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MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies.

We previously reported on MetaBAT, an automated metagenome binning software tool to reconstruct single genomes from microbial communities for subsequent analyses of uncultivated microbial species. MetaBAT has become one of the most popular binning tools largely due to its computational efficiency and ease of use, especially in binning experiments with a large number of samples and a large assembly. MetaBAT requires users to choose parameters to fine-tune its sensitivity and specificity. If those parameters are not chosen properly, binning accuracy can suffer, especially on assemblies of poor quality. Here, we developed MetaBAT 2 to overcome this problem. MetaBAT 2 uses a new adaptive binning algorithm to eliminate manual parameter tuning. We also performed extensive software engineering optimization to increase both computational and memory efficiency. Comparing MetaBAT 2 to alternative software tools on over 100 real world metagenome assemblies shows superior accuracy and computing sp

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MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies.

> 商业许可源文 · EUROPE_PMC · [CC0](https://creativecommons.org/publicdomain/zero/1.0/)

书目信息

  • 引用：Kang DD, Li F, Kirton E, Thomas A, Egan R, An H, Wang Z. (2019). MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ. PMID 31388474 · PMC6662567 · DOI 10.7717/peerj.7359
  • 证据类型：METHODS
  • 主题：metagenomics
  • 被引次数（采集时）：3096
  • 原始记录：[Europe PMC](https://europepmc.org/article/MED/31388474)
  • 来源许可：[CC0](https://creativecommons.org/publicdomain/zero/1.0/)

    • 作者摘要（按来源许可复用）

    We previously reported on MetaBAT, an automated metagenome binning software tool to reconstruct single genomes from microbial communities for subsequent analyses of uncultivated microbial species. MetaBAT has become one of the most popular binning tools largely due to its computational efficiency and ease of use, especially in binning experiments with a large number of samples and a large assembly. MetaBAT requires users to choose parameters to fine-tune its sensitivity and specificity. If those parameters are not chosen properly, binning accuracy can suffer, especially on assemblies of poor quality. Here, we developed MetaBAT 2 to overcome this problem. MetaBAT 2 uses a new adaptive binning algorithm to eliminate manual parameter tuning. We also performed extensive software engineering optimization to increase both computational and memory efficiency. Comparing MetaBAT 2 to alternative software tools on over 100 real world metagenome assemblies shows superior accuracy and computing speed. Binning a typical metagenome assembly takes only a few minutes on a single commodity workstation. We therefore recommend the community adopts MetaBAT 2 for their metagenome binning experiments. MetaBAT 2 is open source software and available at https://bitbucket.org/berkeleylab/metabat.

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    MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. · GeniOmics