FunVIP: Fungal Validation and Identification Pipeline based on phylogenetic analysis

Area of Science:

• Mycology
• Bioinformatics
• Computational Biology

Background:

• DNA sequence data is vital for fungal identification, but public databases contain significant mislabeled sequences.
• Mislabeled data leads to frequent misidentifications, impacting industrial, clinical, and edible fungi research.
• Current identification pipelines necessitate separate validation steps for database-derived datasets.

Purpose of the Study:

• To develop FunVIP, a fully automated, phylogeny-based pipeline for fungal validation and identification.
• To address the challenge of mislabeled sequences in public nucleotide databases.
• To provide a user-friendly tool for accurate and validated fungal identification.

Main Methods:

• FunVIP employs a nine-step automated workflow: input management, sequence organization, alignment, trimming, concatenation, model selection, tree inference, tree interpretation, and report generation.
• The pipeline integrates phylogeny-based identification with automated validation.
• Performance was validated by re-revising the fungal genus Fuscoporia and comparing with BLAST and q2-feature-classifier using fungal datasets.

Main Results:

• FunVIP successfully identified and validated fungal sequences, demonstrating high accuracy.
• The pipeline generated identification results, phylogenetic tree evidence, and conflict reports.
• Comparative analysis showed FunVIP's superior performance against existing methods, especially with mislabeled data.

Conclusions:

• FunVIP is a highly promising tool for achieving easy and accurate fungal identification.
• Its automatic validation capability significantly improves the reliability of fungal identification pipelines.
• FunVIP enhances data integrity by detecting and reporting issues within sequence datasets.