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sRNAnalyzer—a flexible and customizable small RNA sequencing data analysis pipeline

Although many tools have been developed to analyze small RNA sequencing (sRNA-Seq) data, it remains challenging to accurately analyze the small RNA population, mainly due to multiple sequence ID assignment caused by short read length. Additional issues in small RNA analysis include low consistency o...

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Autores principales: Wu, Xiaogang, Kim, Taek-Kyun, Baxter, David, Scherler, Kelsey, Gordon, Aaron, Fong, Olivia, Etheridge, Alton, Galas, David J., Wang, Kai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5716150/
https://www.ncbi.nlm.nih.gov/pubmed/29069500
http://dx.doi.org/10.1093/nar/gkx999
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author Wu, Xiaogang
Kim, Taek-Kyun
Baxter, David
Scherler, Kelsey
Gordon, Aaron
Fong, Olivia
Etheridge, Alton
Galas, David J.
Wang, Kai
author_facet Wu, Xiaogang
Kim, Taek-Kyun
Baxter, David
Scherler, Kelsey
Gordon, Aaron
Fong, Olivia
Etheridge, Alton
Galas, David J.
Wang, Kai
author_sort Wu, Xiaogang
collection PubMed
description Although many tools have been developed to analyze small RNA sequencing (sRNA-Seq) data, it remains challenging to accurately analyze the small RNA population, mainly due to multiple sequence ID assignment caused by short read length. Additional issues in small RNA analysis include low consistency of microRNA (miRNA) measurement results across different platforms, miRNA mapping associated with miRNA sequence variation (isomiR) and RNA editing, and the origin of those unmapped reads after screening against all endogenous reference sequence databases. To address these issues, we built a comprehensive and customizable sRNA-Seq data analysis pipeline—sRNAnalyzer, which enables: (i) comprehensive miRNA profiling strategies to better handle isomiRs and summarization based on each nucleotide position to detect potential SNPs in miRNAs, (ii) different sequence mapping result assignment approaches to simulate results from microarray/qRT-PCR platforms and a local probabilistic model to assign mapping results to the most-likely IDs, (iii) comprehensive ribosomal RNA filtering for accurate mapping of exogenous RNAs and summarization based on taxonomy annotation. We evaluated our pipeline on both artificial samples (including synthetic miRNA and Escherichia coli cultures) and biological samples (human tissue and plasma). sRNAnalyzer is implemented in Perl and available at: http://srnanalyzer.systemsbiology.net/.
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spelling pubmed-57161502017-12-08 sRNAnalyzer—a flexible and customizable small RNA sequencing data analysis pipeline Wu, Xiaogang Kim, Taek-Kyun Baxter, David Scherler, Kelsey Gordon, Aaron Fong, Olivia Etheridge, Alton Galas, David J. Wang, Kai Nucleic Acids Res Computational Biology Although many tools have been developed to analyze small RNA sequencing (sRNA-Seq) data, it remains challenging to accurately analyze the small RNA population, mainly due to multiple sequence ID assignment caused by short read length. Additional issues in small RNA analysis include low consistency of microRNA (miRNA) measurement results across different platforms, miRNA mapping associated with miRNA sequence variation (isomiR) and RNA editing, and the origin of those unmapped reads after screening against all endogenous reference sequence databases. To address these issues, we built a comprehensive and customizable sRNA-Seq data analysis pipeline—sRNAnalyzer, which enables: (i) comprehensive miRNA profiling strategies to better handle isomiRs and summarization based on each nucleotide position to detect potential SNPs in miRNAs, (ii) different sequence mapping result assignment approaches to simulate results from microarray/qRT-PCR platforms and a local probabilistic model to assign mapping results to the most-likely IDs, (iii) comprehensive ribosomal RNA filtering for accurate mapping of exogenous RNAs and summarization based on taxonomy annotation. We evaluated our pipeline on both artificial samples (including synthetic miRNA and Escherichia coli cultures) and biological samples (human tissue and plasma). sRNAnalyzer is implemented in Perl and available at: http://srnanalyzer.systemsbiology.net/. Oxford University Press 2017-12-01 2017-10-24 /pmc/articles/PMC5716150/ /pubmed/29069500 http://dx.doi.org/10.1093/nar/gkx999 Text en © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Computational Biology
Wu, Xiaogang
Kim, Taek-Kyun
Baxter, David
Scherler, Kelsey
Gordon, Aaron
Fong, Olivia
Etheridge, Alton
Galas, David J.
Wang, Kai
sRNAnalyzer—a flexible and customizable small RNA sequencing data analysis pipeline
title sRNAnalyzer—a flexible and customizable small RNA sequencing data analysis pipeline
title_full sRNAnalyzer—a flexible and customizable small RNA sequencing data analysis pipeline
title_fullStr sRNAnalyzer—a flexible and customizable small RNA sequencing data analysis pipeline
title_full_unstemmed sRNAnalyzer—a flexible and customizable small RNA sequencing data analysis pipeline
title_short sRNAnalyzer—a flexible and customizable small RNA sequencing data analysis pipeline
title_sort srnanalyzer—a flexible and customizable small rna sequencing data analysis pipeline
topic Computational Biology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5716150/
https://www.ncbi.nlm.nih.gov/pubmed/29069500
http://dx.doi.org/10.1093/nar/gkx999
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