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SQMtools: automated processing and visual analysis of ’omics data with R and anvi’o
BACKGROUND: The dramatic decrease in sequencing costs over the last decade has boosted the adoption of high-throughput sequencing applications as a standard tool for the analysis of environmental microbial communities. Nowadays even small research groups can easily obtain raw sequencing data. After...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7430844/ https://www.ncbi.nlm.nih.gov/pubmed/32795263 http://dx.doi.org/10.1186/s12859-020-03703-2 |
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author | Puente-Sánchez, Fernando García-García, Natalia Tamames, Javier |
author_facet | Puente-Sánchez, Fernando García-García, Natalia Tamames, Javier |
author_sort | Puente-Sánchez, Fernando |
collection | PubMed |
description | BACKGROUND: The dramatic decrease in sequencing costs over the last decade has boosted the adoption of high-throughput sequencing applications as a standard tool for the analysis of environmental microbial communities. Nowadays even small research groups can easily obtain raw sequencing data. After that, however, non-specialists are faced with the double challenge of choosing among an ever-increasing array of analysis methodologies, and navigating the vast amounts of results returned by these approaches. RESULTS: Here we present a workflow that relies on the SqueezeMeta software for the automated processing of raw reads into annotated contigs and reconstructed genomes (bins). A set of custom scripts seamlessly integrates the output into the anvi’o analysis platform, allowing filtering and visual exploration of the results. Furthermore, we provide a software package with utility functions to expose the SqueezeMeta results to the R analysis environment. CONCLUSIONS: Altogether, our workflow allows non-expert users to go from raw sequencing reads to custom plots with only a few powerful, flexible and well-documented commands. |
format | Online Article Text |
id | pubmed-7430844 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-74308442020-08-18 SQMtools: automated processing and visual analysis of ’omics data with R and anvi’o Puente-Sánchez, Fernando García-García, Natalia Tamames, Javier BMC Bioinformatics Methodology Article BACKGROUND: The dramatic decrease in sequencing costs over the last decade has boosted the adoption of high-throughput sequencing applications as a standard tool for the analysis of environmental microbial communities. Nowadays even small research groups can easily obtain raw sequencing data. After that, however, non-specialists are faced with the double challenge of choosing among an ever-increasing array of analysis methodologies, and navigating the vast amounts of results returned by these approaches. RESULTS: Here we present a workflow that relies on the SqueezeMeta software for the automated processing of raw reads into annotated contigs and reconstructed genomes (bins). A set of custom scripts seamlessly integrates the output into the anvi’o analysis platform, allowing filtering and visual exploration of the results. Furthermore, we provide a software package with utility functions to expose the SqueezeMeta results to the R analysis environment. CONCLUSIONS: Altogether, our workflow allows non-expert users to go from raw sequencing reads to custom plots with only a few powerful, flexible and well-documented commands. BioMed Central 2020-08-14 /pmc/articles/PMC7430844/ /pubmed/32795263 http://dx.doi.org/10.1186/s12859-020-03703-2 Text en © The Author(s) 2020 Open AccessThis 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/. The Creative Commons Public Domain Dedication waiver (http://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 | Methodology Article Puente-Sánchez, Fernando García-García, Natalia Tamames, Javier SQMtools: automated processing and visual analysis of ’omics data with R and anvi’o |
title | SQMtools: automated processing and visual analysis of ’omics data with R and anvi’o |
title_full | SQMtools: automated processing and visual analysis of ’omics data with R and anvi’o |
title_fullStr | SQMtools: automated processing and visual analysis of ’omics data with R and anvi’o |
title_full_unstemmed | SQMtools: automated processing and visual analysis of ’omics data with R and anvi’o |
title_short | SQMtools: automated processing and visual analysis of ’omics data with R and anvi’o |
title_sort | sqmtools: automated processing and visual analysis of ’omics data with r and anvi’o |
topic | Methodology Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7430844/ https://www.ncbi.nlm.nih.gov/pubmed/32795263 http://dx.doi.org/10.1186/s12859-020-03703-2 |
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