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

Joshua Thody, Leighton Folkes, Zahara Medina-Calzada, Ping Xu, Tamas Dalmay, Vincent Moulton

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)
24 Downloads (Pure)

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.
Original languageEnglish
Pages (from-to)8730–8739
Number of pages10
JournalNucleic Acids Research
Volume46
Issue number17
Early online date11 Jul 2018
DOIs
Publication statusPublished - 28 Sep 2018

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