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PeerJ. 2019 Jan 4;7:e6160. doi: 10.7717/peerj.6160. eCollection 2019.

Identifying accurate metagenome and amplicon software via a meta-analysis of sequence to taxonomy benchmarking studies.

Author information

1
Biomolecular Interactions Centre, School of Biological Sciences, University of Canterbury, Christchurch, New Zealand.
2
Department of Biochemistry, University of Otago, Dunedin, New Zealand.
3
Department of Microbiology and Immunology, University of Otago, Dunedin, New Zealand.
4
Institute of Environmental Science and Research, Porirua, New Zealand.
5
European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK.

Abstract

Metagenomic and meta-barcode DNA sequencing has rapidly become a widely-used technique for investigating a range of questions, particularly related to health and environmental monitoring. There has also been a proliferation of bioinformatic tools for analysing metagenomic and amplicon datasets, which makes selecting adequate tools a significant challenge. A number of benchmark studies have been undertaken; however, these can present conflicting results. In order to address this issue we have applied a robust Z-score ranking procedure and a network meta-analysis method to identify software tools that are consistently accurate for mapping DNA sequences to taxonomic hierarchies. Based upon these results we have identified some tools and computational strategies that produce robust predictions.

KEYWORDS:

Benchmark; Bioinformatics; Metabarcoding; Metabenchmark; Metagenomics; eDNA

Conflict of interest statement

The authors declare there are no competing interests.

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