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Aminoacyl-tRNA synthetases are present in both eukaryotes and bacteria. Though eukaryotes have 20 different aminoacyl-tRNA synthetases to couple to 20 amino acids, many bacteria do not have genes for all of these aminoacyl-tRNA synthetases. Despite this, they still use all 20 amino acids to synthesize their proteins. For instance, some bacteria do not have the gene encoding the enzyme that couples glutamine with its partner tRNA. In these organisms, one enzyme adds glutamic acid to all of the...
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Computational Approaches to tRNA-Derived Small RNAs.

Wei-Lin Xu1, Ye Yang2, Yi-Dan Wang3

  • 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, China. xuwl6@mail2.sysu.edu.cn.

Non-Coding RNA
|April 17, 2018
PubMed
Summary
This summary is machine-generated.

This review covers tools and databases for identifying tRNA-derived small RNAs (tDRs), crucial non-coding RNAs. It aids researchers in selecting optimal bioinformatics resources for tDR analysis.

Keywords:
databaseidentificationnext-generation sequencingtRNA halvestRNA-derived small RNAtRNA-derived small RNA fragments

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Area of Science:

  • Molecular Biology
  • Bioinformatics
  • Genomics

Background:

  • tRNA-derived small RNAs (tDRs), including tRNA halves and tRNA-derived small RNA fragments (tRFs), are emerging non-coding RNAs with significant biological functions.
  • Next-generation sequencing (NGS) technologies generate vast amounts of small RNA data, necessitating specialized tools for tDR analysis.

Purpose of the Study:

  • To review and summarize existing software tools and databases for the identification and collection of tDRs.
  • To assist researchers in selecting appropriate bioinformatics tools for their tDR studies.
  • To stimulate the development and improvement of tDR analysis tools.

Main Methods:

  • Comprehensive literature search for relevant bioinformatics tools and databases.
  • Categorization and description of identified tools and databases based on their functionalities.
  • Analysis of the strengths and weaknesses of different approaches for tDR identification.

Main Results:

  • A curated list of available software and databases for tDR analysis is presented.
  • Key features and applications of various tDR identification tools are discussed.
  • Databases for retrieving and managing tDR data are highlighted.

Conclusions:

  • The availability of specialized tools and databases is critical for advancing tDR research.
  • Informed selection of bioinformatics resources can significantly enhance the efficiency and accuracy of tDR studies.
  • Further development of user-friendly and robust tools is encouraged to support the growing field of tDR biology.