Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Kang DD

PROVIDER: S-EPMC6662567 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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

Kang Dongwan D DD Li Feng F Kirton Edward E Thomas Ashleigh A Egan Rob R An Hong H Wang Zhong Z

PeerJ 20190726

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 ...[more]

PMID: 31388474

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Project description:BACKGROUND:Travelers' diarrhea (TD) is often caused by enterotoxigenic Escherichia coli, enteroaggregative E. coli, other bacterial pathogens, Norovirus, and occasionally parasites. Nevertheless, standard diagnostic methods fail to identify pathogens in more than 40% of TD patients. It is predicted that new pathogens may be causative agents of the disease. RESULTS:We performed a comprehensive amplicon and whole genome shotgun (WGS) metagenomic study of the fecal microbiomes from 23 TD patients and seven healthy travelers, all of which were negative for the known etiologic agents of TD based on standard microbiological and immunological assays. Abnormal and diverse taxonomic profiles in TD samples were revealed. WGS reads were assembled and the resulting contigs were visualized using multiple query types. A semi-manual workflow was applied to isolate independent genomes from metagenomic pools. A total of 565 genome bins were extracted, 320 of which were complete enough to be characterized as cellular genomes; 160 were viral genomes. We made predictions of the etiology of disease for many of the individual subjects based on the properties and features of the recovered genomes. Multiple patients with low-diversity metagenomes were predominated by one to several E. coli strains. Functional annotation allowed prediction of pathogenic type in many cases. Five patients were co-infected with E. coli and other members of Enterobacteriaceae, including Enterobacter, Klebsiella, and Citrobacter; these may represent blooms of organisms that appear following secretory diarrhea. New "dark matter" microbes were observed in multiple samples. In one, we identified a novel TM7 genome that phylogenetically clustered with a sludge isolate; it carries genes encoding potential virulence factors. In multiple samples, we observed high proportions of putative novel viral genomes, some of which form clusters with the ubiquitous gut virus, crAssphage. The total relative abundance of viruses was significantly higher in healthy travelers versus TD patients. CONCLUSION:Our study highlights the strength of assembly-based metagenomics, especially the manually curated, visualization-assisted binning of contigs, in resolving unusual and under-characterized pathogenic profiles of human-associated microbiomes. Results show that TD may be polymicrobial, with multiple novel cellular and viral strains as potential players in the diarrheal disease.

Project description:BackgroundIn metagenomic studies, a process called binning is necessary to assign contigs that belong to multiple species to their respective phylogenetic groups. Most of the current methods of binning, such as BLAST, k-mer and PhyloPythia, involve assigning sequence fragments by comparing sequence similarity or sequence composition with already-sequenced genomes that are still far from comprehensive. We propose a semi-supervised seeding method for binning that does not depend on knowledge of completed genomes. Instead, it extracts the flanking sequences of highly conserved 16S rRNA from the metagenome and uses them as seeds (labels) to assign other reads based on their compositional similarity.ResultsThe proposed seeding method is implemented on an unsupervised Growing Self-Organising Map (GSOM), and called Seeded GSOM (S-GSOM). We compared it with four well-known semi-supervised learning methods in a preliminary test, separating random-length prokaryotic sequence fragments sampled from the NCBI genome database. We identified the flanking sequences of the highly conserved 16S rRNA as suitable seeds that could be used to group the sequence fragments according to their species. S-GSOM showed superior performance compared to the semi-supervised methods tested. Additionally, S-GSOM may also be used to visually identify some species that do not have seeds. The proposed method was then applied to simulated metagenomic datasets using two different confidence threshold settings and compared with PhyloPythia, k-mer and BLAST. At the reference taxonomic level Order, S-GSOM outperformed all k-mer and BLAST results and showed comparable results with PhyloPythia for each of the corresponding confidence settings, where S-GSOM performed better than PhyloPythia in the >/= 10 reads datasets and comparable in the > or = 8 kb benchmark tests.ConclusionIn the task of binning using semi-supervised learning methods, results indicate S-GSOM to be the best of the methods tested. Most importantly, the proposed method does not require knowledge from known genomes and uses only very few labels (one per species is sufficient in most cases), which are extracted from the metagenome itself. These advantages make it a very attractive binning method. S-GSOM outperformed the binning methods that depend on already-sequenced genomes, and compares well to the current most advanced binning method, PhyloPythia.

Dataset Information

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

Publications

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

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