ArticleBioinformatics (Oxford, England)2021
Bacteriophage classification for assembled contigs using graph convolutional network.
Article in Bioinformatics (Oxford, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 90 papers, 1 of them a synthesis that pooled it.
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Who cites it
90 citing papers in PubMed, 1 synthesis or guideline pooled it, 148 citations in OpenAlex.
- Bioinformatics Tools and Approaches for Virus Discovery in Genomic Data: A Systematic Review.Viruses · 2025Pooled it
- Innovative Applications of Artificial Intelligence in Bacteriophage Research: A New Chapter in Future Medicine.Microorganisms · 2026Review
- Dental wastewater reveals a hidden reservoir of oral bacteriophage diversity.Microbiology spectrum · 2026Article
- Phage bioinformatics tools: a review of computational approaches for bacteriophage research.Briefings in bioinformatics · 2026Review
- Article
- Significant variability in virulence factors and antibiotic resistance of uropathogenic Escherichia coli strains may be counteracted by their susceptibility to lytic bacteriophages.Scientific reports · 2026Article
- PhaBOX2: an enhanced web server for discovering and analyzing viral contigs in metagenomic data.Nucleic acids research · 2026Article
- Evolving strategies for virus discovery.Microbial genomics · 2026Review
- Human Gut Phageome Analysis Uncovers Thousands of Highly Modular Endolysins.MicrobiologyOpen · 2026Article
- Isolation and characterization of broad-rangeMicrobiology (Reading, England) · 2026Article
- Annual Dynamics and Functional Traits of Viral Communities in Tropical Intertidal Sands of Sanya Bay.Viruses · 2026Article
- Genome Characterization of Temperate Bacteriophages and Associated Genetic Features in Avian PathogenicAnimals : an open access journal from MDPI · 2026Article
- PhageCGRNet: Integrating Chaos Game Representation of Genomes with Convolutional Neural Network for accurate phage host classification prediction.PLoS computational biology · 2026Article
- Tools and approaches to study the human gut virome: from the bench to bioinformatics.mSystems · 2026Review
- Impact of phage enrichment on the observed infant gut phageome.Microbiology spectrum · 2026Article
- PhaGCN_Cluster: A Scalable and Robust Framework for Automated Classification and Discovery of Viral Dark Matter from Metagenomes.Interdisciplinary sciences, computational life sciences · 2026Article
- In silico exploration of the genomic repertoire of Iranian aquatic bacteria: Prophage carriage, bioactive compound potential, CRISPR-Cas immunity, and integrated defensive-metabolic islands.Biochemistry and biophysics reports · 2026Article
- A signature-protein-based approach for accurate and efficient profiling of the human gut virome.Cell reports methods · 2026Article
- Genomic landscape of biosynthetic gene clusters in Iranian extremophiles reveals prolific metabolite potential, prophage associations, and integrated defensive-metabolic islands.BMC microbiology · 2026Article
- HYMET: a hybrid metagenomic pipeline for accurate and efficient taxonomic classification.GigaScience · 2026Article
30 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
Abstract
motivationBacteriophages (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.
resultsIn 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.