scientific-proteomics-mass-spectrometry

To analyze LC-MS/MS proteomics data for differential protein expression, you can use an end-to-end pipeline that automates spectral preprocessing, peptide identification, and protein quantification to output differential results and figures.

Yes, you can quantify proteins using LFQ, TMT, SILAC, and iBAQ methods within a single mass spectrometry workflow that processes LC-MS/MS data to generate standard feature tables and quantifications.

To map post-translational modifications (PTMs) from mass spectrometry data, the workflow identifies modified peptides during spectral preprocessing and outputs the mapped PTM sites alongside differential protein expression results.

Spectral networking and peptide identification require PyOpenMS, matchms, pandas, numpy, scipy, and networkx to process mass spectrometry data and output GraphML files for molecular networking.

Yes, this proteomics pipeline supports GNPS-like molecular networking for compound annotation by calculating spectral similarity scores and generating network graphs for expanded interpretation of mass spectrometry data.

An automated LC-MS/MS data analysis pipeline generates standard output formats including CSVs for feature tables and quantifications, GraphML for spectral networks, and figures for visualizing differential protein expression.