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LEMMI: A continuous benchmarking platform for metagenomics classifiers

View ORCID ProfileMathieu Seppey, View ORCID ProfileMosè Manni, Evgeny M. Zdobnov
doi: https://doi.org/10.1101/507731
Mathieu Seppey
Department of Genetic Medicine and Development, University of Geneva Medical School and Swiss Institute of Bioinformatics, Geneva, Switzerland
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Mosè Manni
Department of Genetic Medicine and Development, University of Geneva Medical School and Swiss Institute of Bioinformatics, Geneva, Switzerland
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Evgeny M. Zdobnov
Department of Genetic Medicine and Development, University of Geneva Medical School and Swiss Institute of Bioinformatics, Geneva, Switzerland
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  • For correspondence: evgeny.zdobnov@unige.ch
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Abstract

Studies of microbiomes are booming, as well as the diversity of computational tools to make sense out of the sequencing data and the volumes of accumulated microbial genotypes. LEMMI (https://lemmi.ezlab.org) is a novel concept of a benchmarking platform of computational tools for metagenome composition assessments that introduces: a continuous integration of tools, their multi-objective ranking, and an effective distribution through software containers. Here, we detail the workflow and discuss the evaluation of some recently released methods. We see this platform eventually as a community-driven effort: where method developers can showcase novel approaches and get unbiased benchmarks for publications, while users can make informed choices and obtain standardized and easy-to-use tools.

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  • The evaluation of recently published methods is discussed.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted October 24, 2019.
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LEMMI: A continuous benchmarking platform for metagenomics classifiers
Mathieu Seppey, Mosè Manni, Evgeny M. Zdobnov
bioRxiv 507731; doi: https://doi.org/10.1101/507731
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LEMMI: A continuous benchmarking platform for metagenomics classifiers
Mathieu Seppey, Mosè Manni, Evgeny M. Zdobnov
bioRxiv 507731; doi: https://doi.org/10.1101/507731

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