sRNATargetDigger: A bioinformatics software for bidirectional identification of sRNA-target pairs with co-regulatory sRNAs information

PLoS One. 2020 Dec 28;15(12):e0244480. doi: 10.1371/journal.pone.0244480. eCollection 2020.

Abstract

Identification of the target genes of microRNAs (miRNAs), trans-acting small interfering RNAs (ta-siRNAs), and small interfering RNAs (siRNAs) is an important step for understanding their regulatory roles in plants. In recent years, many bioinformatics software packages based on small RNA (sRNA) high-throughput sequencing (HTS) and degradome sequencing data analysis have provided strong technical support for large-scale mining of sRNA-target pairs. However, sRNA-target regulation is achieved using a complex network of interactions since one transcript might be co-regulated by multiple sRNAs and one sRNA may also affect multiple targets. Currently used mining software can realize the mining of multiple unknown targets using known sRNA, but it cannot rule out the possibility of co-regulation of the same target by other unknown sRNAs. Hence, the obtained regulatory network may be incomplete. We have developed a new mining software, sRNATargetDigger, that includes two function modules, "Forward Digger" and "Reverse Digger", which can identify regulatory sRNA-target pairs bidirectionally. Moreover, it has the ability to identify unknown sRNAs co-regulating the same target, in order to obtain a more authentic and reliable sRNA-target regulatory network. Upon re-examination of the published sRNA-target pairs in Arabidopsis thaliana, sRNATargetDigger found 170 novel co-regulatory sRNA-target pairs. This software can be downloaded from http://www.bioinfolab.cn/sRNATD.html.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Arabidopsis / genetics
  • Computational Biology / methods*
  • Data Mining / methods*
  • Datasets as Topic
  • Gene Expression Regulation, Plant
  • Gene Regulatory Networks*
  • High-Throughput Nucleotide Sequencing
  • RNA Stability / genetics
  • RNA, Small Untranslated / genetics
  • RNA, Small Untranslated / metabolism*
  • Software*

Substances

  • RNA, Small Untranslated

Grants and funding

This work was supported by National Natural Science Foundation of China [31771457, 31801102, 31970637]; Zhejiang Provincial Natural Science Foundation of China [LY17C190001] and Public Welfare Technology Application Research Project of Zhejiang Province [2016C33193].The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.