期刊
NATURE METHODS
卷 14, 期 11, 页码 1063-+出版社
NATURE PUBLISHING GROUP
DOI: 10.1038/NMETH.4458
关键词
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资金
- UK Engineering and Physical Sciences Research Council (EPSRC) [EP/K032208/1]
- US Department of Energy Joint Genome Institute [DE-AC02-05CH11231]
- Cluster of Excellence on Plant Sciences program of the Deutsche Forschungsgemeinschaft
- Australian Research Council's Linkage Projects [LP150100912]
- European Research Council advanced grant (PhyMo)
- Agency for Science, Technology and Research (A*STAR), Singapore
- Lundbeck Foundation [R44-A4384]
- VILLUM FONDEN Block Stipend on Mobilomics
- National Science Foundation (NSF) [DBI-1458689]
- NSF at the Pittsburgh Supercomputing Center (PSC), under the Extreme Science and Engineering Discovery Environment (XSEDE) [ACI-1445606, ACI-1041726]
- NSF [OCI-1053575]
- Div Of Molecular and Cellular Bioscience
- Direct For Biological Sciences [1330800] Funding Source: National Science Foundation
- Biotechnology and Biological Sciences Research Council [BB/L027801/1] Funding Source: researchfish
- Medical Research Council [MR/M50161X/1, MR/L015080/1] Funding Source: researchfish
- BBSRC [BB/L027801/1] Funding Source: UKRI
- MRC [MR/L015080/1] Funding Source: UKRI
Methods for assembly, taxonomic profiling and binning are key to interpreting metagenome data, but a lack of consensus about benchmarking complicates performance assessment. The Critical Assessment of Metagenome Interpretation (CAMI) challenge has engaged the global developer community to benchmark their programs on highly complex and realistic data sets, generated from similar to 700 newly sequenced microorganisms and similar to 600 novel viruses and plasmids and representing common experimental setups. Assembly and genome binning programs performed well for species represented by individual genomes but were substantially affected by the presence of related strains. Taxonomic profiling and binning programs were proficient at high taxonomic ranks, with a notable performance decrease below family level. Parameter settings markedly affected performance, underscoring their importance for program reproducibility. The CAMI results highlight current challenges but also provide a roadmap for software selection to answer specific research questions.
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