engleski

Scaffolding Assembled Genomes with Long Reads

Development of tools for genome assembling with enough precision to be useful in practice is still an open problem. Third generation sequencers allow genome assembly with smaller fragmentation and high accuracy. In this work, we try to improve the final accuracy of the assembled genome using deep learning techniques. Trained convolutional deep network polishes errors in the assembled genome by learning the correct bases, insertions and deletions patterns. Networks are prepared to be used with Pacific Biosciences and Oxford Nanopore data. Current results show that sometimes models slightly improve, sometimes slightly degrade consensus quality, but there is still a room for progress since there are a lot of untested ideas which are described in the end of the thesis.

bioinformatics, consensus, genom assembly, deep learning

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