ViraMiner: Deep learning on raw DNA sequences for identifying viral genomes in human samples.

ABSTRACT: Despite its clinical importance, detection of highly divergent or yet unknown viruses is a major challenge. When human samples are sequenced, conventional alignments classify many assembled contigs as "unknown" since many of the sequences are not similar to known genomes. In this work, we developed ViraMiner, a deep learning-based method to identify viruses in various human biospecimens. ViraMiner contains two branches of Convolutional Neural Networks designed to detect both patterns and pattern-frequencies on raw metagenomics contigs. The training dataset included sequences obtained from 19 metagenomic experiments which were analyzed and labeled by BLAST. The model achieves significantly improved accuracy compared to other machine learning methods for viral genome classification. Using 30

SUBMITTER: Tampuu A 

PROVIDER: S-EPMC6738585 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature