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An Evaluation of the Accuracy and Speed of Metagenome Analysis Tools

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Journal Sci Rep
Specialty Science
Date 2016 Jan 19
PMID 26778510
Citations 155
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Abstract

Metagenome studies are becoming increasingly widespread, yielding important insights into microbial communities covering diverse environments from terrestrial and aquatic ecosystems to human skin and gut. With the advent of high-throughput sequencing platforms, the use of large scale shotgun sequencing approaches is now commonplace. However, a thorough independent benchmark comparing state-of-the-art metagenome analysis tools is lacking. Here, we present a benchmark where the most widely used tools are tested on complex, realistic data sets. Our results clearly show that the most widely used tools are not necessarily the most accurate, that the most accurate tool is not necessarily the most time consuming, and that there is a high degree of variability between available tools. These findings are important as the conclusions of any metagenomics study are affected by errors in the predicted community composition and functional capacity. Data sets and results are freely available from http://www.ucbioinformatics.org/metabenchmark.html.

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References
1.
Sigrist C, de Castro E, Cerutti L, Cuche B, Hulo N, Bridge A . New and continuing developments at PROSITE. Nucleic Acids Res. 2012; 41(Database issue):D344-7. PMC: 3531220. DOI: 10.1093/nar/gks1067. View

2.
Wilke A, Harrison T, Wilkening J, Field D, Glass E, Kyrpides N . The M5nr: a novel non-redundant database containing protein sequences and annotations from multiple sources and associated tools. BMC Bioinformatics. 2012; 13:141. PMC: 3410781. DOI: 10.1186/1471-2105-13-141. View

3.
Tatusova T, Ciufo S, Fedorov B, ONeill K, Tolstoy I . RefSeq microbial genomes database: new representation and annotation strategy. Nucleic Acids Res. 2013; 42(Database issue):D553-9. PMC: 3965038. DOI: 10.1093/nar/gkt1274. View

4.
Cao C, Jiang W, Wang B, Fang J, Lang J, Tian G . Inhalable microorganisms in Beijing's PM2.5 and PM10 pollutants during a severe smog event. Environ Sci Technol. 2014; 48(3):1499-507. PMC: 3963435. DOI: 10.1021/es4048472. View

5.
Scher J, Abramson S . The microbiome and rheumatoid arthritis. Nat Rev Rheumatol. 2011; 7(10):569-78. PMC: 3275101. DOI: 10.1038/nrrheum.2011.121. View