Bacteriophage classification for assembled contigs using graph convolutional network.

ABSTRACT:

Motivation

Bacteriophages (aka phages), which mainly infect bacteria, play key roles in the biology of microbes. As the most abundant biological entities on the planet, the number of discovered phages is only the tip of the iceberg. Recently, many new phages have been revealed using high-throughput sequencing, particularly metagenomic sequencing. Compared to the fast accumulation of phage-like sequences, there is a serious lag in taxonomic classification of phages. High diversity, abundance and limited known phages pose great challenges for taxonomic analysis. In particular, alignment-based tools have difficulty in classifying fast accumulating contigs assembled from metagenomic data.

Results

In this work, we present a novel semi-supervised learning model, named PhaGCN, to c

SUBMITTER: Shang J 

PROVIDER: S-EPMC8275337 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature