PAREsnip2: A tool for high-throughput prediction of small RNA targets from degradome sequencing data using configurable targeting rules

Thody, Joshua, Folkes, Leighton, Medina-Calzada, Zahara, Xu, Ping, Dalmay, Tamas ORCID: https://orcid.org/0000-0003-1492-5429 and Moulton, Vincent ORCID: https://orcid.org/0000-0001-9371-6435 (2018) PAREsnip2: A tool for high-throughput prediction of small RNA targets from degradome sequencing data using configurable targeting rules. Nucleic Acids Research, 46 (17). 8730–8739. ISSN 0305-1048

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Abstract

Small RNAs (sRNAs) are short, non-coding RNAs that play critical roles in many important biological pathways. They suppress the translation of messenger RNAs (mRNAs) by directing the RNA-induced silencing complex to their sequence-specific mRNA target(s). In plants, this typically results in mRNA cleavage and subsequent degradation of the mRNA. The resulting mRNA fragments, or degradome, provide evidence for these interactions, and thus degradome analysis has become an important tool for sRNA target prediction. Even so, with the continuing advances in sequencing technologies, not only are larger and more complex genomes being sequenced, but also degradome and associated datasets are growing both in number and read count. As a result, existing degradome analysis tools are unable to process the volume of data being produced without imposing huge resource and time requirements. Moreover, these tools use stringent, non-configurable targeting rules, which reduces their flexibility. Here, we present a new and user configurable software tool for degradome analysis, which employs a novel search algorithm and sequence encoding technique to reduce the search space during analysis. The tool significantly reduces the time and resources required to perform degradome analysis, in some cases providing more than two orders of magnitude speed-up over current methods.

Item Type:	Article
Faculty \ School:	Faculty of Science > School of Computing Sciences Faculty of Science > School of Biological Sciences
UEA Research Groups:	Faculty of Science > Research Groups > Plant Sciences Faculty of Science > Research Groups > Computational Biology Faculty of Science > Research Groups > Computational Biology > Phylogenetics (former - to 2018) Faculty of Science > Research Groups > Norwich Epidemiology Centre Faculty of Medicine and Health Sciences > Research Groups > Norwich Epidemiology Centre
Depositing User:	LivePure Connector
Date Deposited:	26 Jun 2018 09:30
Last Modified:	14 Jun 2023 13:26
URI:	https://ueaeprints.uea.ac.uk/id/eprint/67435
DOI:	10.1093/nar/gky609

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