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A Comprehensive, Automatically Updated Fungal ITS Sequence Dataset for Reference-Based Chimera Control in
R Henrik Nilsson1, Leho Tedersoo, Martin Ryberg
1Department of Biological and Environmental Sciences, University of Gothenburg.
Microbes and Environments
|March 20, 2015
Summary
A new reference dataset for fungal internal transcribed spacer (ITS) sequences aids in detecting chimeric DNA, improving fungal identification accuracy in molecular ecology. This resource enhances the reliability of environmental sequencing data.
Area of Science:
- Mycology
- Molecular Ecology
- Bioinformatics
Background:
- The nuclear ribosomal internal transcribed spacer (ITS) region is a key genetic marker for fungal identification.
- Chimeric DNA sequences, formed during PCR or assembly, pose a significant challenge in molecular studies.
- Existing chimera detection tools often require chimera-free reference datasets, which are lacking for fungal ITS.
Purpose of the Study:
- To develop a comprehensive, automatically updated reference dataset for fungal ITS sequences.
- To support accurate chimera detection across the fungal kingdom for both full-length and partial ITS datasets.
- To improve the quality of fungal molecular identification in environmental sequencing.
Main Methods:
- Leveraged the UNITE database to construct a fungal ITS reference dataset.
- Implemented automated updating mechanisms for the dataset.
- Validated the dataset's performance using artificial chimeras.
Main Results:
- Achieved over 99.5% performance in detecting artificial chimeras.
- Successfully identified and flagged nearly 1,000 compromised fungal ITS sequences for removal.
- The dataset supports chimera detection for full-length and partial ITS sequences (ITS1 or ITS2).
Conclusions:
- The developed UNITE-based reference dataset significantly enhances chimera detection for fungal ITS sequences.
- This resource improves the accuracy and reliability of fungal molecular identification in ecological studies.
- The dataset is publicly available and supports ongoing third-party curation for continuous improvement.
