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ProkSeq for complete analysis of RNA-Seq data from prokaryotes
SUMMARY: Since its introduction, RNA-Seq technology has been used extensively in studies of pathogenic bacteria to identify and quantify differences in gene expression across multiple samples from bacteria exposed to different conditions. With some exceptions, tools for studying gene expression, det...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8034529/ https://www.ncbi.nlm.nih.gov/pubmed/33367516 http://dx.doi.org/10.1093/bioinformatics/btaa1063 |
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author | Mahmud, A K M Firoj Delhomme, Nicolas Nandi, Soumyadeep Fällman, Maria |
author_facet | Mahmud, A K M Firoj Delhomme, Nicolas Nandi, Soumyadeep Fällman, Maria |
author_sort | Mahmud, A K M Firoj |
collection | PubMed |
description | SUMMARY: Since its introduction, RNA-Seq technology has been used extensively in studies of pathogenic bacteria to identify and quantify differences in gene expression across multiple samples from bacteria exposed to different conditions. With some exceptions, tools for studying gene expression, determination of differential gene expression, downstream pathway analysis and normalization of data collected in extreme biological conditions is still lacking. Here, we describe ProkSeq, a user-friendly, fully automated RNA-Seq data analysis pipeline designed for prokaryotes. ProkSeq provides a wide variety of options for analysing differential expression, normalizing expression data and visualizing data and results. AVAILABILITY AND IMPLEMENTATION: ProkSeq is implemented in Python and is published under the MIT source license. The pipeline is available as a Docker container https://hub.docker.com/repository/docker/snandids/prokseq-v2.0, or can be used through Anaconda: https://anaconda.org/snandiDS/prokseq. The code is available on Github: https://github.com/snandiDS/prokseq and a detailed user documentation, including a manual and tutorial can be found at https://prokseqV20.readthedocs.io. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. |
format | Online Article Text |
id | pubmed-8034529 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-80345292021-04-14 ProkSeq for complete analysis of RNA-Seq data from prokaryotes Mahmud, A K M Firoj Delhomme, Nicolas Nandi, Soumyadeep Fällman, Maria Bioinformatics Applications Notes SUMMARY: Since its introduction, RNA-Seq technology has been used extensively in studies of pathogenic bacteria to identify and quantify differences in gene expression across multiple samples from bacteria exposed to different conditions. With some exceptions, tools for studying gene expression, determination of differential gene expression, downstream pathway analysis and normalization of data collected in extreme biological conditions is still lacking. Here, we describe ProkSeq, a user-friendly, fully automated RNA-Seq data analysis pipeline designed for prokaryotes. ProkSeq provides a wide variety of options for analysing differential expression, normalizing expression data and visualizing data and results. AVAILABILITY AND IMPLEMENTATION: ProkSeq is implemented in Python and is published under the MIT source license. The pipeline is available as a Docker container https://hub.docker.com/repository/docker/snandids/prokseq-v2.0, or can be used through Anaconda: https://anaconda.org/snandiDS/prokseq. The code is available on Github: https://github.com/snandiDS/prokseq and a detailed user documentation, including a manual and tutorial can be found at https://prokseqV20.readthedocs.io. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2020-12-26 /pmc/articles/PMC8034529/ /pubmed/33367516 http://dx.doi.org/10.1093/bioinformatics/btaa1063 Text en © The Author(s) 2020. 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 (http://creativecommons.org/licenses/by/4.0/ (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 Notes Mahmud, A K M Firoj Delhomme, Nicolas Nandi, Soumyadeep Fällman, Maria ProkSeq for complete analysis of RNA-Seq data from prokaryotes |
title | ProkSeq for complete analysis of RNA-Seq data from prokaryotes |
title_full | ProkSeq for complete analysis of RNA-Seq data from prokaryotes |
title_fullStr | ProkSeq for complete analysis of RNA-Seq data from prokaryotes |
title_full_unstemmed | ProkSeq for complete analysis of RNA-Seq data from prokaryotes |
title_short | ProkSeq for complete analysis of RNA-Seq data from prokaryotes |
title_sort | prokseq for complete analysis of rna-seq data from prokaryotes |
topic | Applications Notes |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8034529/ https://www.ncbi.nlm.nih.gov/pubmed/33367516 http://dx.doi.org/10.1093/bioinformatics/btaa1063 |
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