MetaBAT: Metagenome Binning based on Abundance and Tetranucleotide frequence
Grouping large fragments assembled from shotgun metagenomic sequences to deconvolute complex microbial communities, or metagenome binning, enables the study of individual organisms and their interactions. Here we developed automated metagenome binning software, called MetaBAT, which integrates empirical probabilistic distances of genome abundance and tetranucleotide frequency. On synthetic datasets MetaBAT on average achieves 98percent precision and 90percent recall at the strain level with 281 near complete unique genomes. Applying MetaBAT to a human gut microbiome data set we recovered 176 genome bins with 92percent precision and 80percent recall. Further analyses suggest MetaBAT is able to recover genome fragments missed in reference genomes up to 19percent, while 53 genome bins are novel. In summary, we believe MetaBAT is a powerful tool to facilitate comprehensive understanding of complex microbial communities.
- Research Organization:
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC)
- DOE Contract Number:
- DE-AC02-05CH11231
- OSTI ID:
- 1241199
- Report Number(s):
- LBNL-7106E
- Resource Relation:
- Conference: 9th Annual JGI User Meeting
- Country of Publication:
- United States
- Language:
- English
Similar Records
MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities
MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies