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. 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 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: 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 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 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 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 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 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 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.