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PACKAGE ID003006MLTPL00 METABAT
KWIC TitleMetaBAT 
AUTHORSDongwan Kang, Rob Egan, Jeff Froula, Zhnog Wang
LIMITATION/AUDIENCE CODEUNL/UNL
COMPLETION DATE04/01/2014 
PUBLICATION DATE04/01/2014 
DESCRIPTIONAssembling individual genomes from shotgun metagenomic sequences derived from complex microbial communities is so far one of the most challenging problems in bioinformatics. As it is impractical to directly assemble full-length genomes, a first step that groups contigs from the same organisms, called metagenome binning, has been developed to provide insights of individual organisms. However, current binning methods perform poorly in the context of large complex community, and as a result they fail to recover many novel genomes. To overcome this limitation, we developed integrated software, called MetaBAT, which automatically forms hundreds of individual genome bins from metagenome contigs. Probabilistic models of abundance and tetranucleotide frequency were trained by extensive empirical studies and integrated to decide the membership of contigs iteratively. To test the performance of MetaBAT, we applied MetaBAT to both synthetic and several large-scale real world metagenome datasets. By using two independent metrics, we demonstrate that in all the data sets tested MetaBAT achieves good sensitivity (16~87%) and very high specificity (56~99%) in forming genome bins. Further analyses of the novel genomes recovered from the human gut microbiome suggest a subset of these genomes are potentially associated with pathological conditions. In conclusion, we believe MetaBAT is a powerful tool 
PACKAGE CONTENTSAll Distribution will be handled by Lawrence Berkeley National Laboratory. 
SOURCE CODE INCLUDED?
MEDIA QUANTITY
COMPUTERMLT-PLTFM 
OPERATING SYSTEMSLinux, Mac, Windows 
PROGRAMMING LANGUAGESGCC 
TIME REQUIREMENTSC++ library 
ABSTRACT STATUSReleased As-Is 04/02/2014 
SPONSORUSDOE 
RESEARCH ORGLBNL 
PACKAGE TYPEAS-IS 

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