Abstract: Detecting analytes using proximity-induced tagmentation, strand invasion, restriction, or ligation is provided herein. In some examples, detecting an analyte includes coupling a donor recognition probe to a first portion of the analyte. The donor recognition probe includes a first recognition element specific to the first portion of the analyte, a first oligonucleotide corresponding to the first portion, and a transposase coupled to the first recognition element and the first oligonucleotide. An acceptor recognition probe is coupled to a second portion of the analyte. The acceptor recognition probe includes a second recognition element specific to the second portion of the analyte and a second oligonucleotide coupled to the second recognition element and corresponding to the second portion. The transposase is used to generate a reporter polynucleotide including the first and second oligonucleotides. The analyte is detected based on the reporter including comprising the first and second oligonucleotides.

Abstract: The technology disclosed relates to artificial intelligence-based determination of analyte data for base calling. In particular, the technology disclosed uses input image data that is derived from a sequence of images. Each image in the sequence of images represents an imaged region and depicts intensity emissions indicative of one or more analytes and a surrounding background of the intensity emissions at a respective one of a plurality of sequencing cycles of a sequencing run. The input image data comprises image patches extracted from each image in the sequence of images. The input image data is processed through a neural network to generate an alternative representation of the input image data. The alternative representation is processed through an output layer to generate an output indicating properties of respective portions of the imaged region.

Abstract: A method of processing sequence data comprising a known location of the start of a copy number variant breakpoint to generate a prediction for the location of the end of the copy number variant breakpoint. The method comprises an encoder and a copy number variation (CNV) caller guide. The encoder processes an anchor sequence and corresponding subject candidate sequence to generate a learned representation of the anchor sequence and a learned representation of the corresponding subject candidate sequence. The CNV caller guide determines a similarity between the learned representation of the anchor sequence and a learned representation of the corresponding subject candidate sequence. Similarity between anchor sequence and subject candidate sequence is used as a proxy for likelihood that the end of the CNV breakpoint is located on the subject candidate sequence.

Abstract: The technology disclosed relates to generating ground truth training data to train a neural network-based template generator for cluster metadata determination task. In particular, it relates to accessing sequencing images, obtaining, from a base caller, a base call classifying each subpixel in the sequencing images as one of four bases (A, C, T, and G), generating a cluster map that identifies clusters as disjointed regions of contiguous subpixels which share a substantially matching base call sequence, determining cluster metadata based on the disjointed regions in the cluster map, and using the cluster metadata to generate the ground truth training data for training the neural network-based template generator for the cluster metadata determination task.

Abstract: The technology disclosed relates to artificial intelligence-based base calling. The technology disclosed relates to accessing a progression of per-cycle analyte channel sets generated for sequencing cycles of a sequencing run, processing, through a neural network-based base caller (NNBC), windows of per-cycle analyte channel sets in the progression for the windows of sequencing cycles of the sequencing run such that the NNBC processes a subject window of per-cycle analyte channel sets in the progression for the subject window of sequencing cycles of the sequencing run and generates provisional base call predictions for three or more sequencing cycles in the subject window of sequencing cycles, from multiple windows in which a particular sequencing cycle appeared at different positions, using the NNBC to generate provisional base call predictions for the particular sequencing cycle, and determining a base call for the particular sequencing cycle based on the plurality of base call predictions.

Abstract: Defocus is introduced during sequencing by synthesis by tilt of a flow cell and by variations in flatness of the flow cell. Effects of the defocus are reduced, and base calling quality is improved using techniques relating to dependence of base calling on flow cell tilt. For example, the flow cell surface height is measured throughout the flow cell. A focal height of an imager having a sensor for the sequencing is set, optionally adaptively, one or more times during the sequencing. Each image captured by the sensor is partitioned, e.g., based on differences between focal height and the measured flow cell surface height across areas of the sensor. Filters, e.g., related to defocus correction, are selected based at least in part on the difference between the focal height and the measured flow cell surface height at a particular area of the image being corrected for defocus.

Abstract: This disclosure describes methods, non-transitory computer readable media, and systems that can use a machine-learning model to classify or predict a probability of an oligonucleotide probe yielding an accurate genotype call or hybridizing with a target oligonucleotide—based on the oligonucleotide probe's nucleotide-sequence composition. To intelligently identify oligonucleotide probes that are more likely to yield accurate downstream genotyping—or more likely to successfully hybridize with target oligonucleotides—some embodiments of the disclosed machine-learning model include customized layers trained to detect motifs or other nucleotide-sequence patterns that correlate with favorable or unfavorable probe accuracy.

Abstract: We disclose a computer-implemented method of base calling. The technology disclosed accesses a time series sequence of a read. Respective time series elements in the time series sequence represent respective bases in the read. Then, a composite sequence for the read is generated based on respective aggregate transformations of respective sliding windows of time series elements in the time series sequence. A subject composite element in the composite sequence is generated based on an aggregate transformation of a corresponding window of time series elements in the time series sequence. Then, the composite sequence is processed as an aggregate and generates a base call sequence that has respective base calls for the respective bases in the read.

Abstract: Artificial intelligence driven signal enhancement of sequencing images enables enhanced sequencing by synthesis that determines a sequence of bases in genetic material with any one or more of: improved performance, improved accuracy, and/or reduced cost. A training set of images taken at unreduced and reduced power levels used to excite fluorescence during sequencing by synthesis is used to train a neural network to enable the neural network to recover enhanced images, as if taken at the unreduced power level, from unenhanced images taken at the reduced power level.

Abstract: The technology disclosed relates to artificial intelligence-based base calling. The technology disclosed relates to accessing a progression of per-cycle analyte channel sets generated for sequencing cycles of a sequencing run, processing, through a neural network-based base caller (NNBC), windows of per-cycle analyte channel sets in the progression for the windows of sequencing cycles of the sequencing run such that the NNBC processes a subject window of per-cycle analyte channel sets in the progression for the subject window of sequencing cycles of the sequencing run and generates provisional base call predictions for three or more sequencing cycles in the subject window of sequencing cycles, from multiple windows in which a particular sequencing cycle appeared at different positions, using the NNBC to generate provisional base call predictions for the particular sequencing cycle, and determining a base call for the particular sequencing cycle based on the plurality of base call predictions.

Abstract: Artificial intelligence driven enhancement of motion blurred sequencing images enables enhanced sequencing that determines a sequence of bases in genetic material with any one or more of: improved performance, improved accuracy, and/or reduced cost. A training set of images taken after unreduced and reduced movement settling times during sequencing is used to train a neural network to enable the neural network to recover enhanced images, as if taken after the unreduced movement settling time, from unenhanced images taken after the reduced movement settling time.

Abstract: A method of progressively training a base caller is disclosed. The method includes initially training a base caller, and generating labelled training data using the initially trained base caller; and (i) further training the base caller with analyte comprising organism base sequences, and generating labelled training data using the further trained base caller. The method includes iteratively further training the base caller by repeating step (i) for N iterations, which includes further training the base caller for N1 iterations of the N iterations with analyte comprising a first organism base sequence, and further training the base caller for N2 iterations of the N iterations with analyte comprising a second organism base sequence. A complexity of neural network configurations loaded in the base caller monotonically increases with the N iterations, and labelled training data generated during an iteration is used to train the base caller during an immediate subsequent iteration.

Abstract: Techniques for improving artificial intelligence-based base calling are disclosed. The improved techniques can be used to better train artificial intelligence for base calling by reordering of sequencing images, and training of a neural network-based base caller where the temporal logic is effectively “frozen” (or bypassed). In addition, the improved techniques include various combinations, including, for example, combining “normalization” of sequencing images with reordering of sequencing images and/or with effectively “freezing” the temporal logic.

Abstract: A system, a method and a non-transitory computer readable storage medium for base calling are described. The base calling method includes processing through a neural network first image data comprising images of clusters and their surrounding background captured by a sequencing system for one or more sequencing cycles of a sequencing run. The base calling method further includes producing a base call for one or more of the clusters of the one or more sequencing cycles of the sequencing run.

Abstract: A method of progressively training a base caller is disclosed. The method includes iteratively initially training a base caller with analyte comprising a single-oligo base sequence, and generating labelled training data using the initially trained base caller. At operations (i), the base caller is further trained with analyte comprising multi-oligo base sequences, and labelled training data is generated using the further trained base caller. Operations (i) are iteratively repeated to further train the base caller. In an example, during at least one iteration, a complexity of neural network configuration loaded within the base caller is increased. In an example, labelled training data generated during an iteration is used to train the base caller during an immediate subsequent iteration.

Abstract: The technology disclosed relates to generating ground truth training data to train a neural network-based template generator for cluster metadata determination task. In particular, it relates to accessing sequencing images, obtaining, from a base caller, a base call classifying each subpixel in the sequencing images as one of four bases (A, C, T, and G), generating a cluster map that identifies clusters as disjointed regions of contiguous subpixels which share a substantially matching base call sequence, determining cluster metadata based on the disjointed regions in the cluster map, and using the cluster metadata to generate the ground truth training data for training the neural network-based template generator for the cluster metadata determination task.

Abstract: The technology disclosed processes a first input through a first neural network and produces a first output. The first input comprises first image data derived from images of analytes and their surrounding background captured by a sequencing system for a sequencing run. The technology disclosed processes the first output through a post-processor and produces metadata about the analytes and their surrounding background. The technology disclosed processes a second input through a second neural network and produces a second output. The second input comprises third image data derived by modifying second image data based on the metadata. The second image data is derived from the images of the analytes and their surrounding background. The second output identifies base calls for one or more of the analytes at one or more sequencing cycles of the sequencing run.

Abstract: The technology disclosed relates to generating ground truth training data to train a neural network-based template generator for cluster metadata determination task. In particular, it relates to accessing sequencing images, obtaining, from a base caller, a base call classifying each subpixel in the sequencing images as one of four bases (A, C, T, and G), generating a cluster map that identifies clusters as disjointed regions of contiguous subpixels which share a substantially matching base call sequence, determining cluster metadata based on the disjointed regions in the cluster map, and using the cluster metadata to generate the ground truth training data for training the neural network-based template generator for the cluster metadata determination task.

Abstract: The technology disclosed relates to artificial intelligence based determination of analyte data for base calling. In particular, the technology disclosed uses input image data that is derived from a sequence of images. Each image in the sequence of images represents an imaged region and depicts intensity emissions indicative of one or more analytes and a surrounding background of the intensity emissions at a respective one of a plurality of sequencing cycles of a sequencing run. The input image data comprises image patches extracted from each image in the sequence of images. The input image data is processed through a neural network to generate an alternative representation of the input image data. The alternative representation is processed through an output layer to generate an output indicating properties of respective portions of the imaged region.

Abstract: The technology disclosed predicts quality of base calling during an extended optical base calling process. The base calling process includes pre-prediction base calling process cycles and at least two times as many post-prediction base calling process cycles as pre-prediction cycles. A plurality of time series from the pre-prediction base calling process cycles is given as input to a trained convolutional neural network. The convolutional neural network determines from the pre-prediction base calling process cycles, a likely overall base calling quality expected after post-prediction base calling process cycles. When the base calling process includes a sequence of paired reads, the overall base calling quality time series of the first read is also given as an additional input to the convolutional neural network to determine the likely overall base calling quality after post-prediction cycles of the second read.