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Updated: Sep 2, 2025
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Isolation and Genome Analysis of Single Virions using 'Single Virus Genomics'
Published on: May 26, 2013
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Identification of bacteriophage genome sequences with representation learning
Zeheng Bai1, Yao-Zhong Zhang1, Satoru Miyano1,2
1Division of Health Medical Intelligence, Human Genome Center, The Institute of Medical Science, The University of Tokyo, Minato-ku, Tokyo 108-8639, Japan.
Bioinformatics (Oxford, England)
|August 3, 2022
Summary
Identifying bacteriophages (phages) in metagenomic data is crucial for understanding microbial communities. The INHERIT deep learning model integrates database and alignment-free methods, achieving high accuracy in phage identification.
Area of Science:
- Microbiology
- Bioinformatics
- Computational Biology
Background:
- Bacteriophages (phages) are viruses infecting bacteria and archaea, abundant in the human microbiome.
- Accurate phage identification from metagenomic data is essential for studying phage-microbe interactions.
- Current methods include database-based (alignment) and alignment-free approaches, each with limitations.
Purpose of the Study:
- To develop a novel deep learning model for improved phage identification from metagenomic sequences.
- To integrate the strengths of both database-based and alignment-free methods for enhanced accuracy.
- To provide an open-source tool for the research community.
Main Methods:
- Proposed INHERIT, a deep representation learning model.
- Utilized pre-training for knowledge representation from existing databases.
- Employed a BERT-style deep learning framework, combining alignment-free advantages with pre-training.
- Compared INHERIT against four existing methods on a benchmark dataset.
Main Results:
- INHERIT demonstrated superior performance, achieving an F1-score of 0.9932.
- The model effectively integrates database-based and alignment-free strategies.
- Pre-training on specific species improved the accuracy of the non-alignment deep learning model.
Conclusions:
- INHERIT offers a highly accurate and effective approach for phage identification in metagenomic data.
- The integration of pre-training and BERT-style frameworks advances alignment-free methods.
- The tool is publicly available, facilitating further research in phage biology and microbiome studies.
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