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UMGAP: the Unipept MetaGenomics Analysis Pipeline
BACKGROUND: Shotgun metagenomics yields ever richer and larger data volumes on the complex communities living in diverse environments. Extracting deep insights from the raw reads heavily depends on the availability of fast, accurate and user-friendly biodiversity analysis tools. RESULTS: Because env...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9188040/ https://www.ncbi.nlm.nih.gov/pubmed/35689184 http://dx.doi.org/10.1186/s12864-022-08542-4 |
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author | Van der Jeugt, Felix Maertens, Rien Steyaert, Aranka Verschaffelt, Pieter De Tender, Caroline Dawyndt, Peter Mesuere, Bart |
author_facet | Van der Jeugt, Felix Maertens, Rien Steyaert, Aranka Verschaffelt, Pieter De Tender, Caroline Dawyndt, Peter Mesuere, Bart |
author_sort | Van der Jeugt, Felix |
collection | PubMed |
description | BACKGROUND: Shotgun metagenomics yields ever richer and larger data volumes on the complex communities living in diverse environments. Extracting deep insights from the raw reads heavily depends on the availability of fast, accurate and user-friendly biodiversity analysis tools. RESULTS: Because environmental samples may contain strains and species that are not covered in reference databases and because protein sequences are more conserved than the genes encoding them, we explore the alternative route of taxonomic profiling based on protein coding regions translated from the shotgun metagenomics reads, instead of directly processing the DNA reads. We therefore developed the Unipept MetaGenomics Analysis Pipeline (UMGAP), a highly versatile suite of open source tools that are implemented in Rust and support parallelization to achieve optimal performance. Six preconfigured pipelines with different performance trade-offs were carefully selected, and benchmarked against a selection of state-of-the-art shotgun metagenomics taxonomic profiling tools. CONCLUSIONS: UMGAP’s protein space detour for taxonomic profiling makes it competitive with state-of-the-art shotgun metagenomics tools. Despite our design choices of an extra protein translation step, a broad spectrum index that can identify both archaea, bacteria, eukaryotes and viruses, and a highly configurable non-monolithic design, UMGAP achieves low runtime, manageable memory footprint and high accuracy. Its interactive visualizations allow for easy exploration and comparison of complex communities. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at (10.1186/s12864-022-08542-4). |
format | Online Article Text |
id | pubmed-9188040 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-91880402022-06-12 UMGAP: the Unipept MetaGenomics Analysis Pipeline Van der Jeugt, Felix Maertens, Rien Steyaert, Aranka Verschaffelt, Pieter De Tender, Caroline Dawyndt, Peter Mesuere, Bart BMC Genomics Software BACKGROUND: Shotgun metagenomics yields ever richer and larger data volumes on the complex communities living in diverse environments. Extracting deep insights from the raw reads heavily depends on the availability of fast, accurate and user-friendly biodiversity analysis tools. RESULTS: Because environmental samples may contain strains and species that are not covered in reference databases and because protein sequences are more conserved than the genes encoding them, we explore the alternative route of taxonomic profiling based on protein coding regions translated from the shotgun metagenomics reads, instead of directly processing the DNA reads. We therefore developed the Unipept MetaGenomics Analysis Pipeline (UMGAP), a highly versatile suite of open source tools that are implemented in Rust and support parallelization to achieve optimal performance. Six preconfigured pipelines with different performance trade-offs were carefully selected, and benchmarked against a selection of state-of-the-art shotgun metagenomics taxonomic profiling tools. CONCLUSIONS: UMGAP’s protein space detour for taxonomic profiling makes it competitive with state-of-the-art shotgun metagenomics tools. Despite our design choices of an extra protein translation step, a broad spectrum index that can identify both archaea, bacteria, eukaryotes and viruses, and a highly configurable non-monolithic design, UMGAP achieves low runtime, manageable memory footprint and high accuracy. Its interactive visualizations allow for easy exploration and comparison of complex communities. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at (10.1186/s12864-022-08542-4). BioMed Central 2022-06-10 /pmc/articles/PMC9188040/ /pubmed/35689184 http://dx.doi.org/10.1186/s12864-022-08542-4 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Software Van der Jeugt, Felix Maertens, Rien Steyaert, Aranka Verschaffelt, Pieter De Tender, Caroline Dawyndt, Peter Mesuere, Bart UMGAP: the Unipept MetaGenomics Analysis Pipeline |
title | UMGAP: the Unipept MetaGenomics Analysis Pipeline |
title_full | UMGAP: the Unipept MetaGenomics Analysis Pipeline |
title_fullStr | UMGAP: the Unipept MetaGenomics Analysis Pipeline |
title_full_unstemmed | UMGAP: the Unipept MetaGenomics Analysis Pipeline |
title_short | UMGAP: the Unipept MetaGenomics Analysis Pipeline |
title_sort | umgap: the unipept metagenomics analysis pipeline |
topic | Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9188040/ https://www.ncbi.nlm.nih.gov/pubmed/35689184 http://dx.doi.org/10.1186/s12864-022-08542-4 |
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