SemiBin2: self-supervised contrastive learning leads to better MAGs for short- and long-read sequencing

MOTIVATION: Metagenomic binning methods to reconstruct metagenome-assembled genomes (MAGs) from environmental samples have been widely used in large-scale metagenomic studies. The recently proposed semi-supervised binning method, SemiBin, achieved state-of-the-art binning results in several environments. However, this required annotating contigs, a computationally costly and potentially biased process. RESULTS: We propose SemiBin2, which uses self-supervised learning to learn feature embeddings from the contigs. In simulated and real datasets, we show that self-supervised learning achieves better results than the semi-supervised learning used in SemiBin1 and that SemiBin2 outperforms other state-of-the-art…

• NN
  National Natural Science Foundation of China

  Awards: 31950410544, 61932008, T2225015, 61932008, 2020YFA0712403, 2018SHZDZX01, T2225015
• AF
  Albert-Ludwigs-Universität Freiburg
• FU
  Fudan University

  Award: 2018SHZDZX01
• NK
  National Key Research and Development Program of China

  Award: 2020YFA0712403