MaxBin 2.0: An automated binning algorithm to recover genomes from multiple metagenomic datasets

Yu Wei Wu, Blake A. Simmons, Steven W. Singer

Research output: Contribution to journalArticlepeer-review

1120 Citations (Scopus)


Summary: The recovery of genomes from metagenomic datasets is a critical step to defining the functional roles of the underlying uncultivated populations. We previously developed MaxBin, an automated binning approach for high-throughput recovery of microbial genomes from metagenomes. Here we present an expanded binning algorithm, MaxBin 2.0, which recovers genomes from co-assembly of a collection of metagenomic datasets. Tests on simulated datasets revealed that MaxBin 2.0 is highly accurate in recovering individual genomes, and the application of MaxBin 2.0 to several metagenomes from environmental samples demonstrated that it could achieve two complementary goals: recovering more bacterial genomes compared to binning a single sample as well as comparing the microbial community composition between different sampling environments. Availability and implementation: MaxBin 2.0 is freely available at under BSD license. Supplementary information: Supplementary data are available at Bioinformatics online.

Original languageEnglish
Pages (from-to)605-607
Number of pages3
Issue number4
Publication statusPublished - Feb 2016
Externally publishedYes

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Biology
  • Computational Theory and Mathematics
  • Computer Science Applications
  • Computational Mathematics
  • Statistics and Probability


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