Short k-mer abundance profiles yield robust machine learning features and accurate classifiers for RNA viruses.

ABSTRACT: High-throughput sequencing technologies have greatly enabled the study of genomics, transcriptomics and metagenomics. Automated annotation and classification of the vast amounts of generated sequence data has become paramount for facilitating biological sciences. Genomes of viruses can be radically different from all life, both in terms of molecular structure and primary sequence. Alignment-based and profile-based searches are commonly employed for characterization of assembled viral contigs from high-throughput sequencing data. Recent attempts have highlighted the use of machine learning models for the task, but these models rely entirely on DNA genomes and owing to the intrinsic genomic complexity of viruses, RNA viruses have gone completely overlooked. Here, we present a novel short k-m

SUBMITTER: Alam MNU 

PROVIDER: S-EPMC7500682 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature