Automated Software for Identifying Fungal Sequences
Author Information
Author(s): Nilsson R Henrik, Bok Gunilla, Ryberg Martin, Kristiansson Erik, Hallenberg Nils
Primary Institution: University of Gothenburg
Hypothesis
Can a software pipeline automate the identification of fungal ITS sequences to improve processing speed and accuracy?
Conclusion
The software effectively assigns large sets of fungal sequences to their best matches in databases, improving processing speed and resolution.
Supporting Evidence
- The pipeline processes fungal sequences significantly faster than traditional methods.
- 98% of the identified fungi belonged to the Ascomycota phylum.
- The software can be adapted for other molecular regions and organism groups.
Takeaway
This study created a computer program that helps scientists quickly identify different types of fungi from samples, making it easier to understand the fungi living in our environment.
Methodology
The pipeline uses Perl and integrates NCBI-BLAST for similarity searches, processing 350 fungal ITS sequences from environmental samples.
Potential Biases
The reliance on public databases may introduce bias due to misidentifications and lack of comprehensive taxonomic coverage.
Limitations
The pipeline's effectiveness is limited by the incomplete and sometimes erroneous annotations in public sequence databases.
Participant Demographics
Fungal samples were collected from various building materials in Sweden.
Digital Object Identifier (DOI)
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