Quantitative mass spectrometry workflow. Currently supports proteomics experiments with complex experimental designs for DDA-LFQ, DDA-Isobaric and DIA-LFQ quantification.
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Updated
Dec 9, 2025 - Nextflow
Quantitative mass spectrometry workflow. Currently supports proteomics experiments with complex experimental designs for DDA-LFQ, DDA-Isobaric and DIA-LFQ quantification.
R-based package for detecting differentially abundant proteins in shotgun mass spectrometry-based proteomic experiments with tandem mass tag (TMT) labeling
An exploration of internal reference scaling (IRS) normalization in isobaric tagging proteomics experiments.
A Comet-based, best practices proteomics pipeline.
Data Reduction System (DRS) for the Thirty Meter Telescope IRIS imager/spectrograph
Examples of TMT data analyses using R. Links to notebooks and repositories. Also a few spectral counting analyses.
Compares PAW and MQ for a 7-channel TMT experiment; compares edgeR to two-sample t-test
Converter from Census TMT output file to the input of MSstatsTMT
An open-source Python package for accurate and sensitive peptide and protein quantification.
Developing mouse lens done with MQ
R analysis of TMT data from Yeast triple knockout strains (Paulo et al., 2016, JASMS, v27, p1620-25)
Yeast TMT data - 3 different carbon sources (from Gygi lab) analyzed with PAW pipeline and MaxQuant
Tandem Mass Tag (TMT) dilution series analysis
Quantitative mass spectrometry workflow. Currently supports proteomics experiments with complex experimental designs for DDA-LFQ, DDA-Isobaric and DIA-LFQ quantification.
Data from Plubell et al., 2017 processed with the PAW pipeline.
Re-analysis of data from childhood acute lymphoblastic leukemia study in Nat. Comm. April 2019
Comparison of SPS MS3 TMT data to MS2 TMT data
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