1. Data input
A. Import peptide file:
File format: A comma separated file (.csv) where the first column is the peptide sequence, and following columns are intensity values.
Headers should be used. It is helpful to have the conditions of your samples in the header.
File format: The peptide.txt output file from MaxQuant or MetaLab.
B. Add treatment information
2. Check sample names and sample conditions
Note: you can update your sample names here. Condition names are either auto filled or can be typed in. Please use the drop down options for conditions.
3. Analysis options
A. Data Normalization
B. Choose log transformation
C. Choose peptide-to-KEGG database
PCA analysis

Heatmap of enriched functions

About pepFunk, a metaproteomic peptide-centric functional enrichment workflow.
Welcome to pepFunk!
pepFunk allows you to complete a peptide-focused functional enrichment workflow for gut microbiome metaproteomic studies. This workflow uses KEGG annotation for pathway enrichment, alongside Gene Set Variation Analysis (GSVA) adapted for peptide data. By completing analysis on peptides, rather than proteins, we lose less information and retain more statistical power. We curated peptide database specific to human gut microbiome studies for computational speed.
pepFunk allows you to complete a peptide-focused functional enrichment workflow for gut microbiome metaproteomic studies. This workflow uses KEGG annotation for pathway enrichment, alongside Gene Set Variation Analysis (GSVA) adapted for peptide data. By completing analysis on peptides, rather than proteins, we lose less information and retain more statistical power. We curated peptide database specific to human gut microbiome studies for computational speed.
Updates
January 22, 2021
Fixed a bug where manual condition entering would cause an error.
February 22, 2021
Fixed a bug to allow 10 conditions.
Fixed a bug where manual condition entering would cause an error.
February 22, 2021
Fixed a bug to allow 10 conditions.