tRF2Cancer: A web server to detect tRNA-derived small RNA fragments (tRFs) and their expression in multiple cancers
Ling-Ling Zheng1, Wei-Lin Xu1, Shun Liu1
1Key Laboratory of Gene Engineering of the Ministry of Education, State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou 510275, P. R. China.
Abstract:
tRNA-derived small RNA fragments (tRFs) are one class of small non-coding RNAs derived from transfer RNAs (tRNAs). tRFs play important roles in cellular processes and are involved in multiple cancers. High-throughput small RNA (sRNA) sequencing experiments can detect all the cellular expressed sRNAs, including tRFs. However, distinguishing genuine tRFs from RNA fragments generated by random degradation remains a major challenge. In this study, we developed an integrated web-based computing system, tRF2Cancer, to accurately identify tRFs from sRNA deep-sequencing data and evaluate their expression in multiple cancers. The binomial test was introduced to evaluate whether reads from a small RNA-seq data set represent tRFs or degraded fragments. A classification method was then used to annotate the types of tRFs based on their sites of origin in pre-tRNA or mature tRNA. We applied the pipeline to analyze 10 991 data sets from 32 types of cancers and identified thousands of expressed tRFs. A tool called 'tRFinCancer' was developed to facilitate the users to inspect the expression of tRFs across different types of cancers. Another tool called 'tRFBrowser' shows both the sites of origin and the distribution of chemical modification sites in tRFs on their source tRNA. The tRF2Cancer web server is available at http://rna.sysu.edu.cn/tRFfinder/.
Insights
We developed tRF2Cancer, a web tool to identify tRNA-derived small RNA fragments (tRFs) in cancer. This system accurately distinguishes tRFs from degraded fragments, aiding cancer research.
Area of Science:
- Molecular Biology
- Bioinformatics
- Cancer Research
Background:
- tRNA-derived small RNA fragments (tRFs) are small non-coding RNAs with crucial roles in cellular functions and cancer.
- High-throughput sequencing identifies tRFs but struggles to differentiate them from random RNA degradation products.
Purpose of the Study:
- To develop an integrated computational system, tRF2Cancer, for accurate identification and expression analysis of tRFs in cancer.
- To provide tools for users to explore tRF expression and characteristics across various cancer types.
Main Methods:
- Utilized a binomial test to distinguish genuine tRFs from degraded RNA fragments in small RNA sequencing data.
- Developed a classification method to annotate tRF types based on their origin in pre-tRNA or mature tRNA.
- Integrated tools: tRFinCancer for cancer-specific tRF expression analysis and tRFBrowser for visualizing tRF origin and modification sites.
Main Results:
- Analyzed 10,991 cancer datasets across 32 cancer types, identifying thousands of expressed tRFs.
- The tRF2Cancer system accurately identified and classified tRFs, differentiating them from degradation fragments.
- Developed user-friendly web tools for exploring tRF data in cancer.
Conclusions:
- tRF2Cancer provides a robust platform for tRF identification and analysis in cancer genomics.
- The system facilitates deeper understanding of tRF roles in diverse cancer types.
- Enables researchers to investigate tRFs as potential biomarkers or therapeutic targets in cancer.
