4.8 Article

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

期刊

NATURE COMMUNICATIONS
卷 14, 期 1, 页码 -

出版社

NATURE PORTFOLIO
DOI: 10.1038/s41467-023-39869-5

关键词

-

向作者/读者索取更多资源

MSFragger-DIA is a fast and sensitive tool for direct peptide identification from DIA spectra, demonstrating excellent performance in applications such as large-scale tumor studies and single-cell proteomics.
DIA-MS has emerged as a widely used technological platform for quantitative protein profiling. Here, the authors develop MSFragger-DIA, a robust and fast tool to directly identify peptides from DIA spectra. It demonstrates excellent performance across applications from large-scale tumor studies to single-cell proteomics. 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.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.8
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据