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MIST: a Multilocus Identification System for Trichoderma.

Kai Dou1,2,3, Zhixiang Lu1,2,3, Qiong Wu1,4,5

  • 1School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, People's Republic of China.

Applied and Environmental Microbiology
|July 19, 2020
PubMed
Summary

Accurate identification of Trichoderma fungi is crucial for industry and agriculture. This study introduces the online multilocus identification system (MIST) for automated Trichoderma species detection using DNA barcodes, simplifying fungal research.

Keywords:
Trichodermabarcodingspecies identification

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Area of Science:

  • Mycology
  • Bioinformatics
  • Molecular Biology

Background:

  • Accurate identification of industrially and agriculturally relevant fungi, particularly Trichoderma species, is increasingly challenging due to rapid taxonomic expansion.
  • The genus Trichoderma holds significant importance in various sectors, including industry, agriculture, and bioremediation, necessitating precise species identification for effective utilization.
  • Ambiguity in species identification arises from the growing number of unidentified sequences in public databases, hindering Trichoderma-based research.

Purpose of the Study:

  • To develop an automated system for the precise identification of Trichoderma species.
  • To provide researchers with an accessible tool for fungal identification, overcoming taxonomic knowledge limitations.
  • To facilitate research on Trichoderma biodiversity and applications by offering a reliable identification method.

Main Methods:

  • Development of the online multilocus identification system (MIST) for automated detection of 349 Trichoderma species.
  • Utilizing a reference database of validated sequences from three DNA barcodes: nuclear rRNA internal transcribed spacers (ITS), translation elongation factor 1-alpha (tef1), and RNA polymerase subunit 2 (rpb2).
  • Employing an integrated parametric sequence similarity search (blastn) within a decision tree classifier framework for species identification.

Main Results:

  • MIST successfully utilizes combined information from multiple DNA barcodes for Trichoderma species identification.
  • Verification demonstrated MIST's capability for correct identification of 44 Trichoderma species using tef1 and rpb2 markers.
  • The system provides automated species identification and facilitates retrieval of sequences for manual phylogenetic analysis.

Conclusions:

  • MIST offers an efficient and automated solution for Trichoderma species identification, addressing a critical need in mycology and applied microbiology.
  • The developed system simplifies the identification process, making it accessible to researchers lacking specialized taxonomic expertise.
  • This tool is expected to significantly advance research into the biodiversity and diverse applications of the Trichoderma genus.