bin3C : Exploiting Hi-C sequencing data to accurately resolve metagenome-assembled genomes (MAGs)

DeMaere, M; Darling, A

bin3C : Exploiting Hi-C sequencing data to accurately resolve metagenome-assembled genomes (MAGs)

DeMaere, M

Darling, A

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Publisher:: BMC
Publication Type:: Journal Article
Citation:: Genome Biology, 2019, 20
Issue Date:: 2019

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Field	Value	Language
dc.contributor.author	DeMaere, M https://orcid.org/0000-0002-7601-5108	en_US
dc.contributor.author	Darling, A https://orcid.org/0000-0003-2397-7925	en_US
dc.date.issued	2019	en_US
dc.identifier.citation	Genome Biology, 2019, 20	en_US
dc.identifier.issn	1474-7596	en_US
dc.identifier.uri	http://hdl.handle.net/10453/128934
dc.description.abstract	Most microbes inhabiting the planet cannot be easily grown in the lab. Metagenomic techniques provide a means to study these organisms, and recent advances in the field have enabled the resolution of individual genomes from metagenomes, so-called Metagenome Assembled Genomes (MAGs). In addition to expanding the catalog of known microbial diversity, the systematic retrieval of MAGs stands as a tenable divide and conquer reduction of metagenome analysis to the simpler problem of single genome analysis. Many leading approaches to MAG retrieval depend upon time-series or transect data, whose effectiveness is a function of community complexity, target abundance and depth of sequencing. Without the need for time-series data, promising alternative methods are based upon the high-throughput sequencing technique called Hi-C. The Hi-C technique produces read-pairs which capture in-vivo DNA-DNA proximity interactions (contacts). The physical structure of the community modulates the signal derived from these interactions and a hierarchy of interaction rates exists (Intra-chromosomal > Inter-chromosomal > Inter-cellular). We describe an unsupervised method that exploits the hierarchical nature of Hi-C interaction rates to resolve MAGs from a single time-point. As a quantitative demonstration, next, we validate the method against the ground truth of a simulated human faecal microbiome. Lastly, we directly compare our method against a recently announced proprietary service ProxiMeta, which also performs MAG retrieval using Hi-C data. bin3C has been implemented as a simple open-source pipeline and makes use of the unsupervised community detection algorithm Infomap (https://github.com/cerebis/bin3C).	en_US
dc.publisher	BMC	en_US
dc.relation	http://purl.org/au-research/grants/arc/LP150100912
dc.relation	http://purl.org/au-research/grants/arc/DP180101506
dc.relation.ispartof	Genome Biology	en_US
dc.relation.isbasedon	10.1101/388355	en_US
dc.subject.classification	Bioinformatics	en_US
dc.title	bin3C : Exploiting Hi-C sequencing data to accurately resolve metagenome-assembled genomes (MAGs)	en_US
dc.type	Journal Article
utslib.citation.volume	20	en_US
utslib.for	0803 Computer Software	en_US
utslib.for	0604 Genetics	en_US
utslib.for	05 Environmental Sciences	en_US
utslib.for	06 Biological Sciences	en_US
utslib.for	08 Information and Computing Sciences	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Strength - ithree - Institute of Infection, Immunity and Innovation
utslib.copyright.status	open_access
pubs.declined	2018-12-10T13:09:28.904+1100
pubs.consider-herdc	false	en_US
pubs.publication-status	Published	en_US
pubs.volume	20	en_US

Abstract:

Most microbes inhabiting the planet cannot be easily grown in the lab. Metagenomic techniques provide a means to study these organisms, and recent advances in the field have enabled the resolution of individual genomes from metagenomes, so-called Metagenome Assembled Genomes (MAGs). In addition to expanding the catalog of known microbial diversity, the systematic retrieval of MAGs stands as a tenable divide and conquer reduction of metagenome analysis to the simpler problem of single genome analysis. Many leading approaches to MAG retrieval depend upon time-series or transect data, whose effectiveness is a function of community complexity, target abundance and depth of sequencing. Without the need for time-series data, promising alternative methods are based upon the high-throughput sequencing technique called Hi-C. The Hi-C technique produces read-pairs which capture in-vivo DNA-DNA proximity interactions (contacts). The physical structure of the community modulates the signal derived from these interactions and a hierarchy of interaction rates exists (Intra-chromosomal > Inter-chromosomal > Inter-cellular). We describe an unsupervised method that exploits the hierarchical nature of Hi-C interaction rates to resolve MAGs from a single time-point. As a quantitative demonstration, next, we validate the method against the ground truth of a simulated human faecal microbiome. Lastly, we directly compare our method against a recently announced proprietary service ProxiMeta, which also performs MAG retrieval using Hi-C data. bin3C has been implemented as a simple open-source pipeline and makes use of the unsupervised community detection algorithm Infomap (https://github.com/cerebis/bin3C).

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http://hdl.handle.net/10453/128934