Comprehensive processing of high-throughput small RNA sequencing data including quality checking, normalization, and differential expression analysis using the UEA sRNA Workbench

Beckers, Matthew, Mohorianu, Irina-Ioana, Stocks, Matthew, Applegate, Christopher, Dalmay, Tamas ORCID: https://orcid.org/0000-0003-1492-5429 and Moulton, Vincent ORCID: https://orcid.org/0000-0001-9371-6435 (2017) Comprehensive processing of high-throughput small RNA sequencing data including quality checking, normalization, and differential expression analysis using the UEA sRNA Workbench. RNA, 23 (6). pp. 823-835. ISSN 1355-8382

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Abstract

Recently, high-throughput sequencing (HTS) has revealed compelling details about the small RNA (sRNA) population in eukaryotes. These 20 to 25 nt noncoding RNAs can influence gene expression by acting as guides for the sequence-specific regulatory mechanism known as RNA silencing. The increase in sequencing depth and number of samples per project enables a better understanding of the role sRNAs play by facilitating the study of expression patterns. However, the intricacy of the biological hypotheses coupled with a lack of appropriate tools often leads to inadequate mining of the available data and thus, an incomplete description of the biological mechanisms involved. To enable a comprehensive study of differential expression in sRNA data sets, we present a new interactive pipeline that guides researchers through the various stages of data preprocessing and analysis. This includes various tools, some of which we specifically developed for sRNA analysis, for quality checking and normalization of sRNA samples as well as tools for the detection of differentially expressed sRNAs and identification of the resulting expression patterns. The pipeline is available within the UEA sRNA Workbench, a user-friendly software package for the processing of sRNA data sets. We demonstrate the use of the pipeline on a H. sapiens data set; additional examples on a B. terrestris data set and on an A. thaliana data set are described in the Supplemental Information. A comparison with existing approaches is also included, which exemplifies some of the issues that need to be addressed for sRNA analysis and how the new pipeline may be used to do this.

Item Type:	Article
Faculty \ School:	Faculty of Science > School of Computing Sciences Faculty of Science > School of Biological Sciences Faculty of Science
UEA Research Groups:	Faculty of Science > Research Groups > Computational Biology > Computational biology of RNA (former - to 2018) Faculty of Science > Research Groups > Plant Sciences Faculty of Science > Research Groups > Computational Biology > Phylogenetics (former - to 2018) Faculty of Science > Research Groups > Computational Biology Faculty of Science > Research Groups > Norwich Epidemiology Centre Faculty of Medicine and Health Sciences > Research Groups > Norwich Epidemiology Centre
Depositing User:	Pure Connector
Date Deposited:	10 Mar 2017 01:41
Last Modified:	14 Jun 2023 12:54
URI:	https://ueaeprints.uea.ac.uk/id/eprint/62941
DOI:	10.1261/rna.059360.116

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