGC	106534981	8672	60.5
				TCT CTT GTT CAA A
AJ_P9	85653644	G08	AJ_848	/5AmMC6/CCC AAC TAG GTG AAG	106534982	8755	61.9
				TGA CAG CAT CCA A
AJ_P9	85653645	G09	AJ_849	/5AmMC6/CCC AAC AAG TTA GGA	106534983	8779	60.4
				GAC TGA CTG CAA A
AJ_P9	85653646	G10	AJ_850	/5AmMC6/CCC AAT CAG CAC ACG	106534984	8714	58.6
				AGT TCT AGT AAA A
AJ_P9	85653647	G11	AJ_851	/5AmMC6/CCC AAA CGT CAC CTA	106534985	8737	62.1
				GGT TGG GTT ACA A
AJ_P9	85653648	G12	AJ_852	/5AmMC6/CCC AAA CCT TGT CTC TTA	106534986	8672	60.6
				GCC ATG GAA A
AJ_P9	85653649	H01	AJ_853	/5AmMC6/CCC AAA CCT TGT TAC	106534987	8712	60.6
				TGT GCT AGA GCA A
AJ_P9	85653650	H02	AJ_854	/5AmMC6/CCC AAA CAG AGT GCT	106534988	8681	60.8
				TCC AAC TTC TGA A
AJ_P9	85653651	H03	AJ_855	/5AmMC6/CCC AAT CGT TCA CGA	106534989	8761	60.2
				AGT AGG GTT ACA A
AJ_P9	85653652	H04	AJ_856	/5AmMC6/CCC AAA AAC ATG TTC	106534990	8705	60.1
				CGT AGT TGC CAA A
AJ_P9	85653653	H05	AJ_857	/5AmMC6/CCC AAT GAC CAC AAC	106534991	8699	60.9
				ATA GCA TGT CGA A
AJ_P9	85653654	H06	AJ_858	/5AmMC6/CCC AAG CAT AAA CAC	106534992	8739	60.2
				TCT GGA CAG GTA A
AJ_P9	85653655	H07	AJ_859	/5AmMC6/CCC AAG CTA ACA ACC	106534993	8699	59.7
				ATC GAG AGT CTA A
AJ_P9	85653656	H08	AJ_860	/5AmMC6/CCC AAG TGA AAC TCA	106534994	8699	59.7
				CAC GAG ACT CTA A
AJ_P9	85653657	H09	AJ_861	/5AmMC6/CCC AAG TAA CAA ACC	106534995	8739	60.9
				CAT GAG CTG TGA A
AJ_P9	85653658	H10	AJ_862	/5AmMC6/CCC AAG TCG ACA TCA	106534996	8755	61.9
				CAG TCA AGG TGA A
AJ_P9	85653659	H11	AJ_863	/5AmMC6/CCC AAG AAC TCT CTC	106534997	8672	60.4
				TGC ACA TTG TGA A
AJ_P9	85653660	H12	AJ_864	/5AmMC6/CCC AAC TGC ACA CAT	106534998	8687	60.1
				GGT TTC TTT GAA A
AJ_P10	85653662	A01	AJ_865	/5AmMC6/CCC AAT AAA GCA CTT	106534999	8754	58.7
				TGA GAG TAC CGA A
AJ_P10	85653663	A02	AJ_866	/5AmMC6/CCC AAA TCG CTT GTT	106535000	8687	59.1
				TAA CCT ACT GGA A
AJ_P10	85653664	A03	AJ_867	/5AmMC6/CCC AAC GTT GAG TTT	106535001	8745	59
				AAG CTA CCA GAA A
AJ_P10	85653665	A04	AJ_868	/5AmMC6/CCC AAG TTT CAC TAC	106535002	8681	60
				ACG ACT TCG AGA A
AJ_P10	85653666	A05	AJ_869	/5AmMC6/CCC AAT GGA GAC AGT	106535003	8712	60.4
				CTT CCC TTT GAA A
AJ_P10	85653667	A06	AJ_870	/5AmMC6/CCC AAG TTT CAC TGC	106535004	8712	61.1
				ACT TCA AGG TGA A
AJ_P10	85653668	A07	AJ_871	/5AmMC6/CCC AAC CAG TCT GGT	106535005	8657	61.2
				TCT ACT ACA CGA A
AJ_P10	85653669	A08	AJ_872	/5AmMC6/CCC AAA TTC TCG TTC TCA	106535006	8712	59.9
				GAG TCA GGA A
AJ_P10	85653670	A09	AJ_873	/5AmMC6/CCC AAG TTA CCA ACA	106535007	8699	60.1
				CCT GAG AAG CTA A
AJ_P10	85653671	A10	AJ_874	/5AmMC6/CCC AAA CTA CTG TCA	106535008	8779	60.1
				AAG GAG TAG GCA A
AJ_P10	85653672	A11	AJ_875	/5AmMC6/CCC AAG TTC CCA AGA	106535009	8675	62
				CCT ACA AGC TGA A
AJ_P10	85653673	A12	AJ_876	/5AmMC6/CCC AAT TTA GCC TAA	106535010	8714	59
				CAG CAA CAG GTA A
AJ_P10	85653674	B01	AJ_877	/5AmMC6/CCC AAA TCT GTT CTC TGC	106535011	8687	59
				AAA GTC GTA A
AJ_P10	85653675	B02	AJ_878	/5AmMC6/CCC AAA GTC CTT GTC	106535012	8681	60.3
				TCA AAC TCA GGA A
AJ_P10	85653676	B03	AJ_879	/5AmMC6/CCC AAA TCT TGT GTG	106535013	8736	59.3
				TCG AAG CAA CTA A
AJ_P10	85653677	B04	AJ_880	/5AmMC6/CCC AAG TGC AAC TGG	106535014	8770	60.4
				AGA CAG ACT TTA A
AJ_P10	85653678	B05	AJ_881	/5AmMC6/CCC AAA CTG TCT TGT	106535015	8696	59.3
				TCG AAC AGC ATA A
AJ_P10	85653679	B06	AJ_882	/5AmMC6/CCC AAT TTG TAC ATC	106535016	8687	59.4
				GCT TCA TCG GAA A
AJ_P10	85653680	B07	AJ_883	/5AmMC6/CCC AAT ACA GAA GGA	106535017	8739	58.9
				GTA CCT GAC CTA A
AJ_P10	85653681	B08	AJ_884	/5AmMC6/CCC AAT CGC AAA GAA	106535018	8714	59.4
				GTA CCA GTT TCA A
AJ_P10	85653682	B09	AJ_885	/5AmMC6/CCC AAC TGG TAG ACA	106535019	8779	60.4
				TGC ATA GAA GCA A
AJ_P10	85653683	B10	AJ_886	/5AmMC6/CCC AAG AGA ACT ACC	106535020	8795	62.2
				GTT GTG AAG GCA A
AJ_P10	85653684	B11	AJ_887	/5AmMC6/CCC AAT TTC GAG AGT	106535021	8714	58.8
				CAC ATC AAC AGA A
AJ_P10	85653685	B12	AJ_888	/5AmMC6/CCC AAC GGT AAG GCT	106535022	8712	60.1
				ACC TCT TTG TAA A
AJ_P10	85653686	C01	AJ_889	/5AmMC6/CCC AAC TAC GCT ACT	106535023	8723	58.5
				AAA GTA AAG GCA A
AJ_P10	85653687	C02	AJ_890	/5AmMC6/CCC AAC GTG AGT TCG	106535024	8721	60
				TTA ACT ACC AGA A
AJ_P10	85653688	C03	AJ_891	/5AmMC6/CCC AAT GGT CTA GCA	106535025	8681	60.2
				TTC AAC TAC CGA A
AJ_P10	85653689	C04	AJ_892	/5AmMC6/CCC AAT GTT TCA GAC	106535026	8672	60
				CTG ACT ACC TGA A
AJ_P10	85653690	C05	AJ_893	/5AmMC6/CCC AAT AAC AGA ACC	106577190	8699	60.3
				CAT GCT CAG GTA A
AJ_P10	85653691	C06	AJ_894	/5AmMC6/CCC AAA CAC GTT GCA	106535028	8687	60
				CTT TAC TTT GGA A
AJ_P10	85653692	C07	AJ_895	/5AmMC6/CCC AAT GCT GAC GTA	106535029	8723	59.3
				CAC AAA CAA GTA A
AJ_P10	85653693	C08	AJ_896	/5AmMC6/CCC AAA GCT GTT GCT	106535030	8736	59.9
				GTT AAA CCG TAA A
AJ_P10	85653694	C09	AJ_897	/5AmMC6/CCC AAC ATG TTG TGG	106535031	8761	60.8
				TAG CTA CCG AAA A
AJ_P10	85653695	C10	AJ_898	/5AmMC6/CCC AAA TCT CTG TGG	106535032	8761	60.2
				TAG CAT AAC GGA A
AJ_P10	85653696	C11	AJ_899	/5AmMC6/CCC AAG AGC TCT CGT	106535033	8736	57.8
				GTT ACT AAA GTA A
AJ_P10	85653697	C12	AJ_900	/5AmMC6/CCC AAA GCC TTG GTT	106535034	8727	59.5
				GTC AGT CTT AAA A
AJ_P10	85653698	D01	AJ_901	/5AmMC6/CCC AAG TAC CTC TAC	106535035	8657	60.2
				TCT GAC TCA GGA A
AJ_P10	85653699	D02	AJ_902	/5AmMC6/CCC AAG GCA TAC AAC	106535036	8666	61.9
				TCT GAC CTG TCA A
AJ_P10	85653700	D03	AJ_903	/5AmMC6/CCC AAC CAG TAA ACC	106535037	8675	62.6
				AGT GAC TTG CCA A
AJ_P10	85653701	D04	AJ_904	/5AmMC6/CCC AAG ACT CCT TGG	106535038	8721	60.6
				TTC AAC GGT AAA A
AJ_P10	85653702	D05	AJ_905	/5AmMC6/CCC AAC TTA GGT AGG	106535039	8770	59.7
				TAG CAC ACT GAA A
AJ_P10	85653703	D06	AJ_906	/5AmMC6/CCC AAA GTC CAG AGC	106535040	8714	58.8
				ACA TTT CAT AGA A
AJ_P10	85653704	D07	AJ_907	/5AmMC6/CCC AAG GCT ACA TGT	106535041	8675	61.6
				CAC CTA ACC AGA A
AJ_P10	85653705	D08	AJ_908	/5AmMC6/CCC AAT GTC CAT GAC	106535042	8672	60.4
				TTT CCT AAC GGA A
AJ_P10	85653706	D09	AJ_909	/5AmMC6/CCC AAG CAC ATG GTT	106535043	8699	61.2
				CCA CAT AAA CGA A
AJ_P10	85653707	D10	AJ_910	/5AmMC6/CCC AAG CCA TGT TGC	106535044	8699	61.4
				ACA CTA CAA AGA A
AJ_P10	85653708	D11	AJ_911	/5AmMC6/CCC AAA CGC ATC CAA	106535045	8739	60.9
				AGT TAG GGT ACA A
AJ_P10	85653709	D12	AJ_912	/5AmMC6/CCC AAC CAC TCG TAG	106535046	8706	60.1
				TCT ACT AGG AGA A
AJ_P10	85653710	E01	AJ_913	/5AmMC6/CCC AAA CTG TGT TGT	106535047	8752	59.8
				CTC ACT AGA GGA A
AJ_P10	85653711	E02	AJ_914	/5AmMC6/CCC AAG TAC TCC TAC	106535048	8657	61.1
				TCG TAC ATG GCA A
AJ_P10	85653712	E03	AJ_915	/5AmMC6/CCC AAT AAC ACG AAA	106535049	8714	59.9
				GCT TGT GCA TCA A
AJ_P10	85653713	E04	AJ_916	/5AmMC6/CCC AAT TTC TAG AAC	106535050	8687	59.6
				TGT GCT TGC ACA A
AJ_P10	85653714	E05	AJ_917	/5AmMC6/CCC AAG GTG TAC CTT	106535051	8777	61.7
				TGA CCA GTG AGA A
AJ_P10	85653715	E06	AJ_918	/5AmMC6/CCC AAG TTA CCT CTT	106535052	8681	60.1
				GCC ATA CGA GAA A
AJ_P10	85653716	E07	AJ_919	/5AmMC6/CCC AAG AAC GTT CTG	106535053	8714	59.6
				CTC ATA GCA AAA A
AJ_P10	85653717	E08	AJ_920	/5AmMC6/CCC AAA CGC TTC TTC ATT	106535054	8696	59.4
				GTA ACA GGA A
AJ_P10	85653718	E09	AJ_921	/5AmMC6/CCC AAG AGT CTC GAC	106535055	8657	59.8
				TCC TCT ACT AGA A
AJ_P10	85653719	E10	AJ_922	/5AmMC6/CCC AAG AGT ACA GAA	106535056	8690	59.8
				CCT CAC TTT CGA A
AJ_P10	85653720	E11	AJ_923	/5AmMC6/CCC AAG TAC TGC TGA	106535057	8699	60.3
				CAC AAC TAA CGA A
AJ_P10	85653721	E12	AJ_924	/5AmMC6/CCC AAA GCC TTT GGT	106535058	8761	60.1
				AGT CAG ACA GTA A
AJ_P10	85653722	F01	AJ_925	/5AmMC6/CCC AAA GGC TAC TCA	106535059	8714	59
				GAA CAA CTT TGA A
AJ_P10	85653723	F02	AJ_926	/5AmMC6/CCC AAG TAC CTC ACT	106535060	8675	61.6
				CAA GCA TCA GGA A
AJ_P10	85653724	F03	AJ_927	/5AmMC6/CCC AAA TAG TCT CAG	106535061	8752	60.3
				TGT GCT AGT GCA A
AJ_P10	85653725	F04	AJ_928	/5AmMC6/CCC AAG TCC ACT TTC	106535062	8697	62
				TGC ACT AAG GGA A
AJ_P10	85653726	F05	AJ_929	/5AmMC6/CCC AAC AGT GCT TGC	106535063	8723	60.3
				AAA CAT CAA AGA A
AJ_P10	85653727	F06	AJ_930	/5AmMC6/CCC AAA CTT GTC TCT CTG	106535064	8712	59.5
				AGT ACA GGA A
AJ_P10	85653728	F07	AJ_931	/5AmMC6/CCC AAA GTT CTC CAC	106535065	8730	60.2
				AAG TGT CAG AGA A
AJ_P10	85653729	F08	AJ_932	/5AmMC6/CCC AAG TCT TCA CAC	106535066	8690	60.4
				TCA GAA CGT GAA A
AJ_P10	85653730	F09	AJ_933	/5AmMC6/CCC AAT CCG AAG TTG	106535067	8754	59
				CGT AGA CTA AAA A
AJ_P10	85653731	F10	AJ_934	/5AmMC6/CCC AAG AAA CAT CGT	106535068	8699	60.1
				ACA CAG TCT CGA A
AJ_P10	85653732	F11	AJ_935	/5AmMC6/CCC AAG AAC CAT CAC	106535069	8675	62.4
				CTG TCA GCA TGA A
AJ_P10	85653733	F12	AJ_936	/5AmMC6/CCC AAC GAC ATA CCT	106535070	8739	60.5
				AAA GCA TGG TGA A
AJ_P10	85653734	G01	AJ_937	/5AmMC6/CCC AAG GTC ACA GCA	106535071	8666	61.9
				CTT TCC ACT AGA A
AJ_P10	85653735	G02	AJ_938	/5AmMC6/CCC AAC GAG TTA CAC	106535072	8705	59.6
				GTT TGC CTA AAA A
AJ_P10	85653736	G03	AJ_939	/5AmMC6/CCC AAG TAC GCT AGT	106535073	8681	58.9
				CTC TCA CAT AGA A
AJ_P10	85653737	G04	AJ_940	/5AmMC6/CCC AAA GTG TCT GAC	106535074	8681	59.9
				CAT ACT TAC CGA A
AJ_P10	85653738	G05	AJ_941	/5AmMC6/CCC AAC GTT CCA TAC	106535075	8699	60
				CAA GGA CAT AGA A
AJ_P10	85653739	G06	AJ_942	/5AmMC6/CCC AAG GAC TTC GAC	106535076	8681	59.1
				TTC CTA CTA AGA A
AJ_P10	85653740	G07	AJ_943	/5AmMC6/CCC AAT GAC GTT GTA	106535077	8681	60.5
				AAC CTC TCA CGA A
AJ_P10	85653741	G08	AJ_944	/5AmMC6/CCC AAG CAC TGT GTA	106535078	8690	61
				AAC AAC CTT CGA A
AJ_P10	85653742	G09	AJ_945	/5AmMC6/CCC AAC ATG TAG AGA	106535079	8763	58.1
				AAC TCT CGA GAA A
AJ_P10	85653743	G10	AJ_946	/5AmMC6/CCC AAC AGC TTC CTC	106535080	8672	59.5
				ATA GTC TTA GGA A
AJ_P10	85653744	G11	AJ_947	/5AmMC6/CCC AAG TCC TAC ACA	106535081	8675	61.3
				CAG TCA TAC GGA A
AJ_P10	85653745	G12	AJ_948	/5AmMC6/CCC AAG TCC ATA CAT	106535082	8666	62.3
				CCG AAC TGT GCA A
AJ_P10	85653746	H01	AJ_949	/5AmMC6/CCC AAG AAC TTC CAC	106535083	8681	61
				TTA GCA TGT GCA A
AJ_P10	85653747	H02	AJ_950	/5AmMC6/CCC AAG GTT CTA CAT	106535084	8690	60.7
				CAC GTA CGC AAA A
AJ_P10	85653748	H03	AJ_951	/5AmMC6/CCC AAG AGT GCT ACC	106535085	8730	60
				TTC GTA CAG AAA A
AJ_P10	85653749	H04	AJ_952	/5AmMC6/CCC AAA TAA GTC CTG	106535086	8763	58.8
				AAG GAA CGC ATA A
AJ_P10	85653750	H05	AJ_953	/5AmMC6/CCC AAG TGC AAC GAG	106535087	8739	61.4
				ACC TTT GAC AAA A
AJ_P10	85653751	H06	AJ_954	/5AmMC6/CCC AAG CAC TGT TGA	106535088	8681	61.5
				AAC CCT TTC GAA A
AJ_P10	85653752	H07	AJ_955	/5AmMC6/CCC AAA CTC GTC ACC	106535089	8681	61
				TTT GGG TAA ACA A
AJ_P10	85653753	H08	AJ_956	/5AmMC6/CCC AAA GCC TTC TTG	106535090	8721	60.2
				GTC ATA GAC AGA A
AJ_P10	85653754	H09	AJ_957	/5AmMC6/CCC AAC AAC GGT ACT	106535091	8752	61.1
				TTG TTG GTA GCA A
AJ_P10	85653755	H10	AJ_958	/5AmMC6/CCC AAC ATT CTG GTG	106535092	8752	60.6
				TTA CGA ACT GGA A
AJ_P10	85653756	H11	AJ_959	/5AmMC6/CCC AAT GAA ACC ATC	106535093	8699	61
				CAT GTC AGA GCA A
AJ_P10	85653757	H12	AJ_960	/5AmMC6/CCC AAA ACT GAC CAT	106535094	8761	61.9
				TGT GGT GTG CAA A

Example 11

Titration or Dilution Series

The ability to perform multiple experiments in parallel enables straightforward exploration of the counting results from samples with a range of starting concentrations or amounts of target, sometimes known as a titration experiment or a dilution series.

An example of dilution series data for a labeled RPLPO gene sequence is shown in FIG. 26. Plotted in the figure is the counting result N for each array where the nominal starting concentration of mRNA in the reaction is shown on the X axis.

Some of the graphical output from the analysis software for the same experiment is shown in FIG. 27. For each array, there is a compound display with the following elements: (i) an intensity histogram (green) for the index spots, (ii) a blue line registered with the histogram, showing the dynamic threshold for spot counting, (iii) a 32×32 grid which is a digital representation of each array. A white site in the grid denotes a spot whose intensity was above the dynamic threshold, and a black site denotes that the intensity was below the dynamic threshold, and (iv) the result N and the quality score Q is reported for each array as text.

While preferred embodiments of the present invention have been shown and described herein, it will be obvious to those skilled in the art that such embodiments may be provided by way of example only. Numerous variations, changes, and substitutions will now occur to those skilled in the art without departing from the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed in practicing the invention.

Claims

1. An array reader system comprising a computer system comprising a digital processor configured to execute computer-executable instructions to at least:(a) acquiring image data of a region of an array, wherein the array comprises a plurality of stochastically labeled and non-labeled features, and wherein the features comprise oligonucleotide probes;(b) measuring a signal intensity and a local background intensity for one of the plurality of stochastically labeled and non-labeled features based on the acquired image data;(c) calculating a local background corrected signal intensity for the one of the plurality of stochastically labeled and non-labeled features using the signal intensity and the local background intensity measured in (b);(d) transforming the local background corrected signal intensity calculated in (c) for the one of the plurality of stochastically labeled and non-labeled features to determine the number of stochastically labeled and non-labeled features in the of the array; and(e) calculating the absolute number of a target molecule based on the number of stochastically labeled and non-labeled features determined in (d) within the region of the array.

2. The array reader system of claim 1, further comprising an optical imaging system that comprises one or more image sensors and an illumination system, wherein the illumination system comprises at least one light source; and wherein acquiring the image data of the region of the array comprises acquiring the image data of the region of the array from the optical imaging system.

3. The array reader system of claim 1, wherein the array comprises a microarray, microscope slide, or microwell plate.

4. The array reader system of claim 2, wherein the optical imaging system has a magnification of less than 1, equal to 1, or greater than 1.

5. The array reader system of claim 2, wherein the optical imaging system comprises a fluorescence imaging system.

6. The array reader system of claim 2, wherein the optical imaging system comprises a phosphorescence imaging system.

7. The array reader system of claim 2, wherein the optical imaging system comprises an imaging system that operates in a transmitted light, reflected light, or scattered light imaging mode, or combinations thereof.

8. The array reader system of claim 2, wherein the one or more image sensors have a resolution of at least 320×240 pixels.

9. The array reader system of claim 2, wherein the one or more image sensors are CCD image sensors.

10. The array reader system of claim 2, wherein the one or more image sensors are CMOS Image sensors.

11. The array reader system of claim 2, wherein the one or more image sensors comprise one or more circuit boards.

12. The array reader system of claim 2, wherein the optical imaging system further comprises one or more components selected from the group including, but not limited to, a microscope objective, a camera lens, a finite-conjugate lens, an infinite-conjugate lens, a plano-convex lens, a double convex lens, a plano-concave lens, a double concave lens, an achromatic cemented doublet, or a bandpass filter.

13. The array reader system of claim 5, wherein the fluorescence imaging system is designed for use with fluorescein, Cy3, Cy5, or phycoerythrin fluorophores.

14. The array reader system of claim 2, wherein the at least one light source is an LED or LED assembly.

15. The array reader system of claim 2, wherein the at least one light source is electronically synchronized with the image sensor, the at least one light source being turned on when the image sensor is acquiring an image and turned off when the image sensor is not acquiring an image.

16. The array reader system of claim 2, wherein the illumination system is an off-axis illumination system.

17. The array reader system of claim 16, wherein the off-axis illumination system satisfies the Scheimpflug condition.

18. The array reader system of claim 16, wherein the off-axis illumination system does not satisfy the Scheimpflug condition.

19. The array reader system of claim 16, wherein the off-axis illumination subsystem is a Kohler illumination system.

20. The array reader system of claim 16, wherein the off-axis illumination system is an Abbe illumination system.

21. The array reader system of claim 2, wherein the illumination system is an epi-illumination system.

22. The array reader system of claim 21, wherein the epi-illumination system is a Kohler illumination system.

23. The array reader system of claim 21, wherein the epi-illumination system is an Abbe illumination system.

24. The array reader system of claim 2, wherein the illumination system is a trans-illumination system.

25. The array reader system of claim 24, wherein the trans-illumination system is a Kohler illumination system.

26. The array reader system of claim 24, wherein the trans-illumination system is an Abbe illumination system.

27. The array reader system of claim 2, wherein the optical imaging system further comprises a translation stage.

28. The array reader system of claim 27, wherein the translation stage is a single-axis translation stage.

29. The array reader system of claim 27, wherein the translation stage is a dual-axis translation stage.

30. The array reader system of claim 27, wherein the translation stage is a multi-axis translation stage.

31. The array reader system of claim 1, wherein the digital processor is configured to automatically locates features of the array within the acquired image data.

32. The array reader system of claim 1, wherein calculating the local background corrected signal intensity for the one of the plurality of stochastically labeled and non-labeled features using the signal intensity and the local background intensity measured in (b) comprises: (i) centering a predefined analysis window on an array feature within an image,(ii) calculating an intensity value statistic for signal and background pixels according to a predefined pattern of pixels within the array feature, and(iii) utilizing the intensity value statistic for signal and background pixels to calculate the local background corrected signal intensity for the array feature.

33. The array reader system of claim 32, wherein the digital processor is configured to perform a k-means clustering analysis of the background corrected signal intensity values for the array features to determine a dynamic signal intensity threshold for discrimination between the plurality of stochastically labeled and non-labeled features of the array.

34. The array reader system of claim 32, wherein the digital processor is configured to perform a k-medoids clustering analysis of the background corrected signal intensity values for the array features to determine a dynamic signal intensity threshold for discrimination between the plurality of stochastically labeled and non-labeled features of the array.

35. The array reader system of claim 32, wherein the digital processor is configured to perform a mixture model statistical analysis of the background corrected signal intensity values for the array features to determine a dynamic signal intensity threshold for discrimination between the plurality of stochastically labeled and non-labeled features of the array.

36. The array reader system of claim 32, wherein the digital processor is configured to perform an empirical analysis based on sorting of background corrected signal intensity values for the array features to determine a dynamic signal intensity threshold for discrimination between the plurality of stochastically labeled and non-labeled features of the array.

37. The array reader system of claim 32, wherein the digital processor is configured to perform an empirical analysis based on sorting of pairwise differences in background corrected signal intensity values for the array features to determine a dynamic signal intensity threshold for discrimination between the plurality of stochastically labeled and non-labeled features of the array.

38. The array reader system of claim 32, wherein the digital processor is configured to perform one or more statistical analyses of the background corrected signal intensity values for the array features to determine a dynamic signal intensity threshold for discrimination between the plurality of stochastically labeled and non-labeled features of the array, and wherein the one or more statistical analyses are selected from the list comprising k-means clustering, k-medoids clustering, mixture model statistical analysis, an empirical analysis, or any combination thereof.

39. The array reader system of claim 1, wherein calculating the absolute number of the target molecule based on the number of stochastically labeled and non-labeled features determined in (d) within the region of the array comprises calculate the absolute number of the target molecule based on the number of stochastically labeled features, the number of non-labeled features within the region of the array, and the predictions of the Poisson distribution.

40. The array reader system of claim 1, wherein the digital processor is configured to calculate a confidence interval for the number of target molecules.

41. The array reader system of claim 2, wherein the optical imaging system and computer system are combined within a single, stand-alone instrument.

42. The array reader system of claim 2, wherein the optical imaging system and computer system are configured as separate instrument modules.

43. The array reader system of claim 32, wherein the digital processor is configured to perform an analysis of the local background corrected signal intensities for the array features to determine a dynamic signal intensity threshold, and wherein the analysis comprises fitting a model function to the local background corrected signal intensities by varying model parameters.

44. The array reader system of claim 32, wherein the digital processor is configured to perform an analysis of the local background corrected signal intensities for the array features to determine a dynamic signal intensity threshold, and wherein the analysis comprises maximizing a quality metric relating to a statistical difference between feature intensities above the threshold and feature intensities below the threshold.