engleski

Deep Learning Model for Base Calling of MinION Nanopore Reads

MinION by Oxford Nanopore Technologie is affordable and portable sequencing device suitable for various applications. The device produces very long reads, however, it suffers from high sequencing error rate. The goal of this thesis is to show that the reported accuracy of the sequencing data is not only limited by sequencing technology, but also by the current software tools used for base calling and can be further improved by using different deep learning concepts. Approach for base calling of raw data using convolutional neural networks is proposed as an alternative to recurrent neural networks used by other basecallers offering improvements both in speed and accuracy. A detailed comparison of the developed tool with the existing tools for base calling R9 data is given.

base calling, Oxford Nanopore Technologies, MinION, deep learning, seq2seq, convolutional neural network, residual network, CTC loss

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