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vClean: assessing virus sequence contamination in viral genomes
Ryota Wagatsuma1,2, Yohei Nishikawa2,3, Masahito Hosokawa1,2,3,4
1Department of Life Science and Medical Bioscience, Graduate School of Advanced Science and Engineering, Waseda University, 2-2 Wakamatsu-cho, Shinjuku-ku, Tokyo 162-8480, Japan.
NAR Genomics and Bioinformatics
|January 9, 2025
Summary
A new tool, vClean, automatically detects sequence contamination in environmental viral genomes. This automated detection is crucial for accurate analysis of viral metagenomics and single-virus genomics data.
Area of Science:
- Environmental Virology
- Bioinformatics
- Genomics
Background:
- Viral metagenomics and single-virus genomics enable environmental virus genome recovery.
- Current methods risk introducing sequence contamination into assembled viral genomes.
- Lack of practical tools hinders detection of these contaminations, impacting analysis accuracy.
Purpose of the Study:
- To introduce vClean, a novel automated tool for detecting sequence contamination in viral genomes.
- To establish a reliable method for quality control in environmental viral genome analysis.
Main Methods:
- vClean utilizes machine learning based on nucleotide sequence features and gene patterns.
- For tailed double-stranded DNA phages, predictions are refined by analyzing single-copy-like gene duplications.
- Performance was validated using simulated datasets and real-world metagenomic data.
Main Results:
- vClean achieved a binary accuracy of 0.932 on simulated datasets.
- Analysis of 4693 public ocean metagenomic viral genomes revealed 1604 (34.2%) contaminated genomes.
- Contamination was also detected in single-virus genome data from river water samples.
Conclusions:
- vClean effectively identifies sequence contamination in environmental viral genomes.
- The tool provides a new benchmark for viral genome quality control.
- vClean is poised to become an essential tool for environmental viral genomics research.
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