Our Tools & Resources
We are committed to open-source and reproducible science. Here are some of the tools we’ve developed to analyze complex proteomics data.
v0.1ProteoForge
Revisions: March 2026
A framework for identifying quantitatively differential proteoforms (dPFs) from peptide level proteomics data.
v0.2QuEStVar
Updated: July 2025
A framework to enable combined testing to expand the statistical explainability by employing equivalence and difference testing in scalable and rapid manner.
v0.3.1SQuAPP
Published: May 2023
Simple Quantitative Analysis of Proteins and PTMs (SQuAPP) is a workflow-based web application built on R-Shiny to enable rapid high-level analysis of quantitative proteomics data. SQuAPP provides streamlined and straightforward access to many aspects of typical downstream analysis done with quantitative proteomics data. SQuAPP can bring multiple levels of proteomics data to process and visually compare them for further visualizations.
v0.1.0RoLiMviz
Updated: June 2021
A comprehensive platform for the analysis and visualization of data processed with RoLiM.
v0.1.1RoLiM
Updated: March 2025
A tool for fast and accurate deconvolution of linear amino acid motifs in large proteomic datasets.
v4.1TopFIND
Legacy Project
Termini oriented protein Function Inferred Database (TopFIND) is an integrated knowledgebase focused on protein termini, their formation by proteases and functional implications. It contains information about the processing and the processing state of proteins and functional implications thereof derived from research literature, contributions by the scientific community and biological databases.
v4.1TopFINDer
Legacy Project
TopFIND ExploreR retrieves general and position specific information for a list of protein termini as well as protease specific analysis tools. Submit a list of termini (UniProt accession and peptide sequence) and TopFINDer will retrieve known evidence for the termini.
v4.1PathFINDer
Legacy Project
PathFINDer is TopFIND’s protease web explorer inspired by Fortelny et al, PLoS Biology, 2014. PathFINDer uses a graph to model known protease interactions as a network and finds paths in this network. These paths represent biological pathways thus enabling mechanistic insights.