Abstract: The technology disclosed corrects inter-cluster intensity profile variation for improved base calling on a cluster-by-cluster basis. The technology disclosed accesses current intensity data and historic intensity data of a target cluster, where the current intensity data is for a current sequencing cycle and the historic intensity data is for one or more preceding sequencing cycles. A first accumulated intensity correction parameter is determined by accumulating distribution intensities measured for the target cluster at the current and preceding sequencing cycles. A second accumulated intensity correction parameter is determined by accumulating intensity errors measured for the target cluster at the current and preceding sequencing cycles. Based on the first and second accumulated intensity correction parameters, next intensity data for a next sequencing cycle is corrected to generate corrected next intensity data, which is used to base call the target cluster at the next sequencing cycle.

Abstract: The technology disclosed extracts intensities from sequencing images for base calling target clusters and attenuates spatial crosstalk from neighboring clusters. The technology disclosed accesses a particular section from a plurality of sections of an image output by a sensor, the particular section of the image including at least one pixel depicting intensity emission values from a target cluster and neighboring clusters located across the sensor, and convolves the particular section of the image with a corresponding convolution kernel in a plurality of convolution kernels, to generate a feature map comprising a plurality of feature values. The technology disclosed further assigns a corresponding feature value to the target cluster based on feature values in the plurality of feature values adjoining a center of the target cluster, and processes the corresponding feature value assigned to the target cluster, to base call the target cluster.

Abstract: This disclosure describes methods, non-transitory computer readable media, and systems that can generate genotype calls from a combined pipeline for processing nucleotide reads from multiple read types/sources for robust, accurate genotype calls. For example, the disclosed systems can train and/or utilize a genotype-call-integration machine-learning model to generate predictions for genotype calls based on data associated with a first type of nucleotide reads (e.g., short reads) and a second type of nucleotide reads (e.g., long reads). As disclosed, the disclosed systems can determine sequencing metrics and can utilize a genotype-call-integration machine-learning model to generate predictions (e.g., genotype probabilities, variant call classifications) for generating output genotype calls based on the sequencing metrics.

Abstract: This disclosure describes methods, non-transitory computer readable media, and systems that can utilize a machine-learning model to refine structural variant calls of a call generation model. For example, the disclosed systems can train and utilize a structural variant refinement machine-learning model to reduce false positives and/or false negatives. Indeed, the disclosed systems can improve or refine structural variant calls (e.g., between 50-200 base pairs in length) determined by a call generation model by training and utilizing the structural variant refinement machine-learning model. As disclosed, the systems can determine sequencing metrics and can customize training data for a structural variant refinement machine-learning model to generate modified structural variant calls.

Abstract: Systems, methods, and apparatus are described herein for performing sequencing of one or more biological samples in at least two flow cells on a sequencing device. A sequencing system may comprise one or more of a scheduling engine, the sequencing device, and a display. The scheduling engine may maintain scheduling information of a state of compute resources and non-compute resources. The sequencing device may receive the scheduling information from the scheduling engine; determine the state of the compute resources and non-compute resources; determine a sequencing analysis priority associated with performing analysis of the at least two flow cells on the sequencing device; and perform the sequencing task related to the one or more biological samples in the at least two flow cells according to the sequencing analysis priority. The display may display real-time feedback associated with completion of the sequencing task for each flow cell.

Abstract: The technology disclosed corrects inter-cluster intensity profile variation for improved base calling on a cluster-by-cluster basis. The technology disclosed accesses current intensity data and historic intensity data of a target cluster, where the current intensity data is for a current sequencing cycle and the historic intensity data is for one or more preceding sequencing cycles. A first accumulated intensity correction parameter is determined by accumulating distribution intensities measured for the target cluster at the current and preceding sequencing cycles. A second accumulated intensity correction parameter is determined by accumulating intensity errors measured for the target cluster at the current and preceding sequencing cycles. Based on the first and second accumulated intensity correction parameters, next intensity data for a next sequencing cycle is corrected to generate corrected next intensity data, which is used to base call the target cluster at the next sequencing cycle.

Abstract: The technology disclosed relates to equalizer-based intensity correction for base calling. In particular, the technology disclosed relates to accessing an image whose pixels depict intensity emissions from a target cluster and intensity emissions from additional adjacent clusters, selecting a lookup table that contains pixel coefficients that are configured to increase a signal-to-noise ratio, applying the pixel coefficients to intensity values of the pixels in the image to produce an output, and base calling the target cluster based on the output.

Abstract: This disclosure describes methods, non-transitory computer readable media, and systems that can configure a field programmable gate array (FPGA) or other configurable processor to implement a neural network and train the neural network using the configurable processor by modifying certain network parameters of a subset of the neural network’s layers. For instance, the disclosed systems can configure a configurable processor on a computing device to implement a base-calling-neural network (or other neural network) that includes different sets of layers. Based on a set of images of oligonucleotide clusters or other datasets, the neural network generates predicted classes, such as by generating nucleobase calls for oligonucleotide clusters. Based on the predicted classes, the disclosed systems subsequently modify certain network parameters for a subset of the neural network’s layers, such by modifying parameters for a set of top layers.

Abstract: This disclosures describes embodiments of methods, systems, and non-transitory computer readable media that accurately and efficiently estimate the effects of phasing and pre-phasing for a particular cluster of oligonucleotides and determining a cluster-specific-phasing correction for the cluster. For instance, the disclosed systems can dynamically identify clusters of oligonucleotides exhibiting error-inducing sequences that frequently cause phasing or pre-phasing. When the disclosed systems detect signals during cycles at read positions following such an error-inducing sequence, the disclosed systems can generate cluster-specific-phasing coefficients and correct the signals according to such cluster-specific-phasing coefficients. For instance, the disclosed system can utilize a linear equalizer, decision feedback equalizer, or a maximum likelihood sequence estimator to generate cluster-specific-phasing coefficients.

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: The technology disclosed relates to state-based base calling. In particular, the technology disclosed relates to incorporating state information about data from previous sequencing cycles into the analysis of data from a current sequencing cycle when generating a base call for the current sequencing cycle. For example, when generating a base call for an Nth sequencing cycle, the technology disclosed can incorporate into the base calling logic state information about data from sequencing cycles 1 to N?1.

Abstract: The technology disclosed corrects inter-cluster intensity profile variation for improved base calling on a cluster-by-cluster basis. The technology disclosed accesses current intensity data and historic intensity data of a target cluster, where the current intensity data is for a current sequencing cycle and the historic intensity data is for one or more preceding sequencing cycles. A first accumulated intensity correction parameter is determined by accumulating distribution intensities measured for the target cluster at the current and preceding sequencing cycles. A second accumulated intensity correction parameter is determined by accumulating intensity errors measured for the target cluster at the current and preceding sequencing cycles. Based on the first and second accumulated intensity correction parameters, next intensity data for a next sequencing cycle is corrected to generate corrected next intensity data, which is used to base call the target cluster at the next sequencing cycle.

Abstract: The technology disclosed relates to equalizer-based intensity correction for base calling. In particular, the technology disclosed relates to accessing an image whose pixels depict intensity emissions from a target cluster and intensity emissions from additional adjacent clusters, selecting a lookup table that contains pixel coefficients that are configured to increase a signal-to-noise ratio, applying the pixel coefficients to intensity values of the pixels in the image to produce an output, and base calling the target cluster based on the output.

Abstract: This disclosure describes methods, non-transitory computer readable media, and systems that can utilize a machine learning model to recalibrate nucleotide base calls (e.g., variant calls) of a call generation model. For instance, the disclosed systems can train and utilize a call recalibration machine learning model to generate a set of predicted variant call classifications based on sequencing metrics associated with a sample nucleotide sequence. Leveraging the set of variant call classifications, the disclosed systems can further update or modify nucleotide base calls (e.g., variant calls) corresponding to genomic coordinates, such as multiallelic genomic coordinates, haploid genomic coordinates, and genomic coordinates indicated (by the call generation model) to exhibit homozygous reference genotypes.

Abstract: The technology disclosed includes a system. The system includes a spatial convolutional neural network configured to process sequencing images of clusters, and produce spatially convolved features, a filtering logic configured to select, from the spatially convolved features, a subset of spatially convolved features that contain centers of the clusters, a compression logic configured to compress the subset of spatially convolved features into a set of compressed features, a contextualization logic configured to access state information for compressed features in the set of compressed features, a temporal convolutional neural network configured to process the set of stateful compressed features, and produce temporally convolved stateful features, and a base calling logic configured to generate base calls for the clusters based on the temporally convolved stateful features.

Abstract: The technology disclosed corrects inter-cluster intensity profile variation for improved base calling on a cluster-by-cluster basis. The technology disclosed accesses current intensity data and historic intensity data of a target cluster, where the current intensity data is for a current sequencing cycle and the historic intensity data is for one or more preceding sequencing cycles. A first accumulated intensity correction parameter is determined by accumulating distribution intensities measured for the target cluster at the current and preceding sequencing cycles. A second accumulated intensity correction parameter is determined by accumulating intensity errors measured for the target cluster at the current and preceding sequencing cycles. Based on the first and second accumulated intensity correction parameters, next intensity data for a next sequencing cycle is corrected to generate corrected next intensity data, which is used to base call the target cluster at the next sequencing cycle.

Abstract: A method of base calling using at least two base callers is disclosed. The method includes executing at least a first base caller and a second base caller on sensor data generated for sensing cycles in a series of sensing cycles; generating, by the first base caller, first classification information associated with the sensor data, based on executing the first base caller on the sensor data; and generating, by the second base caller, second classification information associated with the sensor data, based on executing the second base caller on the sensor data. In an example, based on the first classification information and the second classification information, a final classification information is generated, where the final classification information includes one or more base calls for the sensor data.

Abstract: This disclosure describes methods, non-transitory computer readable media, and systems that can utilize a machine learning model to recalibrate nucleotide-base calls (e.g., variant calls) of a call-generation model. For instance, the disclosed systems can train and utilize a call-recalibration-machine-learning model to generate a set of predicted variant-call classifications based on sequencing metrics associated with a sample nucleotide sequence. Leveraging the set of variant-call classifications, the disclosed systems can further update or modify nucleotide-base calls (e.g., variant calls) corresponding to genomic coordinates.

Abstract: We disclose a system. The system comprises a memory and a runtime logic. The memory stores a plurality of specialist signal profilers. Each specialist signal profiler in the plurality of specialist signal profilers is trained to maximize signal-to-noise ratio of sequenced signals in a particular signal profile detected for analytes in a particular analyte class and characterized in a particular training data set. The runtime logic, having access to the memory, is configured to execute a base calling operation by applying respective specialist signal profilers in the plurality of specialist signal profilers to sequenced signals in respective signal profiles detected for analytes in respective analyte classes during the base calling operation.

Abstract: The technology disclosed extracts intensities from sequencing images for base calling target clusters and attenuates spatial crosstalk from neighboring clusters. The technology disclosed accesses a particular section from a plurality of sections of an image output by a sensor, the particular section of the image including at least one pixel depicting intensity emission values from a target cluster and neighboring clusters located across the sensor, and convolves the particular section of the image with a corresponding convolution kernel in a plurality of convolution kernels, to generate a feature map comprising a plurality of feature values. The technology disclosed further assigns a corresponding feature value to the target cluster based on feature values in the plurality of feature values adjoining a center of the target cluster, and processes the corresponding feature value assigned to the target cluster, to base call the target cluster.