Bacteriophage classification for assembled contigs using graph convolutional network
| Autor: | Yanni Sun, Jiayu Shang, Jingzhe Jiang |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Statistics and Probability Computer Science - Machine Learning Source code AcademicSubjects/SCI01060 Computer science media_common.quotation_subject Computational biology Biochemistry Convolutional neural network DNA sequencing Machine Learning (cs.LG) Bacteriophage 03 medical and health sciences Protein sequencing Quantitative Biology - Genomics Bioinformatics of Microbes and Microbiomes Bacteriophages Molecular Biology 030304 developmental biology media_common Genomics (q-bio.GN) 0303 health sciences biology Contig 030302 biochemistry & molecular biology High-Throughput Nucleotide Sequencing biology.organism_classification Computer Science Applications Computational Mathematics Computational Theory and Mathematics Metagenomics FOS: Biological sciences Graph (abstract data type) Metagenome Software |
| Zdroj: | Bioinformatics |
| ISSN: | 1367-4811 1367-4803 |
| Popis: | 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 (CNN) and protein sequence similarity gained from gene-sharing network. Then we apply graph convolutional network (GCN) 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. Comment: 15 pages, 10 figures |
| Databáze: | OpenAIRE |
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