tRFTar: Prediction of tRF-target gene interactions via systemic re-analysis of Argonaute CLIP-seq datasets

Yiran Zhou; Haoran Peng; Qinghua Cui; Yuan Zhou

doi:10.1016/j.ymeth.2020.10.006

tRFTar: Prediction of tRF-target gene interactions via systemic re-analysis of Argonaute CLIP-seq datasets

Methods. 2021 Mar:187:57-67. doi: 10.1016/j.ymeth.2020.10.006. Epub 2020 Oct 9.

Authors

Yiran Zhou¹, Haoran Peng², Qinghua Cui³, Yuan Zhou⁴

Affiliations

¹ Department of Biomedical Informatics, Department of Physiology and Pathophysiology, Center for Noncoding RNA Medicine, MOE Key Lab of Cardiovascular Sciences, School of Basic Medical Sciences, Peking University, Beijing 100191, China.
² School of Life Sciences, Central China Normal University, Wuhan 430079, China.
³ Department of Biomedical Informatics, Department of Physiology and Pathophysiology, Center for Noncoding RNA Medicine, MOE Key Lab of Cardiovascular Sciences, School of Basic Medical Sciences, Peking University, Beijing 100191, China; Center of Bioinformatics, Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China. Electronic address: cuiqinghua@hsc.pku.edu.cn.
⁴ Department of Biomedical Informatics, Department of Physiology and Pathophysiology, Center for Noncoding RNA Medicine, MOE Key Lab of Cardiovascular Sciences, School of Basic Medical Sciences, Peking University, Beijing 100191, China. Electronic address: zhouyuanbioinfo@hsc.pku.edu.cn.

PMID: 33045361
DOI: 10.1016/j.ymeth.2020.10.006

Abstract

tRNA-derived fragments (tRFs), which by definition are cleaved from tRNAs, comprise a novel class of regulatory small non-coding RNAs. Recent evidence has revealed that tRFs can be loaded onto Argonaute (AGO) family proteins to perform post-transcriptional regulations via substantial tRF-target gene interactions (TGIs). However, there is no resource that systematically profiles potential AGO-mediated TGIs. To this end, we performed a systemic computational screening of potential AGO-mediated TGIs by a re-analysis of 146 crosslinking-immunoprecipitation and high-throughput sequencing (CLIP-seq) datasets in which 920,690 TGIs between 12,102 tRFs and 5,688 target genes were identified. The predicted TGIs have superior signal-to-noise ratio and good consistency with TGIs identified from an orthogonal technique. AGO-bound tRFs are not evenly distributed, where the 5'-tRF and 3'-tRF are enriched and some commonly expressed tRFs are also overrepresented. The tRFs tend to target conserved regions of transcripts and co-express with their target genes. Filtering TGIs with consistent co-expression with target genes results in a set of regulatory TGIs that contains 25,281 tRF-target pairs. Together, our results unveiled the extensive regulatory interactions between tRFs and target genes. Finally, the CLIP-derived TGIs were incorporated in a user-friendly online platform termed as tRFTar, where various functions like custom searching, co-expressed TGI filtering, genome browser and TGI-based tRF functional enrichment analysis are enabled to help users to investigate the functions of tRFs. The tRFTar is freely available at http://www.rnanut.net/tRFTar/.

Keywords: CLIP-seq; Post-transcriptional regulation; Small non-coding RNA; tRNA-derived fragments.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Argonaute Proteins / genetics*
Chromatin Immunoprecipitation Sequencing / methods*
Datasets as Topic
Epigenesis, Genetic
Epigenomics / methods*
Humans
MicroRNAs / genetics
MicroRNAs / metabolism*
RNA, Transfer / genetics
RNA, Transfer / metabolism
Software

Substances

Argonaute Proteins
MicroRNAs
RNA, Transfer