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CLMB: deep contrastive learning for robust metagenomic binning by Zhang, Pengfei; Jiang, Zhengyuan; Wang, Yixuan; Li, Yu is a scholarly article available to read on EtoBox.

What is CLMB: deep contrastive learning for robust metagenomic binning about?

The reconstruction of microbial genomes from large metagenomic datasets is a critical procedure for finding uncultivated microbial populations and defining their microbial functional roles. To achieve that, we need to perform metagenomic binning, clustering the assembled contigs into draft genomes. Despite the existing computational tools, most of them neglect one important property of the metagenomic data, that is, the noise. To further improve the metagenomic binning step and reconstruct better metagenomes, we propose a deep Contrastive Learning framework for Metagenome Binning (CLMB), which can efficiently eliminate the disturbance of noise and produce more stable and robust results. Essentially, instead of denoising the data explicitly, we add simulated noise to the training data and force the deep learning model to produce similar and stable representations for both the noise-free data and the distorted data. Consequently, the trained model will be robust to noise and handle it implicitly during usage. CLMB outperforms the previous state-of-the-art binning methods significantly, recovering the most near-complete genomes on almost all the benchmarking datasets (up to 17\% more

Author

Zhang, Pengfei; Jiang, Zhengyuan; Wang, Yixuan; Li, Yu

Published

2021

Language

EN