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: 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: Systems and methods of identifying nucleobases in a template polynucleotide are disclosed. In one embodiment, such a method may include providing a substrate comprising a plurality of double stranded template polynucleotides in a cluster. Each double stranded template polynucleotide may comprise a first strand and a second strand. The method may further include contacting the plurality of double stranded template polynucleotides with first primers which bind to the first strand and second primers which bind to the second strand. The method may further include extending the first primers and the second primers by contacting the cluster with labeled nucleobases to form first labeled primers and second labeled primers. The method may further include stimulating light emissions from the first and second labeled primers, wherein an amplitude of the signal generated by the first labeled primers is greater than an amplitude of the signal generated by the second labeled primers.

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: 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: 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: A system for analysis of base call sensor output has memory accessible by the runtime program storing tile data including sensor data for a tile from sensing cycles of a base calling operation. A neural network processor having access to the memory is configured to execute runs of a neural network using trained parameters to produce classification data for sensing cycles. A run of the neural network operates on a sequence of N arrays of tile data from respective sensing cycles of N sensing cycles, including a subject cycle, to produce the classification data for the subject cycle. Data flow logic moves tile data and the trained parameters from the memory to the neural network processor for runs of the neural network using input units including data for spatially aligned patches of the N arrays from respective sensing cycles of N sensing cycles.

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 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 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 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.