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RNA-seq03:21

RNA-seq

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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
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Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations
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tRFtarget: a database for transfer RNA-derived fragment targets.

Ningshan Li1,2, Nayang Shan2,3, Lingeng Lu4

  • 1SJTU-Yale Joint Center for Biostatistics and Data Science, Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China.

Nucleic Acids Research
|October 9, 2020
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Summary

Transfer RNA-derived fragments (tRFs) are key small non-coding RNAs. The new tRFtarget database predicts tRF interactions with genes, aiding disease mechanism research.

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Area of Science:

  • Molecular Biology
  • Genomics
  • Bioinformatics

Background:

  • Transfer RNA-derived fragments (tRFs) are emerging small non-coding RNAs with significant biological roles.
  • Understanding tRF functions in human diseases requires accurate prediction of their target genes and binding sites.

Purpose of the Study:

  • To develop a comprehensive, publicly accessible web-based database for predicting tRF targets.
  • To provide insights into the molecular mechanisms of tRFs in biological processes and diseases.

Main Methods:

  • Utilized RNAhybrid and IntaRNA for computational prediction of tRF-mRNA interactions.
  • Integrated predicted binding site locations, regions, and binding stability free energy with graphical illustrations.
  • Compiled 936 tRFs and 135,000 predicted targets across eight species.

Main Results:

  • Developed the tRFtarget database (http://trftarget.net) for tRF target prediction.
  • Database includes detailed interaction data, search functionalities (tRF ID or gene symbol), and curated experimental evidence.
  • Facilitated downstream analysis via integration with the DAVID web server for functional and pathway annotation.

Conclusions:

  • The tRFtarget database serves as a valuable resource for researchers investigating tRF functions.
  • Enables experimental validation of predicted tRF-target interactions.
  • Facilitates deeper understanding of tRF roles in molecular mechanisms and human diseases.