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benchdamic: benchmarking of differential abundance methods for microbiome data
SUMMARY: Recently, an increasing number of methodological approaches have been proposed to tackle the complexity of metagenomics and microbiome data. In this scenario, reproducibility and replicability have become two critical issues, and the development of computational frameworks for the comparati...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9825737/ https://www.ncbi.nlm.nih.gov/pubmed/36477500 http://dx.doi.org/10.1093/bioinformatics/btac778 |
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author | Calgaro, Matteo Romualdi, Chiara Risso, Davide Vitulo, Nicola |
author_facet | Calgaro, Matteo Romualdi, Chiara Risso, Davide Vitulo, Nicola |
author_sort | Calgaro, Matteo |
collection | PubMed |
description | SUMMARY: Recently, an increasing number of methodological approaches have been proposed to tackle the complexity of metagenomics and microbiome data. In this scenario, reproducibility and replicability have become two critical issues, and the development of computational frameworks for the comparative evaluations of such methods is of utmost importance. Here, we present benchdamic, a Bioconductor package to benchmark methods for the identification of differentially abundant taxa. AVAILABILITY AND IMPLEMENTATION: benchdamic is available as an open-source R package through the Bioconductor project at https://bioconductor.org/packages/benchdamic/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. |
format | Online Article Text |
id | pubmed-9825737 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-98257372023-01-10 benchdamic: benchmarking of differential abundance methods for microbiome data Calgaro, Matteo Romualdi, Chiara Risso, Davide Vitulo, Nicola Bioinformatics Applications Note SUMMARY: Recently, an increasing number of methodological approaches have been proposed to tackle the complexity of metagenomics and microbiome data. In this scenario, reproducibility and replicability have become two critical issues, and the development of computational frameworks for the comparative evaluations of such methods is of utmost importance. Here, we present benchdamic, a Bioconductor package to benchmark methods for the identification of differentially abundant taxa. AVAILABILITY AND IMPLEMENTATION: benchdamic is available as an open-source R package through the Bioconductor project at https://bioconductor.org/packages/benchdamic/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2022-12-07 /pmc/articles/PMC9825737/ /pubmed/36477500 http://dx.doi.org/10.1093/bioinformatics/btac778 Text en © The Author(s) 2022. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Applications Note Calgaro, Matteo Romualdi, Chiara Risso, Davide Vitulo, Nicola benchdamic: benchmarking of differential abundance methods for microbiome data |
title | benchdamic: benchmarking of differential abundance methods for microbiome data |
title_full | benchdamic: benchmarking of differential abundance methods for microbiome data |
title_fullStr | benchdamic: benchmarking of differential abundance methods for microbiome data |
title_full_unstemmed | benchdamic: benchmarking of differential abundance methods for microbiome data |
title_short | benchdamic: benchmarking of differential abundance methods for microbiome data |
title_sort | benchdamic: benchmarking of differential abundance methods for microbiome data |
topic | Applications Note |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9825737/ https://www.ncbi.nlm.nih.gov/pubmed/36477500 http://dx.doi.org/10.1093/bioinformatics/btac778 |
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