Trans-Proteomic Pipeline¶
Overview¶
The Trans-Proteomic Pipeline provides a complete workflow to generate spectral libraries suitable for OpenSWATH. Data sets acquired in data-dependent acquisition (DDA) or data-independent acquisition (DIA; preprocessed by DIA-Umpire [2]) can be searched by spectrum-centric scoring against a reference FASTA database using several supported search engines. After statistical validation using PeptideProphet, iProphet & MAYU, a consensus spectral library is generated by SpectraST, which serves as final input for the OpenMS tool TargetedFileConverter
.
Contact and Support¶
We provide support separately for the TPP, the msproteomicstools and the OpenMS components of the workflow.
Tutorial¶
A comprehensive tutorial [1] describes the individual steps to generate spectral libraries for SWATH-MS using the Trans-Proteomic Pipeline (TPP). Please follow the tutorial until the generation of SpectraST spectral libraries. If you are using DIA data, follow the DIA-Umpire tutorial to generate pseudo-spectra first, which can then be processed using the TPP.
In the last step, import the SpectraST consensus library (format sptxt
or splib
) and convert it to a MRM
transition list:
# This will generate the file db_assays.mrm
spectrast -cNdb_pqp -cICID-QTOF -cM db_consensus.splib
Convert then the MRM
transition list to a TraML spectral library and follow the remaining steps in the Generic Transition Lists section.
References¶
[1] | Schubert OT, Gillet LC, Collins BC, Navarro P, Rosenberger G, Wolski WE, Lam H, Amodei D, Mallick P, MacLean B, Aebersold R. Building high-quality assay libraries for targeted analysis of SWATH MS data. Nat Protoc. 2015 Mar;10(3):426-41. doi: 10.1038/nprot.2015.015. Epub 2015 Feb 12. PMID: 25675208 |
[2] | Tsou CC, Avtonomov D, Larsen B, Tucholska M, Choi H, Gingras AC, Nesvizhskii AI. DIA-Umpire: comprehensive computational framework for data-independent acquisition proteomics. Nat Methods. 2015 Mar;12(3):258-64, 7 p following 264. doi: 10.1038/nmeth.3255. Epub 2015 Jan 19. PMID: 25599550 |