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PendoTMBase: A Database for Plant Endogenous Target Mimics.

Jiacong Deng1,2, Qingyun Li3, Likun Huang4

  • 1College of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou, 350002, China.

Interdisciplinary Sciences, Computational Life Sciences
|September 30, 2020
PubMed
Summary

Researchers identified 2669 plant endogenous target mimics (eTMs) across 43 species, creating the PendoTMBase database. This resource aids genetic studies and plant breeding by providing eTM data and prediction tools.

Keywords:
DatabasePlantTarget mimicsmicroRNA

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Area of Science:

  • Plant genomics
  • Bioinformatics
  • Molecular biology

Background:

  • Rapid advancements in next-generation sequencing generate vast plant genome and transcriptome data.
  • Mature microRNA (miRNA) sequence information is available in databases like miRBase.
  • Endogenous target mimics (eTMs) play regulatory roles by interacting with miRNAs.

Purpose of the Study:

  • To identify and catalog plant endogenous target mimics (eTMs).
  • To develop a specialized web-based database, PendoTMBase, for eTM information.
  • To provide tools for predicting novel eTMs and facilitate plant genetic studies.

Main Methods:

  • Utilized mature miRNA sequences from miRBase (release 21) to search for seed-matched target sites in plant genomes.
  • Identified 2669 non-redundant eTM records across 43 plant species.
  • Developed a web-based platform (PendoTMBase) to store and present eTM data, including predicted structures, expression levels, and Gene Ontology (GO) annotations.

Main Results:

  • A comprehensive database, PendoTMBase, containing 2669 non-redundant eTM records from 43 plant species was established.
  • Detailed information on predicted pairing structures between eTMs and their target miRNAs is available.
  • In silico evaluations confirmed the representativeness and utility of identified eTMs for genetic studies and plant breeding.

Conclusions:

  • The PendoTMBase database provides a valuable resource for plant molecular biology research.
  • Identified eTMs are suitable for genetic studies and applications in high value-added plant breeding.
  • The platform's novel eTM prediction function supports ongoing research and development in plant science.