Analysis of DIA proteomics data using MSFragger-DIA and FragPipe computational platform

Fengchao Yu (), Guo Ci Teo, Andy T. Kong, Klemens Fröhlich, Ginny Xiaohe Li, Vadim Demichev and Alexey I. Nesvizhskii ()
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Fengchao Yu: University of Michigan
Guo Ci Teo: University of Michigan
Andy T. Kong: University of Michigan
Klemens Fröhlich: University of Basel
Ginny Xiaohe Li: University of Michigan
Vadim Demichev: Charité – Universitätsmedizin Berlin
Alexey I. Nesvizhskii: University of Michigan

Nature Communications, 2023, vol. 14, issue 1, 1-14

Abstract: 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.

Date: 2023
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DOI: 10.1038/s41467-023-39869-5

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