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TAMPA: interpretable analysis and visualization of metagenomics-based taxon abundance profiles

BACKGROUND: Metagenomic taxonomic profiling aims to predict the identity and relative abundance of taxa in a given whole-genome sequencing metagenomic sample. A recent surge in computational methods that aim to accurately estimate taxonomic profiles, called taxonomic profilers, has motivated communi...

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Autores principales: Sarwal, Varuni, Brito, Jaqueline, Mangul, Serghei, Koslicki, David
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9972184/
https://www.ncbi.nlm.nih.gov/pubmed/36852763
http://dx.doi.org/10.1093/gigascience/giad008
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author Sarwal, Varuni
Brito, Jaqueline
Mangul, Serghei
Koslicki, David
author_facet Sarwal, Varuni
Brito, Jaqueline
Mangul, Serghei
Koslicki, David
author_sort Sarwal, Varuni
collection PubMed
description BACKGROUND: Metagenomic taxonomic profiling aims to predict the identity and relative abundance of taxa in a given whole-genome sequencing metagenomic sample. A recent surge in computational methods that aim to accurately estimate taxonomic profiles, called taxonomic profilers, has motivated community-driven efforts to create standardized benchmarking datasets and platforms, standardized taxonomic profile formats, and a benchmarking platform to assess tool performance. While this standardization is essential, there is currently a lack of tools to visualize the standardized output of the many existing taxonomic profilers. Thus, benchmarking studies rely on a single-value metrics to compare performance of tools and compare to benchmarking datasets. This is one of the major problems in analyzing metagenomic profiling data, since single metrics, such as the F1 score, fail to capture the biological differences between the datasets. FINDINGS: Here we report the development of TAMPA (Taxonomic metagenome profiling evaluation), a robust and easy-to-use method that allows scientists to easily interpret and interact with taxonomic profiles produced by the many different taxonomic profiler methods beyond the standard metrics used by the scientific community. We demonstrate the unique ability of TAMPA to generate a novel biological hypothesis by highlighting the taxonomic differences between samples otherwise missed by commonly utilized metrics. CONCLUSION: In this study, we show that TAMPA can help visualize the output of taxonomic profilers, enabling biologists to effectively choose the most appropriate profiling method to use on their metagenomics data. TAMPA is available on GitHub, Bioconda, and Galaxy Toolshed at https://github.com/dkoslicki/TAMPA and is released under the MIT license.
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spelling pubmed-99721842023-03-01 TAMPA: interpretable analysis and visualization of metagenomics-based taxon abundance profiles Sarwal, Varuni Brito, Jaqueline Mangul, Serghei Koslicki, David Gigascience Technical Note BACKGROUND: Metagenomic taxonomic profiling aims to predict the identity and relative abundance of taxa in a given whole-genome sequencing metagenomic sample. A recent surge in computational methods that aim to accurately estimate taxonomic profiles, called taxonomic profilers, has motivated community-driven efforts to create standardized benchmarking datasets and platforms, standardized taxonomic profile formats, and a benchmarking platform to assess tool performance. While this standardization is essential, there is currently a lack of tools to visualize the standardized output of the many existing taxonomic profilers. Thus, benchmarking studies rely on a single-value metrics to compare performance of tools and compare to benchmarking datasets. This is one of the major problems in analyzing metagenomic profiling data, since single metrics, such as the F1 score, fail to capture the biological differences between the datasets. FINDINGS: Here we report the development of TAMPA (Taxonomic metagenome profiling evaluation), a robust and easy-to-use method that allows scientists to easily interpret and interact with taxonomic profiles produced by the many different taxonomic profiler methods beyond the standard metrics used by the scientific community. We demonstrate the unique ability of TAMPA to generate a novel biological hypothesis by highlighting the taxonomic differences between samples otherwise missed by commonly utilized metrics. CONCLUSION: In this study, we show that TAMPA can help visualize the output of taxonomic profilers, enabling biologists to effectively choose the most appropriate profiling method to use on their metagenomics data. TAMPA is available on GitHub, Bioconda, and Galaxy Toolshed at https://github.com/dkoslicki/TAMPA and is released under the MIT license. Oxford University Press 2023-02-28 /pmc/articles/PMC9972184/ /pubmed/36852763 http://dx.doi.org/10.1093/gigascience/giad008 Text en © The Author(s) 2023. Published by Oxford University Press GigaScience. 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 Technical Note
Sarwal, Varuni
Brito, Jaqueline
Mangul, Serghei
Koslicki, David
TAMPA: interpretable analysis and visualization of metagenomics-based taxon abundance profiles
title TAMPA: interpretable analysis and visualization of metagenomics-based taxon abundance profiles
title_full TAMPA: interpretable analysis and visualization of metagenomics-based taxon abundance profiles
title_fullStr TAMPA: interpretable analysis and visualization of metagenomics-based taxon abundance profiles
title_full_unstemmed TAMPA: interpretable analysis and visualization of metagenomics-based taxon abundance profiles
title_short TAMPA: interpretable analysis and visualization of metagenomics-based taxon abundance profiles
title_sort tampa: interpretable analysis and visualization of metagenomics-based taxon abundance profiles
topic Technical Note
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9972184/
https://www.ncbi.nlm.nih.gov/pubmed/36852763
http://dx.doi.org/10.1093/gigascience/giad008
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