Illumina metabarcoding pipeline for fungal ITS

High-throughput metabarcoding studies on fungi and other eukaryotic microorganisms are rapidly becoming more frequent and more complex, requiring researchers to handle ever increasing amounts of raw sequence data. Here we provide a flexible pipeline for pruning and analyzing fungal barcode (ITS rDNA) data generated as paired-end reads on Illumina MiSeq sequencers. The pipeline presented includes specific steps fine-tuned for ITS, that are mostly missing from pipelines developed for prokaryotes. It (i) employs state of the art programs and follows best practices in fungal high-throughput metabarcoding, (ii) consists of modules and scripts easily modifiable by the user to ensure maximum flexibility with regard to specific needs of a project or future methodological developments, (iii) and is straightforward to use, also in classroom settings. We provide detailed descriptions and revision techniques for each step, thus avoiding a black-box approach and giving the user maximum control over data treatment. Employing this pipeline will improve and speed up the tedious and error-prone process of cleaning fungal Illumina metabarcoding data.

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Dataset DOI: doi:10.12761/sgn.2014.2

Data and Resources

Additional Info

Field Value
Geographic coverage
Geographic description Flörsheim, Germany
Bounding coordinates
North: 50.0069
West: 8.3992
East: 8.3992
South: 50.0069
Temporal coverage
Time period
Begin: 2013
End: 2013
Taxonomic coverage
Kingdom Fungi
General taxonomic description Example dataset covering some soil fungal operational taxonomic units (OTUs). The number of OTUs is dependent on the method of delimitation.
Other info
Last Updated March 10, 2021, 15:16 (UTC)
Created December 17, 2020, 15:42 (UTC)

Responsible parties

Creator
Name Miklós Bálint
Organization affiliations
Senckenberg Gesellschaft für Naturforschung

Role

Contact
Name Imke Schmitt
E-mail
Organization affiliations
Senckenberg Gesellschaft für Naturforschung

Associated party
Name Miklós Bálint
Role

Research data management planning

Data will be stored at (long-term archived) Information still missing

Link to this dataset:

https://doi.org/10.12761/sgn.2014.2