ArticleBioinformatics (Oxford, England)2023
SemiBin2: self-supervised contrastive learning leads to better MAGs for short- and long-read sequencing.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 134 papers.
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134 citing papers in PubMed.
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- Widespread genomic islands are hotspots of genome variations and mosaicism in giant viruses.Nature communications · 2026Article
- Deployable high-fidelity metagenome binning at scale with QuickBin.Communications biology · 2026Article
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- Environmental filtering shapes biosynthetic potential and resistome of antarctic microbiomes.World journal of microbiology & biotechnology · 2026Article
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- A genomic catalog of the mouse gut virome reveals features associated with ageing.Nature communications · 2026Article
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- Profiling the gut resistome to unlock antimicrobial resistance biology and inform clinical risk.Nature communications · 2026Review
- Shotgun metagenomics and metatranscriptomics of soil microbial communities under monoculture and polyculture cover crops.Microbiology resource announcements · 2026Article
- Prolonged Stagnation Reduces Treated Wastewater Biostability by Altering Microbial Community: Insights From Metaproteomics.Environmental microbiology · 2026Article
- Protocol for the assessment of the impact of mycotoxins and glyphosate residues on the gut microbiome and resistome of European fallow deer.STAR protocols · 2026Article
74 more citing papers are in PubMed but not listed here.
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3 authors.
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Abstract
motivationMetagenomic 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.
resultsWe 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 binners. Compared to SemiBin1, SemiBin2 can reconstruct 8.3-21.5% more high-quality bins and requires only 25% of the running time and 11% of peak memory usage in real short-read sequencing samples. To extend SemiBin2 to long-read data, we also propose ensemble-based DBSCAN clustering algorithm, resulting in 13.1-26.3% more high-quality genomes than the second best binner for long-read data. AVAILABILITY AND IMPLEMENTATION: SemiBin2 is available as open source software at https://github.com/BigDataBiology/SemiBin/ and the analysis scripts used in the study can be found at https://github.com/BigDataBiology/SemiBin2_benchmark.
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