Welcome to FungiFun3


What is ORA?

Requires: gene IDs

What is GSEA?

Requires: gene IDs & associated ranked numerical values (e.g. log fold-changes or fold-changes)

Overrepresentation analysis

Select species


Summary

Note: In case of selecting no p-value adjustment, the column 'adjusted p-value' has the same value as the column 'p-value'


Download XLSX

Visualizations

Gene Set Enrichment Analysis (GSEA)

Select species



Summary

Note: In case of selecting no p-value adjustment, the column 'Adjusted p-value' has the same value as the column 'p-value'


Download XLSX

Visualizations

Frequently Asked Questions

What is FungiFun3?

FungiFun3 is a user-friendly web application for overrepresentation or gene set enrichment analysis of fungal genes and proteins offering interactive tables, charts and figures, which users can directly manipulate to their needs.

Which ontologies are provided?

FungiFun3 supports annotations based on Gene Ontology (GO) (Ashburner et al., 2000) Kyoto Encyclopedia of Genes and Genomes (KEGG) (Kanehisa et al., 2000) , and the Functional Catalogue (FunCat) (Ruepp et al., 2004) .

What data resources are used?

GO annotations are provided by collecting information from the European Bioinformatics Institute (EBI) (Huntley et al., 2015) , the FungiDB database (Basenko et al., 2018) , and UniProt (Bateman et al., 2023) . KEGG gene-to-pathway annotations are collected using the KEGG REST API. FunCat is provided as legacy support, as the associated database, PEDANT, is offline and annotations are not updated anymore.

Which IDs are supported?

For every supported strain and ontology the type and format of the supported IDs are provided under the link “Show example IDs” in the “Select input” form.

How many species are supported?

As of 2024, 1,287 organisms in total are supported. These include two host systems - human and mouse - as well as 1,285 fungal strains.

What is Overrepresentation Analysis (ORA)?

Overrepresentation analysis (ORA) (Chicco et al., 2022) is a bioinformatics technique to determine which gene sets (e.g. pathways, biological processes) are significantly enriched in a list of genes of interest compared to a background set. In FungiFun3, you can input a list of gene IDs (either by typing or by Excel/XLSX file) corresponding to your selected species and ontology, or you can simply click the button “Load random IDs” to generate 100 random gene IDs for your selected species and ontology. The application grants the user granular control over background set composition, statistical test, direction of representation (over or under representation), p-value threshold, type of multiple hypothesis test, and type of annotation. The application will then calculate the enrichment of each gene set and return the significant ones. Results are presented in both tabular and graphical form.

What is Gene Set Enrichment Analysis (GSEA)?

Gene Set Enrichment Analysis (GSEA) (Subramanian et al., 2005) is a knowledge-based approach for interpreting genome-wide expression profiles. By focusing on gene sets and utilizing prior biological knowledge, GSEA provides insights into the collective behavior of genes and helps uncover biological mechanisms underlying different phenotypes or conditions. In FungiFun3, you can perform GSEA by selecting a species, an ontology (KEGG, GO, or FunCat), and inputting (Excel/XLSX file) a list of gene IDs with their associated fold-changes or log fold-changes. The analysis will then identify gene sets that are overrepresented at the top or bottom of the ranked gene list, indicating their association with a biological pathway. Users can customize the analysis by setting the p-value cutoff, the minimum and maximum gene set size, and the type of enrichment and type of annotation. Results are presented in both tabular and graphical form.

Additional description of GSEA parameters

  • Minimum/maximum gene set size: minimal/maximal size of a gene set to test. All pathways below/above the threshold are excluded.

  • Test type: this parameter defines the GSEA score type.

    • 'two-tailed': the enrichment score is computed according to the original GSEA description.

    • 'one-tailed-positive/negative': these score types are intended to be used for one-tailed tests, that is, only positive or negative enrichment is of interest.

  • P-value precision: this parameter sets the boundary for calculating the p value.

  • GSEA weighting: GSEA weight parameter. Adjusts displayed statistic values, values closer to 0 flatten plots. Default = 1 (suggested), value of 0.5 is appropriate too, 0 is unweighted.

Further description are given in Korotkevich et al., 2021 and at the Bioconductor documentation of the fgsea package.

Imprint

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Cite us

  • FungiFun3:
    Up to come.
  • FungiFun2:
    Steffen Priebe, Christian Kreisel, Fabian Horn, Reinhard Guthke, Jörg Linde, FungiFun2: a comprehensive online resource for systematic analysis of gene lists from fungal species, Bioinformatics, Volume 31, Issue 3, February 2015, Pages 445–446, https://doi.org/10.1093/bioinformatics/btu627