MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies
By Dongwan D. Kang, Feng Li, Edward Kirton, A. Thomas, Rob Egan, Hong An, Zhong Wang. PeerJ, 2019
3,317 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Molecular Biology, Plant Science
View Paper: https://www.semanticscholar.org/paper/350f06ca486a5d3dcce1617549e0292ba884893f
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Citations in the corpus
- Critical Assessment of Metagenome Interpretation: the second round of challenges (2021, background): “Vamb always had the best purity, while UltraBinner had the best ARI for the marine GSA, MetaWRAP for the strain-madness GSA and MetaBAT 39,40 for MAs and plant-associated assemblies.”