Evidence map›Paper›PMID 26217018›Full record

ArticleMolecular & cellular proteomics : MCP2015

Peptide-Centric Proteome Analysis: An Alternative Strategy for the Analysis of Tandem Mass Spectrometry Data.

Ying S Ting, Jarrett D Egertson, Samuel H Payne, Sangtae Kim, Brendan MacLean, Lukas Käll, Ruedi Aebersold, Richard D Smith, William Stafford Noble, Michael J MacCoss

Abstract readComparative Study
In one paragraph

Article in Molecular & cellular proteomics : MCP, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 85 papers.

0numbers the graph read from it
0cells of the map it votes in
85citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

85 citing papers in PubMed.

  1. Article
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  4. Review
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  10. Carafe enables high qualitybioRxiv : the preprint server for biology · 2025
    Article
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  18. diaTracer enables spectrum-centric analysis of diaPASEF proteomics data.bioRxiv : the preprint server for biology · 2024
    Article
  19. Article
  20. Review

25 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Ying S TingFrom the ‡Department of Genome Sciences, University of Washington, Seattle, Washington;
Jarrett D EgertsonFrom the ‡Department of Genome Sciences, University of Washington, Seattle, Washington;
Samuel H Payne§Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington;
Sangtae Kim§Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington;
Brendan MacLeanFrom the ‡Department of Genome Sciences, University of Washington, Seattle, Washington;
Lukas Käll¶Science for Life Laboratory, Royal Institute of Technology (KTH), Stockholm, Sweden;
Ruedi Aebersold‖Department of Biology, Institute of Molecular Systems Biology, Swiss Federal Institute of Technology (ETH) Zurich, Zurich, Switzerland; ‡‡Faculty of Science, University of Zurich, Zurich, Switzerland.
Richard D Smith§Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington;
William Stafford NobleFrom the ‡Department of Genome Sciences, University of Washington, Seattle, Washington; **Department of Computer Science and Engineering, University of Washington, Seattle, Washington;
Michael J MacCossFrom the ‡Department of Genome Sciences, University of Washington, Seattle, Washington; maccoss@uw.edu.

Funding

Equipment Supplement Request for a Research Resource for Ultra-sensitive High Throughput ProteomicsP41GM103493 · NIGMS · BATTELLE PACIFIC NORTHWEST LABORATORIES · PI SMITH, RICHARD D · 2012 to 2022
$22.6M
TrainingP41GM103533 · NIGMS · UNIVERSITY OF WASHINGTON · PI MACCOSS, MICHAEL · 2012 to 2021
$20.7M
Skyline Targeted Proteomics EnvironmentR01GM103551 · NIGMS · UNIVERSITY OF WASHINGTON · PI MACCOSS, MICHAEL · 2012 to 2020
$3.4M
Machine learning analysis of tandem mass spectraR01GM096306 · NIGMS · UNIVERSITY OF WASHINGTON · PI NOBLE, WILLIAM STAFFORD · 2011 to 2014
$2.5M
Systematic and Comprehensive Sampling of Peptides in Mixtures by Tandem Mass SpectrometryR21CA192983 · NCI · UNIVERSITY OF WASHINGTON · PI MACCOSS, MICHAEL · 2015 to 2017
$648k
Time-Course Proteomics to Uncover the Mechanism and Nature of Aging in C. elegansF31AG037265 · NIA · UNIVERSITY OF WASHINGTON · PI EGERTSON, JARRETT DAVID · 2010 to 2013
$111k
European Research Council 233226NCI NIH HHS R21 CA192983NIA NIH HHS F31 AG037265NIGMS NIH HHS P41 GM103493NIGMS NIH HHS P41 GM103533NIGMS NIH HHS R01 GM096306NIGMS NIH HHS R01 GM103551
6 · The paper itself

Abstract

In mass spectrometry-based bottom-up proteomics, data-independent acquisition is an emerging technique because of its comprehensive and unbiased sampling of precursor ions. However, current data-independent acquisition methods use wide precursor isolation windows, resulting in cofragmentation and complex mixture spectra. Thus, conventional database searching tools that identify peptides by interpreting individual tandem MS spectra are inherently limited in analyzing data-independent acquisition data. Here we discuss an alternative approach, peptide-centric analysis, which tests directly for the presence and absence of query peptides. We discuss how peptide-centric analysis resolves some limitations of traditional spectrum-centric analysis, and we outline the unique characteristics of peptide-centric analysis in general.

Indexed as

Databases, ProteinHumansPeptidesProteomeProteomicsSoftwareTandem Mass SpectrometryPeptidesProteome

Identifiers

PMID26217018
PMCPMC4563716

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.