PharaCon: a new framework for identifying bacteriophages via conditional representation learning.

Journal: Bioinformatics (Oxford, England)
PMID: 39992229

Abstract

MOTIVATION: Identifying bacteriophages (phages) within metagenomic sequences is essential for understanding microbial community dynamics. Transformer-based foundation models have been successfully employed to address various biological challenges. However, these models are typically pre-trained with self-supervised tasks that do not consider label variance in the pre-training data. This presents a challenge for phage identification as pre-training on mixed bacterial and phage data may lead to information bias due to the imbalance between bacterial and phage samples.

Authors

  • Zeheng Bai
    Division of Health Medical Intelligence, Human Genome Center, The Institute of Medical Science, The University of Tokyo, 4-6-1, Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan.
  • Yao-Zhong Zhang
    The Institute of Medical Science, The University of Tokyo, Shirokanedai 4-6-1, Minato-ku, Tokyo, 108-8639, Japan.
  • Yuxuan Pang
    Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, PR China, and also in the School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, PR China.
  • Seiya Imoto
    The Institute of Medical Science, The University of Tokyo, Shirokanedai 4-6-1, Minato-ku, Tokyo, 108-8639, Japan.

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