HaploDMF: viral haplotype reconstruction from long reads via deep matrix factorization.

ABSTRACT:

Motivation

Lacking strict proofreading mechanisms, many RNA viruses can generate progeny with slightly changed genomes. Being able to characterize highly similar genomes (i.e. haplotypes) in one virus population helps study the viruses' evolution and their interactions with the host/other microbes. High-throughput sequencing data has become the major source for characterizing viral populations. However, the inherent limitation on read length by next-generation sequencing makes complete haplotype reconstruction difficult.

Results

In this work, we present a new tool named HaploDMF that can construct complete haplotypes using third-generation sequencing (TGS) data. HaploDMF utilizes a deep matrix factorization model with an adapted loss function to learn latent features from al

SUBMITTER: Cai D 

PROVIDER: S-EPMC9750122 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature