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how_are_we_stranded_here: quick determination of RNA-Seq strandedness
BACKGROUND: Quality control checks are the first step in RNA-Sequencing analysis, which enable the identification of common issues that occur in the sequenced reads. Checks for sequence quality, contamination, and complexity are commonplace, and allow users to implement steps downstream which can ac...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8783475/ https://www.ncbi.nlm.nih.gov/pubmed/35065593 http://dx.doi.org/10.1186/s12859-022-04572-7 |
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author | Signal, Brandon Kahlke, Tim |
author_facet | Signal, Brandon Kahlke, Tim |
author_sort | Signal, Brandon |
collection | PubMed |
description | BACKGROUND: Quality control checks are the first step in RNA-Sequencing analysis, which enable the identification of common issues that occur in the sequenced reads. Checks for sequence quality, contamination, and complexity are commonplace, and allow users to implement steps downstream which can account for these issues. Strand-specificity of reads is frequently overlooked and is often unavailable even in published data, yet when unknown or incorrectly specified can have detrimental effects on the reproducibility and accuracy of downstream analyses. RESULTS: To address these issues, we developed how_are_we_stranded_here, a Python library that helps to quickly infer strandedness of paired-end RNA-Sequencing data. Testing on both simulated and real RNA-Sequencing reads showed that it correctly measures strandedness, and measures outside the normal range may indicate sample contamination. CONCLUSIONS: how_are_we_stranded_here is fast and user friendly, making it easy to implement in quality control pipelines prior to analysing RNA-Sequencing data. how_are_we_stranded_here is freely available at https://github.com/betsig/how_are_we_stranded_here. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-022-04572-7. |
format | Online Article Text |
id | pubmed-8783475 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-87834752022-01-24 how_are_we_stranded_here: quick determination of RNA-Seq strandedness Signal, Brandon Kahlke, Tim BMC Bioinformatics Software BACKGROUND: Quality control checks are the first step in RNA-Sequencing analysis, which enable the identification of common issues that occur in the sequenced reads. Checks for sequence quality, contamination, and complexity are commonplace, and allow users to implement steps downstream which can account for these issues. Strand-specificity of reads is frequently overlooked and is often unavailable even in published data, yet when unknown or incorrectly specified can have detrimental effects on the reproducibility and accuracy of downstream analyses. RESULTS: To address these issues, we developed how_are_we_stranded_here, a Python library that helps to quickly infer strandedness of paired-end RNA-Sequencing data. Testing on both simulated and real RNA-Sequencing reads showed that it correctly measures strandedness, and measures outside the normal range may indicate sample contamination. CONCLUSIONS: how_are_we_stranded_here is fast and user friendly, making it easy to implement in quality control pipelines prior to analysing RNA-Sequencing data. how_are_we_stranded_here is freely available at https://github.com/betsig/how_are_we_stranded_here. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-022-04572-7. BioMed Central 2022-01-22 /pmc/articles/PMC8783475/ /pubmed/35065593 http://dx.doi.org/10.1186/s12859-022-04572-7 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/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/ (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 Signal, Brandon Kahlke, Tim how_are_we_stranded_here: quick determination of RNA-Seq strandedness |
title | how_are_we_stranded_here: quick determination of RNA-Seq strandedness |
title_full | how_are_we_stranded_here: quick determination of RNA-Seq strandedness |
title_fullStr | how_are_we_stranded_here: quick determination of RNA-Seq strandedness |
title_full_unstemmed | how_are_we_stranded_here: quick determination of RNA-Seq strandedness |
title_short | how_are_we_stranded_here: quick determination of RNA-Seq strandedness |
title_sort | how_are_we_stranded_here: quick determination of rna-seq strandedness |
topic | Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8783475/ https://www.ncbi.nlm.nih.gov/pubmed/35065593 http://dx.doi.org/10.1186/s12859-022-04572-7 |
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