Deep Basecaller for Sanger Sequencing

    公开(公告)号:US20220013193A1

    公开(公告)日:2022-01-13

    申请号:US17312168

    申请日:2019-12-10

    Abstract: A deep basecaller system for Sanger sequencing and associated methods are provided. The methods use deep machine learning. A Deep Learning Model is used to determine scan labelling probabilities based on an analyzed trace. A Neural Network is trained to learn the optimal mapping function to minimize a Connectionist Temporal Classification (CTC) Loss function. The CTC function is used to calculate loss by matching a target sequence and predicted scan labelling probabilities. A Decoder generates a sequence with the maximum probability. A Basecall position finder using prefix beam search is used to walk through CTC labelling probabilities to find a scan range and then the scan a position of peak labelling probability within the scan range for each called base. Quality Value (QV) is determined using a feature vector calculated from CTC labelling probabilities as an index into a QV look-up table to find a quality score.

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