Trans-Proteomic Pipeline: Robust Mass Spectrometry-Based Proteomics Data Analysis Suite

Abstract

The Trans-Proteomic Pipeline (TPP) mass spectrometry data analysis suite has been in continual development and refinement since its first tools, PeptideProphet and ProteinProphet, were published 20 years ago. The current release provides a large complement of tools for spectrum processing, spectrum searching, search validation, abundance computation, protein inference, and more. Many of the tools include machine-learning modeling to extract the most information from data sets and build robust statistical models to compute the probabilities that derived information is correct. Here we present the latest information on the many TPP tools, and how TPP can be deployed on various platforms from personal Windows laptops to Linux clusters and expansive cloud computing environments. We describe tutorials on how to use TPP in a variety of ways and describe synergistic projects that leverage TPP. We conclude with plans for continued development of TPP.

Database Creator for Mass Analysis of Peptides and Proteins, DC-MAPP: A Standalone Tool for Simplifying Manual Analysis of Mass Spectral Data to Identify Peptide/Protein Sequences

“…Moreover, these two web tools do not show the m/z values of multiply charged fragment ions that can arise from ESI-MS/MS data. In the case of other popular standalone tools such as TPP, 12 MaxQuant, 14 and MSFragger, 16 it is not possible to view the back-end generated databases, although they have advanced features. Further, other computational tools such as comet, 9 pyteomics, 22 and spectrum_utils, 23 can create custom databases by in silico methods, but these tools can be handled by only those people who have sufficiently good knowledge of computer programming languages.…”

“…Thus far, many software programs have been developed and widely used for the well-established bottom-up proteomic (BUP) approach . Certain popular database search engines for BUP are SEQUEST, MASCOT, Comet, Andromeda, and Morpheus, − and some commonly available standalone tools are trans-proteomic pipeline (TPP), OpenMS, MaxQuant, MS-GF+ MSFragger, and PatternLab V. − Similarly, softwares have also been developed for the top-down proteomics (TDP) (). For the approaches involved in middle-down proteomics (MDP), only a few softwares such as YADA, XDIA, isoScale, and Histone coder () are available especially for histone and antibody characterization. − …”

Does the Ubiquitination Degradation Pathway Really Reach inside of the Chloroplast? A Re-Evaluation of Mass Spectrometry-Based Assignments of Ubiquitination

“…The raw data files in PXD031388 self-identify the mass spectrometer as a “Q Exactive” (Thermo Fisher Scientific) and the data in PXD031468 self-identify as a “Q Exactive HF” (Thermo Fisher Scientific) and a timsTOF (Bruker) because this information is embedded in the raw files (this information is more reliable than the written information provided in papers or ProteomeXchange as is also the case for Sun et alsee Table ). These raw files were then converted into mzML using ThermoRawFileParser and processed using MSFragger 3.2 and the Trans-Proteomic Pipeline (TPP) software suite version 6.2.0 . In the following sections, we discuss the MS-based experiments and results presented in Sun et al and compare that with the results we obtained from reprocessing these raw mass spectra.…”

“…Nevertheless, we can conclude that MS is a very powerful tool to detect ubiquitination and indeed several large scale ubiquitination studies have been published for Arabidopsis − as well as many non-plant species. ,− However, as in all large-scale “omics” studies, even low false discovery rates can result in many false positive observations especially for PTMs, and a critical evaluation of the MS results is therefore needed. Because the original raw MS data for many protein MS-based publications are available through ProteomeXchange (), it is possible to re-evaluate reported results by re-searching these raw MS data. , However, because many of these data sets are large and because of the need for specific expertise, many readers are not in the position to explore the raw data by themselves. Initiatives such as PeptideAtlas are therefore important to provide access to reanalysis of published MS data sets as is available for A.…”

WOMBAT-P: Benchmarking Label-Free Proteomics Data Analysis Workflows

“…In the third workflow (Proline workflow), we used SearchGUI 24 , Proline 25 , and PolySTest 26 . Finally, we also included a workflow using tools from the TPP 27 , specifically PeptideProphet 29 , ProteinProphet and StPeter 30 with Comet 31 for database search and ROTS 28 for statistical analysis (TPP workflow). Multiple modules were written in Python and R scripts to facilitate conversion and parametrization within the workflows.…”