CAMISIM: simulating metagenomes and microbial communities
Abstract 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 a...
| Main Authors: | , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2019-02-01
|
| Series: | Microbiome |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s40168-019-0633-6 |
| _version_ | 1830476271568551936 |
|---|---|
| author | Adrian Fritz Peter Hofmann Stephan Majda Eik Dahms Johannes Dröge Jessika Fiedler Till R. Lesker Peter Belmann Matthew Z. DeMaere Aaron E. Darling Alexander Sczyrba Andreas Bremges Alice C. McHardy |
| author_facet | Adrian Fritz Peter Hofmann Stephan Majda Eik Dahms Johannes Dröge Jessika Fiedler Till R. Lesker Peter Belmann Matthew Z. DeMaere Aaron E. Darling Alexander Sczyrba Andreas Bremges Alice C. McHardy |
| author_sort | Adrian Fritz |
| collection | DOAJ |
| description | Abstract 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. All data sets and the software are freely available at https://github.com/CAMI-challenge/CAMISIM |
| first_indexed | 2024-12-21T15:51:38Z |
| format | Article |
| id | doaj.art-691fffe684ce4951892d70988eef06af |
| institution | Directory Open Access Journal |
| issn | 2049-2618 |
| language | English |
| last_indexed | 2024-12-21T15:51:38Z |
| publishDate | 2019-02-01 |
| publisher | BMC |
| record_format | Article |
| series | Microbiome |
| spelling | doaj.art-691fffe684ce4951892d70988eef06af2022-12-21T18:58:13ZengBMCMicrobiome2049-26182019-02-017111210.1186/s40168-019-0633-6CAMISIM: simulating metagenomes and microbial communitiesAdrian Fritz0Peter Hofmann1Stephan Majda2Eik Dahms3Johannes Dröge4Jessika Fiedler5Till R. Lesker6Peter Belmann7Matthew Z. DeMaere8Aaron E. Darling9Alexander Sczyrba10Andreas Bremges11Alice C. McHardy12Computational Biology of Infection Research, Helmholtz Centre for Infection ResearchComputational Biology of Infection Research, Helmholtz Centre for Infection ResearchComputational Biology of Infection Research, Helmholtz Centre for Infection ResearchComputational Biology of Infection Research, Helmholtz Centre for Infection ResearchComputational Biology of Infection Research, Helmholtz Centre for Infection ResearchComputational Biology of Infection Research, Helmholtz Centre for Infection ResearchComputational Biology of Infection Research, Helmholtz Centre for Infection ResearchComputational Biology of Infection Research, Helmholtz Centre for Infection ResearchThe ithree institute, University of Technology SydneyThe ithree institute, University of Technology SydneyCenter for Biotechnology and Faculty of Technology, Bielefeld UniversityComputational Biology of Infection Research, Helmholtz Centre for Infection ResearchComputational Biology of Infection Research, Helmholtz Centre for Infection ResearchAbstract 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. All data sets and the software are freely available at https://github.com/CAMI-challenge/CAMISIMhttp://link.springer.com/article/10.1186/s40168-019-0633-6Metagenomics softwareMicrobial communityBenchmarkingSimulationMetagenome assemblyGenome binning |
| spellingShingle | Adrian Fritz Peter Hofmann Stephan Majda Eik Dahms Johannes Dröge Jessika Fiedler Till R. Lesker Peter Belmann Matthew Z. DeMaere Aaron E. Darling Alexander Sczyrba Andreas Bremges Alice C. McHardy CAMISIM: simulating metagenomes and microbial communities Microbiome Metagenomics software Microbial community Benchmarking Simulation Metagenome assembly Genome binning |
| title | CAMISIM: simulating metagenomes and microbial communities |
| title_full | CAMISIM: simulating metagenomes and microbial communities |
| title_fullStr | CAMISIM: simulating metagenomes and microbial communities |
| title_full_unstemmed | CAMISIM: simulating metagenomes and microbial communities |
| title_short | CAMISIM: simulating metagenomes and microbial communities |
| title_sort | camisim simulating metagenomes and microbial communities |
| topic | Metagenomics software Microbial community Benchmarking Simulation Metagenome assembly Genome binning |
| url | http://link.springer.com/article/10.1186/s40168-019-0633-6 |
| work_keys_str_mv | AT adrianfritz camisimsimulatingmetagenomesandmicrobialcommunities AT peterhofmann camisimsimulatingmetagenomesandmicrobialcommunities AT stephanmajda camisimsimulatingmetagenomesandmicrobialcommunities AT eikdahms camisimsimulatingmetagenomesandmicrobialcommunities AT johannesdroge camisimsimulatingmetagenomesandmicrobialcommunities AT jessikafiedler camisimsimulatingmetagenomesandmicrobialcommunities AT tillrlesker camisimsimulatingmetagenomesandmicrobialcommunities AT peterbelmann camisimsimulatingmetagenomesandmicrobialcommunities AT matthewzdemaere camisimsimulatingmetagenomesandmicrobialcommunities AT aaronedarling camisimsimulatingmetagenomesandmicrobialcommunities AT alexandersczyrba camisimsimulatingmetagenomesandmicrobialcommunities AT andreasbremges camisimsimulatingmetagenomesandmicrobialcommunities AT alicecmchardy camisimsimulatingmetagenomesandmicrobialcommunities |