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 an...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2019-07-01
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| Series: | PeerJ |
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| Online Access: | https://peerj.com/articles/7359.pdf |
| _version_ | 1797420160642973696 |
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| author | Dongwan D. Kang Feng Li Edward Kirton Ashleigh Thomas Rob Egan Hong An Zhong Wang |
| author_facet | Dongwan D. Kang Feng Li Edward Kirton Ashleigh Thomas Rob Egan Hong An Zhong Wang |
| author_sort | Dongwan D. Kang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T06:58:37Z |
| format | Article |
| id | doaj.art-ef93cd75315146248e60fd974ebfe6dd |
| institution | Directory Open Access Journal |
| issn | 2167-8359 |
| language | English |
| last_indexed | 2024-03-09T06:58:37Z |
| publishDate | 2019-07-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ |
| spelling | doaj.art-ef93cd75315146248e60fd974ebfe6dd2023-12-03T10:00:29ZengPeerJ Inc.PeerJ2167-83592019-07-017e735910.7717/peerj.7359MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assembliesDongwan D. Kang0Feng Li1Edward Kirton2Ashleigh Thomas3Rob Egan4Hong An5Zhong Wang6Department of Energy, Joint Genome Institute, Walnut Creek, CA, USASchool of Computer Science and Technology, University of Science and Technology of China, Hefei, Anhui, ChinaDepartment of Energy, Joint Genome Institute, Walnut Creek, CA, USADepartment of Energy, Joint Genome Institute, Walnut Creek, CA, USADepartment of Energy, Joint Genome Institute, Walnut Creek, CA, USASchool of Computer Science and Technology, University of Science and Technology of China, Hefei, Anhui, ChinaDepartment of Energy, Joint Genome Institute, Walnut Creek, CA, USAWe 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.https://peerj.com/articles/7359.pdfMetagenomicsMetagenome binningClustering |
| spellingShingle | Dongwan D. Kang Feng Li Edward Kirton Ashleigh Thomas Rob Egan Hong An Zhong Wang MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies PeerJ Metagenomics Metagenome binning Clustering |
| title | MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies |
| title_full | MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies |
| title_fullStr | MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies |
| title_full_unstemmed | MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies |
| title_short | MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies |
| title_sort | metabat 2 an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies |
| topic | Metagenomics Metagenome binning Clustering |
| url | https://peerj.com/articles/7359.pdf |
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