Naslov
Scaffolding Assembled Genomes with Long Reads

Autori
Jurić, Antonio

Vrsta, podvrsta i kategorija rada
Ocjenski radovi, diplomski rad, diplomski

Fakultet
Fakultet elektrotehnike i računarstva

Mjesto
Zagreb

Datum
20.09

Godina
2018

Stranica
49

Mentor
Šikić, Mile

Neposredni voditelj
49

Ključne riječi
bioinformatics, consensus, genom assembly, deep learning

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo

POVEZANOST RADA