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Title: PECAN: library-free peptide detection for data-independent acquisition tandem mass spectrometry data

Abstract

Data-independent acquisition (DIA) is an emerging mass spectrometry (MS)-based technique for unbiased and reproducible measurement of protein mixtures. DIA tandem mass spectrometry spectra are often highly multiplexed, containing product ions from multiple cofragmenting precursors. Detecting peptides directly from DIA data is therefore challenging; most DIA data analyses require spectral libraries. Here we present PECECAN (http://pecan.maccosslab.org), a library-free, peptide-centric tool that robustly and accurately detects peptides directly from DIA data. PECECAN reports evidence of detection based on product ion scoring, which enables detection of low-abundance analytes with poor precursor ion signal. We demonstrate the chromatographic peak picking accuracy and peptide detection capability of PECECAN, and we further validate its detection with data-dependent acquisition and targeted analyses. Lastly, we used PECECAN to build a plasma proteome library from DIA data and to query known sequence variants.

Authors:
ORCiD logo; ; ; ; ORCiD logo; ORCiD logo; ORCiD logo
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1411915
Report Number(s):
PNNL-SA-129704
Journal ID: ISSN 1548-7091; KP1601010
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Journal Article
Journal Name:
Nature Methods
Additional Journal Information:
Journal Volume: 14; Journal Issue: 9; Journal ID: ISSN 1548-7091
Publisher:
Nature Publishing Group
Country of Publication:
United States
Language:
English
Subject:
96 KNOWLEDGE MANAGEMENT AND PRESERVATION; Data Acquisition; Proteome Informatics; Proteomic Analysis; Proteomics; Software

Citation Formats

Ting, Ying S., Egertson, Jarrett D., Bollinger, James G., Searle, Brian C., Payne, Samuel H., Noble, William Stafford, and MacCoss, Michael J. PECAN: library-free peptide detection for data-independent acquisition tandem mass spectrometry data. United States: N. p., 2017. Web. doi:10.1038/nmeth.4390.
Ting, Ying S., Egertson, Jarrett D., Bollinger, James G., Searle, Brian C., Payne, Samuel H., Noble, William Stafford, & MacCoss, Michael J. PECAN: library-free peptide detection for data-independent acquisition tandem mass spectrometry data. United States. doi:10.1038/nmeth.4390.
Ting, Ying S., Egertson, Jarrett D., Bollinger, James G., Searle, Brian C., Payne, Samuel H., Noble, William Stafford, and MacCoss, Michael J. Mon . "PECAN: library-free peptide detection for data-independent acquisition tandem mass spectrometry data". United States. doi:10.1038/nmeth.4390.
@article{osti_1411915,
title = {PECAN: library-free peptide detection for data-independent acquisition tandem mass spectrometry data},
author = {Ting, Ying S. and Egertson, Jarrett D. and Bollinger, James G. and Searle, Brian C. and Payne, Samuel H. and Noble, William Stafford and MacCoss, Michael J.},
abstractNote = {Data-independent acquisition (DIA) is an emerging mass spectrometry (MS)-based technique for unbiased and reproducible measurement of protein mixtures. DIA tandem mass spectrometry spectra are often highly multiplexed, containing product ions from multiple cofragmenting precursors. Detecting peptides directly from DIA data is therefore challenging; most DIA data analyses require spectral libraries. Here we present PECECAN (http://pecan.maccosslab.org), a library-free, peptide-centric tool that robustly and accurately detects peptides directly from DIA data. PECECAN reports evidence of detection based on product ion scoring, which enables detection of low-abundance analytes with poor precursor ion signal. We demonstrate the chromatographic peak picking accuracy and peptide detection capability of PECECAN, and we further validate its detection with data-dependent acquisition and targeted analyses. Lastly, we used PECECAN to build a plasma proteome library from DIA data and to query known sequence variants.},
doi = {10.1038/nmeth.4390},
journal = {Nature Methods},
issn = {1548-7091},
number = 9,
volume = 14,
place = {United States},
year = {2017},
month = {8}
}