Analysis of DIA Proteomics Data Using MSFragger-DIA and FragPipe Computational Platform

Overview

Journal Nat Commun

Specialty Biology

Date 2023 Jul 12

PMID 37438352

Authors

Fengchao Yu

Guo Ci Teo

Andy T Kong

Klemens Frohlich

Ginny Xiaohe Li

Vadim Demichev

Alexey I Nesvizhskii

Affiliations

Soon will be listed here.

Abstract

Liquid chromatography (LC) coupled with data-independent acquisition (DIA) mass spectrometry (MS) has been increasingly used in quantitative proteomics studies. Here, we present a fast and sensitive approach for direct peptide identification from DIA data, MSFragger-DIA, which leverages the unmatched speed of the fragment ion indexing-based search engine MSFragger. Different from most existing methods, MSFragger-DIA conducts a database search of the DIA tandem mass (MS/MS) spectra prior to spectral feature detection and peak tracing across the LC dimension. To streamline the analysis of DIA data and enable easy reproducibility, we integrate MSFragger-DIA into the FragPipe computational platform for seamless support of peptide identification and spectral library building from DIA, data-dependent acquisition (DDA), or both data types combined. We compare MSFragger-DIA with other DIA tools, such as DIA-Umpire based workflow in FragPipe, Spectronaut, DIA-NN library-free, and MaxDIA. We demonstrate the fast, sensitive, and accurate performance of MSFragger-DIA across a variety of sample types and data acquisition schemes, including single-cell proteomics, phosphoproteomics, and large-scale tumor proteome profiling studies.

Citing Articles

Integration of proteomics profiling data to facilitate discovery of cancer neoantigens: a survey.

Luo S, Peng H, Shi Y, Cai J, Zhang S, Shao N Brief Bioinform. 2025; 26(2).

PMID: 40052441 PMC: 11886573. DOI: 10.1093/bib/bbaf087.

Mapping the nanoscale organization of the human cell surface proteome reveals new functional associations and surface antigen clusters.

Floyd B, Schmidt E, Till N, Yang J, Liao P, George B bioRxiv. 2025; .

PMID: 40027624 PMC: 11870420. DOI: 10.1101/2025.02.12.637979.

A Scalable, Web-Based Platform for Proteomics Data Processing, Result Storage and Analysis.

Schneider M, Zolg D, Samaras P, Ben Fredj S, Bold D, Guevende A J Proteome Res. 2025; 24(3):1241-1249.

PMID: 39982847 PMC: 11894649. DOI: 10.1021/acs.jproteome.4c00871.

Phosphorylation-dependent WRN-RPA interaction promotes recovery of stalled forks at secondary DNA structure.

Noto A, Valenzisi P, Di Feo F, Fratini F, Kulikowicz T, Sommers J Nat Commun. 2025; 16(1):997.

PMID: 39870632 PMC: 11772831. DOI: 10.1038/s41467-025-55958-z.

─ A User-Friendly Command-Line Tool Simplifying Differential Expression Analysis in Quantitative Proteomics.

Wolski W, Grossmann J, Schwarz L, Leary P, Turker C, Nanni P J Proteome Res. 2025; 24(2):955-965.

PMID: 39849819 PMC: 11812002. DOI: 10.1021/acs.jproteome.4c00911.

References

Yang K, Yu F, Teo G, Li K, Demichev V, Ralser M . MSBooster: improving peptide identification rates using deep learning-based features. Nat Commun. 2023; 14(1):4539. PMC: 10374903. DOI: 10.1038/s41467-023-40129-9. View

Yang Y, Liu X, Shen C, Lin Y, Yang P, Qiao L . In silico spectral libraries by deep learning facilitate data-independent acquisition proteomics. Nat Commun. 2020; 11(1):146. PMC: 6952453. DOI: 10.1038/s41467-019-13866-z. View

Ting Y, Egertson J, Bollinger J, Searle B, Payne S, Noble W . PECAN: library-free peptide detection for data-independent acquisition tandem mass spectrometry data. Nat Methods. 2017; 14(9):903-908. PMC: 5578911. DOI: 10.1038/nmeth.4390. View

Kall L, Canterbury J, Weston J, Noble W, MacCoss M . Semi-supervised learning for peptide identification from shotgun proteomics datasets. Nat Methods. 2007; 4(11):923-5. DOI: 10.1038/nmeth1113. View

Tsai T, Choi M, Banfai B, Liu Y, MacLean B, Dunkley T . Selection of Features with Consistent Profiles Improves Relative Protein Quantification in Mass Spectrometry Experiments. Mol Cell Proteomics. 2020; 19(6):944-959. PMC: 7261813. DOI: 10.1074/mcp.RA119.001792. View

Amodei D, Egertson J, MacLean B, Johnson R, Merrihew G, Keller A . Improving Precursor Selectivity in Data-Independent Acquisition Using Overlapping Windows. J Am Soc Mass Spectrom. 2019; 30(4):669-684. PMC: 6445824. DOI: 10.1007/s13361-018-2122-8. View

Pham T, Henneman A, Jimenez C . iq: an R package to estimate relative protein abundances from ion quantification in DIA-MS-based proteomics. Bioinformatics. 2020; 36(8):2611-2613. PMC: 7178409. DOI: 10.1093/bioinformatics/btz961. View

Teo G, Kim S, Tsou C, Collins B, Gingras A, Nesvizhskii A . mapDIA: Preprocessing and statistical analysis of quantitative proteomics data from data independent acquisition mass spectrometry. J Proteomics. 2015; 129:108-120. PMC: 4630088. DOI: 10.1016/j.jprot.2015.09.013. View

Veiga Leprevost F, Haynes S, Avtonomov D, Chang H, Shanmugam A, Mellacheruvu D . Philosopher: a versatile toolkit for shotgun proteomics data analysis. Nat Methods. 2020; 17(9):869-870. PMC: 7509848. DOI: 10.1038/s41592-020-0912-y. View

10.

Kong A, Leprevost F, Avtonomov D, Mellacheruvu D, Nesvizhskii A . MSFragger: ultrafast and comprehensive peptide identification in mass spectrometry-based proteomics. Nat Methods. 2017; 14(5):513-520. PMC: 5409104. DOI: 10.1038/nmeth.4256. View

11.

Rost H, Rosenberger G, Navarro P, Gillet L, Miladinovic S, Schubert O . OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data. Nat Biotechnol. 2014; 32(3):219-23. DOI: 10.1038/nbt.2841. View

12.

Skowronek P, Krohs F, Lubeck M, Wallmann G, Itang E, Koval P . Synchro-PASEF Allows Precursor-Specific Fragment Ion Extraction and Interference Removal in Data-Independent Acquisition. Mol Cell Proteomics. 2022; 22(2):100489. PMC: 9868879. DOI: 10.1016/j.mcpro.2022.100489. View

13.

Chambers M, MacLean B, Burke R, Amodei D, Ruderman D, Neumann S . A cross-platform toolkit for mass spectrometry and proteomics. Nat Biotechnol. 2012; 30(10):918-20. PMC: 3471674. DOI: 10.1038/nbt.2377. View

14.

Bekker-Jensen D, Bernhardt O, Hogrebe A, Martinez-Val A, Verbeke L, Gandhi T . Rapid and site-specific deep phosphoproteome profiling by data-independent acquisition without the need for spectral libraries. Nat Commun. 2020; 11(1):787. PMC: 7005859. DOI: 10.1038/s41467-020-14609-1. View

15.

Searle B, Swearingen K, Barnes C, Schmidt T, Gessulat S, Kuster B . Generating high quality libraries for DIA MS with empirically corrected peptide predictions. Nat Commun. 2020; 11(1):1548. PMC: 7096433. DOI: 10.1038/s41467-020-15346-1. View

16.

Yu F, Teo G, Kong A, Haynes S, Avtonomov D, Geiszler D . Identification of modified peptides using localization-aware open search. Nat Commun. 2020; 11(1):4065. PMC: 7426425. DOI: 10.1038/s41467-020-17921-y. View

17.

Keller A, Nesvizhskii A, Kolker E, Aebersold R . Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search. Anal Chem. 2002; 74(20):5383-92. DOI: 10.1021/ac025747h. View

18.

Li K, Vaudel M, Zhang B, Ren Y, Wen B . PDV: an integrative proteomics data viewer. Bioinformatics. 2018; 35(7):1249-1251. PMC: 6821182. DOI: 10.1093/bioinformatics/bty770. View

19.

Kitata R, Choong W, Tsai C, Lin P, Chen B, Chang Y . A data-independent acquisition-based global phosphoproteomics system enables deep profiling. Nat Commun. 2021; 12(1):2539. PMC: 8099862. DOI: 10.1038/s41467-021-22759-z. View

20.

Gotti C, Roux-Dalvai F, Joly-Beauparlant C, Mangnier L, Leclercq M, Droit A . Extensive and Accurate Benchmarking of DIA Acquisition Methods and Software Tools Using a Complex Proteomic Standard. J Proteome Res. 2021; 20(10):4801-4814. DOI: 10.1021/acs.jproteome.1c00490. View