The codebase of the OpenMS project

A cross-platform proteomics data analysis suite

Proteomics search & quantification so fast that it feels like magic

mzmine source code repository

Python library for processing (tandem) mass spectrometry data and for computing spectral similarities.

This is the git repository matching the Bioconductor package xcms: LC/MS and GC/MS Data Analysis

A modular, python-based framework for mass spectrometry. Powered by nbdev.

pymzML - an interface between Python and mzML Mass spectrometry Files

Python package for efficient mass spectrometry data processing and visualization

SIRIUS is a software for discovering a landscape of de-novo identification of metabolites using tandem mass spectrometry. This repository contains the code of the SIRIUS Software (GUI and CLI)

Base Classes and Functions for Mass Spectrometry and Proteomics

Ultrafast, comprehensive peptide identification for mass spectrometry–based proteomics

PeptideProphet, PTMProphet, ProteinProphet, iProphet, Abacus, and FDR filtering

Visualization and Analysis of mass spectrometric and chromatographic data.

Official repository of open data MassBank records, with released versions also available from https://doi.org/10.5281/zenodo.3378723

modular & open DIA search

Protein Identification with Deep Learning

Using R and Bioconductor packages for the analysis and comprehension of proteomics data.

An open-source Python package for efficient accession and visualization of Bruker TimsTOF raw data from the Mann Labs at the Max Planck Institute of Biochemistry.

An open-source Python package for the visual annotation of proteomics data with sequence specific knowledge.

LC-MS data processing tool for large-scale metabolomics experiments.

A deep learning toolkit for mass spectrometry

Python Package for the downstream analysis of mass-spectrometry-based proteomics data

Word2Vec based similarity measure of mass spectrometry data.

Workflow solutions for mass-spectrometry based non-target analysis.

Thermo MSFileReader Python bindings

Quantitative mass spectrometry workflow. Currently supports proteomics experiments with complex experimental designs for DDA-LFQ, DDA-Isobaric and DIA-LFQ quantification.

Quantitative Analysis of Mass Spectrometry Data

Data toolkits for processing NMR, MALDI MSI, MALDI single cell, Raman Spectroscopy, LC-MS and GC-MS raw data, chemoinformatics data analysis and data visualization.

Picotti lab data analysis package.