Bacteriophage classification for assembled contigs using graph convolutional network.
Bioinformatics
; 37(Suppl_1): i25-i33, 2021 07 12.
Article
en En
| MEDLINE
| ID: mdl-34252923
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 conduct taxonomic classification for phage contigs. In this learning model, we construct a knowledge graph by combining the DNA sequence features learned by convolutional neural network and protein sequence similarity gained from gene-sharing network. Then we apply graph convolutional network to utilize both the labeled and unlabeled samples in training to enhance the learning ability. We tested PhaGCN on both simulated and real sequencing data. The results clearly show that our method competes favorably against available phage classification tools. AVAILABILITY AND IMPLEMENTATION The source code of PhaGCN is available via https//github.com/KennthShang/PhaGCN.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Bacteriófagos
Idioma:
En
Revista:
Bioinformatics
Asunto de la revista:
INFORMATICA MEDICA
Año:
2021
Tipo del documento:
Article
País de afiliación:
China