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: Systems, methods, and apparatuses are described herein. For instance, a detection apparatus may comprise memory and at least one processor. The detection apparatus may be configured to obtain an image comprising at least one feature and a plurality of fiducials. The plurality of fiducials may be arranged in a pattern. The detection apparatus may be configured to determine a plurality of sub-regions of the image. Each sub-region comprises a subset of the fiducials comprised in the image. The detection apparatus may be configured to perform a geometric transform on each sub-region to generate a respective local transform associated with each sub-region. The detection apparatus may be configured to register respective locations of the fiducials comprised in the image based on the respective local transform associated with each sub-region. A size of each sub-region may be selected such that each sub-region is substantially invariant to stage jitter.

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 is directed to cluster segmentation and base calling. The technology disclosed describes a computer-implemented method including segmenting a population of clusters into a plurality of subpopulations of clusters based on one or more prior bases called at one or more prior sequencing cycles of a sequencing run. At a current sequencing cycle of the sequencing run, the method includes applying a mixture of four distributions to current sequenced data of each subpopulation of clusters in the plurality of subpopulations of clusters, the four distributions corresponding to four bases adenine (A), cytosine (C), guanine (G), and thymine (T), and the current sequenced data being generated at the current sequencing cycle. The method further includes base calling clusters in a particular subpopulation of clusters using a corresponding mixture of four distributions.

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: Presented herein are techniques for indexing of nucleic acid, e.g., for use in conjunction with sequencing. The techniques include generating indexed nucleic acid fragments from an individual sample, whereby the index sequence incorporated into each index site of the nucleic acid fragment is selected from a plurality of distinguishable of index sequences and such that the population of generated nucleic acid fragments represents each index sequence from the plurality. In this manner, the generated indexed nucleic acid fragments from a single sample are indexed with a diverse mix of index sequences that reduce misassignment due to index read errors associated with low sequence diversity.

Abstract: Fiducial markers are provided on patterned arrays of the type that may be used for molecular analysis, such as sequencing. The fiducials may have configurations that enhance their detection in image or detection data, that facilitate or improve processing, that provide encoding of useful information, and so forth. Examples of the fiducials may include an “always on” type that respond to multiple frequencies of radiation used during processing and detection so as to return signals during successive cycles of imaging.

Abstract: Presented herein are techniques for indexing of nucleic acid, e.g., for use in conjunction with sequencing. The techniques include generating indexed nucleic acid fragments from an individual sample, whereby the index sequence incorporated into each index site of the nucleic acid fragment is selected from a plurality of distinguishable of index sequences and such that the population of generated nucleic acid fragments represents each index sequence from the plurality. In this manner, the generated indexed nucleic acid fragments from a single sample are indexed with a diverse mix of index sequences that reduce misassignment due to index read errors associated with low sequence diversity.

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: This disclosure describes methods, non-transitory computer readable media, and systems that can introduce short calibration sequences into a sequencing device and run calibration cycles to adjust or otherwise determine a sequencing parameter corresponding to the sequencing device. For instance, the disclosed systems can detect a flow cell (or other sample-nucleotide slide) with calibration sequences incorporated into samples' library fragments or into a surface of the sample-nucleotide slide. By running one or more calibration cycles to incorporate nucleobases on oligonucleotides corresponding to calibration sequences and capture corresponding images for calibration sequences—separate from genomic sequencing cycles for sample genomic sequences—the disclosed systems can determine a sequencing parameter corresponding to the sequencing device.

Abstract: Fiducial markers are provided on patterned arrays of the type that may be used for molecular analysis, such as sequencing. The fiducials may have configurations and layouts that enhance their detection in image or detection data, that facilitate or improve processing, that provide encoding of useful information, and so forth. Examples of the fiducials may include offset layouts that may be useful in detecting the fiducials in different types and approaches in imaging, and that may help to distinguish regions of the array from one another in image data.

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 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: 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: 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 generating base calls by a base caller is disclosed. The method includes receiving a plurality of sensor data from a flow cell, wherein the plurality of sensor data is within a first range and identifying a second range, such that at least a threshold percentage of the plurality of sensor data are within the second range. At least a subset of the plurality of sensor data, that are within the second range, are mapped to a third range, thereby generating a plurality of normalized sensor data. The plurality of normalized sensor data is processed in a base caller, to call, for the plurality of normalized sensor data, one or more corresponding bases.

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: 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.