CAMISIM: Simulating metagenomes and microbial communities

Fritz, A; Hofmann, P; Majda, S; Dahms, E; Dröge, J; Fiedler, J; Lesker, TR; Belmann, P; Demaere, MZ; Darling, AE; Sczyrba, A; Bremges, A; McHardy, AC

CAMISIM: Simulating metagenomes and microbial communities

Fritz, A Hofmann, P Majda, S Dahms, E Dröge, J Fiedler, J Lesker, TR Belmann, P Demaere, MZ

Darling, AE

Sczyrba, A

Bremges, A McHardy, AC

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Publication Type:: Journal Article
Citation:: Microbiome, 2019, 7 (1)
Issue Date:: 2019-02-08

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Field	Value	Language
dc.contributor.author	Fritz, A	en_US
dc.contributor.author	Hofmann, P	en_US
dc.contributor.author	Majda, S	en_US
dc.contributor.author	Dahms, E	en_US
dc.contributor.author	Dröge, J	en_US
dc.contributor.author	Fiedler, J	en_US
dc.contributor.author	Lesker, TR	en_US
dc.contributor.author	Belmann, P	en_US
dc.contributor.author	Demaere, MZ https://orcid.org/0000-0002-7601-5108	en_US
dc.contributor.author	Darling, AE https://orcid.org/0000-0003-2397-7925	en_US
dc.contributor.author	Sczyrba, A https://orcid.org/0000-0002-4405-3847	en_US
dc.contributor.author	Bremges, A	en_US
dc.contributor.author	McHardy, AC https://orcid.org/0000-0003-2370-3430	en_US
dc.date.available	2019-01-21	en_US
dc.date.issued	2019-02-08	en_US
dc.identifier.citation	Microbiome, 2019, 7 (1)	en_US
dc.identifier.uri	http://hdl.handle.net/10453/130649
dc.description.abstract	© 2019 The Author(s). Background: Shotgun metagenome data sets of microbial communities are highly diverse, not only due to the natural variation of the underlying biological systems, but also due to differences in laboratory protocols, replicate numbers, and sequencing technologies. Accordingly, to effectively assess the performance of metagenomic analysis software, a wide range of benchmark data sets are required. Results: We describe the CAMISIM microbial community and metagenome simulator. The software can model different microbial abundance profiles, multi-sample time series, and differential abundance studies, includes real and simulated strain-level diversity, and generates second- and third-generation sequencing data from taxonomic profiles or de novo. Gold standards are created for sequence assembly, genome binning, taxonomic binning, and taxonomic profiling. CAMSIM generated the benchmark data sets of the first CAMI challenge. For two simulated multi-sample data sets of the human and mouse gut microbiomes, we observed high functional congruence to the real data. As further applications, we investigated the effect of varying evolutionary genome divergence, sequencing depth, and read error profiles on two popular metagenome assemblers, MEGAHIT, and metaSPAdes, on several thousand small data sets generated with CAMISIM. Conclusions: CAMISIM can simulate a wide variety of microbial communities and metagenome data sets together with standards of truth for method evaluation.	en_US
dc.relation.ispartof	Microbiome	en_US
dc.relation.isbasedon	10.1186/s40168-019-0633-6	en_US
dc.subject.mesh	Animals	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Mice	en_US
dc.subject.mesh	Sequence Analysis, DNA	en_US
dc.subject.mesh	Algorithms	en_US
dc.subject.mesh	Models, Biological	en_US
dc.subject.mesh	Computer Simulation	en_US
dc.subject.mesh	Software	en_US
dc.subject.mesh	Metagenome	en_US
dc.subject.mesh	Metagenomics	en_US
dc.subject.mesh	Gastrointestinal Microbiome	en_US
dc.title	CAMISIM: Simulating metagenomes and microbial communities	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	7	en_US
utslib.for	0604 Genetics	en_US
utslib.for	0803 Computer Software	en_US
utslib.for	0602 Ecology	en_US
utslib.for	0605 Microbiology	en_US
utslib.for	1108 Medical Microbiology	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.issue	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	7	en_US

Abstract:

© 2019 The Author(s). Background: Shotgun metagenome data sets of microbial communities are highly diverse, not only due to the natural variation of the underlying biological systems, but also due to differences in laboratory protocols, replicate numbers, and sequencing technologies. Accordingly, to effectively assess the performance of metagenomic analysis software, a wide range of benchmark data sets are required. Results: We describe the CAMISIM microbial community and metagenome simulator. The software can model different microbial abundance profiles, multi-sample time series, and differential abundance studies, includes real and simulated strain-level diversity, and generates second- and third-generation sequencing data from taxonomic profiles or de novo. Gold standards are created for sequence assembly, genome binning, taxonomic binning, and taxonomic profiling. CAMSIM generated the benchmark data sets of the first CAMI challenge. For two simulated multi-sample data sets of the human and mouse gut microbiomes, we observed high functional congruence to the real data. As further applications, we investigated the effect of varying evolutionary genome divergence, sequencing depth, and read error profiles on two popular metagenome assemblers, MEGAHIT, and metaSPAdes, on several thousand small data sets generated with CAMISIM. Conclusions: CAMISIM can simulate a wide variety of microbial communities and metagenome data sets together with standards of truth for method evaluation.

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http://hdl.handle.net/10453/130649