Abstract: This disclosure describes methods, non-transitory computer readable media, and systems that can determine allele likelihoods of a genomic region exhibiting certain haplotype alleles using one or both of consolidated computations and data exchanges across specialized hardware. For instance, the disclosed systems can determine an intermediate allele likelihood of a genomic region comprising a haplotype allele by running a single-pass-concurrent-multiplication operation. In some cases, the disclosed systems determine and store subsets of intermediate allele likelihoods corresponding to marker-variant groups and extemporaneously generate sets of intermediate allele likelihoods for a set of marker variants by using the intermediate-allele-likelihood subsets as hot-start points.

Abstract: A system for base calling includes memory storing a topology of a neural network, a plurality of weights sets, and sensor data for a series of sensing cycles. Sequencing events span temporal progression of the base calling operation through subseries of sensing cycles, and spatial progression of the base calling operation through locations on a biosensor. A configurable processor is configured to load the topology on the configurable processor, select a weight set in dependence upon a subject subseries of sensing cycles and/or a subject location on the biosensor, load subject sensor data for the subject subseries of sensing cycles and the subject location on the processing elements, configure the topology using the selected weight set, and cause the neural network to process the subject sensor data to produce base call classification data for the subject subseries and the subject location.

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: A system and method includes employing a cyclical redundancy check (CRC) code to information being transmitted on the channel, the CRC code including appending a single CRC code bit to the information, the single CRC code bit having a value of 0. The method further includes adding an error correcting code with a value of 0 to the information and to a plurality of error correcting code parity bits provided by the error correcting code. The method further includes transmitting, by the communication device, the information on the channel without the single CRC code bit, the additional information bit, and the error correcting code parity bits for being decoded and set by a receiver.

Abstract: A system and method for maximizing channel bandwidth while employing error control coding is presented. The method includes employing a cyclical redundancy check (CRC) code to information being transmitted on the channel, the CRC code including appending a single CRC code bit to the information, the single CRC code bit having a value of 0. The method further includes adding an error correcting code with a value of 0 to the information and to a plurality of error correcting code parity bits provided by the error correcting code. The method further includes transmitting, by the communication device, the information on the channel without the single CRC code bit, the additional information bit, and the error correcting code parity bits for being decoded and set by a receiver.