Abstract: Devices, systems, and methods for non-volatile storage include a well activation device operable to modify one or more wells from a plurality of wells of a flow cell to provide a set of readable wells. Readable wells are configured to allow exposure of a well to substances from nucleotide sequencing fluids, and prevent exposure to other substances and fluids, such as nucleotide synthesizing fluids. The well activation device may also modify wells to provide a set of writeable wells. This set of wells is configured to allow exposure to the nucleotide synthesizing fluids and substances; and prevent exposure to the nucleotide sequencing fluids and substances. There may also be provisions made for risk mitigation for data errors such as generating commands to write specified data to a nucleotide sequence associated with a particular location in a storage device, reading the nucleotide sequence and performing a comparison.

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: 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: A neural network processes sequencing images on a patch-by-patch basis for base calling. The sequencing images depict intensity emissions of a set of analytes. The patches depict the intensity emissions for a subset of the analytes and have undiverse intensity patterns due to limited base diversity. The neural network has convolution filters that have receptive fields confined to the patches. The convolution filters detect intensity patterns in the patches with losses in detection due to the undiverse intensity patterns and confined receptive fields. An intensity contextualization unit determines intensity context data based on intensity values in the images. The data flow logic appends the intensity context data to the sequencing images to generate intensity contextualized images. The neural network applies the convolution filters on the intensity contextualized images and generates base call classifications.

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: Embodiments provided herein relate to methods and compositions for preparing an immobilized library of barcoded DNA fragments of a target nucleic acid, identifying genomic variants, determining the contiguity information, phasing information, and methylation status of the target nucleic acid.

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.

Abstract: The technology disclosed uses neural networks to determine analyte metadata by (i) processing input image data derived from a sequence of image sets through a neural network and generating an alternative representation of the input image data, the input image data has an array of units that depicts analytes and their surrounding background, (ii) processing the alternative representation through an output layer and generating an output value for each unit in the array, (iii) thresholding output values of the units and classifying a first subset of the units as background units depicting the surrounding background, and (iv) locating peaks in the output values of the units and classifying a second subset of the units as center units containing centers of the analytes.

Abstract: The technology disclosed relates to artificial intelligence-based base calling of index sequences. The technology disclosed accesses index images generated for the index sequences during index sequencing cycles of a sequencing run. The index images depict intensity emissions generated as a result of nucleotide incorporation in the index sequences during the sequencing run. The technology disclosed normalizes an index image from a current index sequencing cycle based on (i) intensity values of index images from one or more preceding index sequencing cycles, (ii) intensity values of index images from one or more succeeding index sequencing cycles, and (iii) intensity values of index images from the current index sequencing cycle. The technology disclosed processes normalized versions of the index images through a neural network-based base caller and generates a base call for each of the index sequencing cycles, thereby producing index reads for the index sequences.

Abstract: The technology disclosed relates to a system that comprises a spatial convolution network and a bus network. The spatial convolution network is configured to process a window of per-cycle sequencing image sets on a cycle-by-cycle basis by separately processing respective per-cycle sequencing image sets through respective spatial processing pipelines to generate respective per-cycle spatial feature map sets for respective sequencing cycles. The bus network is configured to form buses between spatial convolution layers within the respective spatial processing pipelines. The buses are configured to cause respective per-cycle spatial feature map sets generated by two or more spatial convolution layers in a particular sequence of spatial convolution layer for a particular sequencing cycle to combine into a combined per-cycle spatial feature map set, and provide the combined per-cycle spatial feature map set as input to another spatial convolution layer in the particular sequence of spatial convolution layer.

Abstract: The technology disclosed compresses a larger, teacher base caller into a smaller, student base caller. The student base caller has fewer processing modules and parameters than the teacher base caller. The teacher base caller is trained using hard labels (e.g., one-hot encodings). The trained teacher base caller is used to generate soft labels as output probabilities during the inference phase. The soft labels are used to train the student base caller.

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: The technology disclosed relates to an artificial intelligence-based method of base calling. In particular, it relates to processing, through a spatial network of a neural network-based base caller, a first window of per-cycle analyte channel sets in for a first window of sequencing cycles of a sequencing run, and generating respective sequences of spatial output sets for respective sequencing cycles in the first window of sequencing cycles, processing, through a compression network of the neural network-based base caller, respective final spatial output sets in the respective sequences of spatial output sets, and generating respective compressed spatial output sets for the respective sequencing cycles in the first window of sequencing cycles, and generating, based on the respective compressed spatial output sets, base call predictions for one or more sequencing cycles in the first window of sequencing cycles.

Abstract: The technology disclosed relates to a system that comprises a spatial convolution network and a temporal convolution network. The spatial convolution network is configured to process a window of per-cycle sequencing image sets and generate respective per-cycle spatial feature map sets. Trained coefficients of spatial convolution filters in spatial convolution filter banks of respective sequences of spatial convolution filter banks vary between sequences of spatial convolution layers in respective sequences of spatial convolution layers. The temporal convolution network is configured to process the per-cycle spatial feature map sets on a groupwise basis and generate respective per-group temporal feature map sets. Trained coefficients of temporal convolution filters in respective temporal convolution filter banks vary between temporal convolution filter banks in respective temporal convolution filter banks.

Abstract: A method includes grafting oligonucleotides to a flow cell and preparing a library of polynucleotides. Each polynucleotide has been written to contain retrievable information and includes a region complementary to one of the sequencing initiation primers grafted to the flow cell. Each polynucleotide is indexed to permit discrete identification of that polynucleotide and the information it contains over other polynucleotides in the library. Another method includes writing two polynucleotides including two sequences with reverse complementary joining sequences onto a flow cell. One of the polynucleotides is extended to generate a third polynucleotide comprising a sequence that is the combination of the first and second sequences. A fourth polynucleotide is written with a third joining sequence of a fourth sequence.

Abstract: Presented herein are methods and compositions for tagmentation of nucleic acids. The methods are useful for generating tagged DNA fragments that are qualitatively and quantitatively representative of the target nucleic acids in the sample from which they are generated.