Evidence map›Paper›PMID 28394336›Full record

ArticleNature methods2017

MSFragger: ultrafast and comprehensive peptide identification in mass spectrometry-based proteomics.

Andy T Kong, Felipe V Leprevost, Dmitry M Avtonomov, Dattatreya Mellacheruvu, Alexey I Nesvizhskii

Abstract read
In one paragraph

Article in Nature methods, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1,424 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1,424citing papers in PubMed, 1 pooled it
–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

1,424 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. An AI-Ready Phosphorylation Meta-Analysis forJournal of proteome research · 2026
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  17. Translation fidelity mechanisms and their changes in aging.bioRxiv : the preprint server for biology · 2026
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  18. Bacterial stress responses lower mRNA-protein level correlations.Proceedings of the National Academy of Sciences of the United States of America · 2026
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  19. Estimating protein isoform abundances with [Formula: see text].Proceedings of the National Academy of Sciences of the United States of America · 2026
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  20. Article

1,364 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

5 authors.

Andy T KongDepartment of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA.
Felipe V LeprevostDepartment of Pathology, University of Michigan, Ann Arbor, Michigan, USA.ORCID http://orcid.org/0000-0002-8228-5374
Dmitry M AvtonomovDepartment of Pathology, University of Michigan, Ann Arbor, Michigan, USA.
Dattatreya MellacheruvuDepartment of Pathology, University of Michigan, Ann Arbor, Michigan, USA.ORCID http://orcid.org/0000-0001-8221-7673
Alexey I NesvizhskiiDepartment of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA.ORCID http://orcid.org/0000-0002-2806-7819

Funding

COMPUTATIONAL TOOLS FOR MASS SPECTROMETRY-BASED INTERACTOME DATAR01GM094231 · NIGMS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Alexey I Nesvizhskii · 2010 to 2026
$5.4M
University of Michigan Proteogenomics Data Analysis CenterU24CA210967 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI CHINNAIYAN, ARUL M, DHANASEKARAN, SARAVANA M · 2016 to 2020
$4.1M
NCI NIH HHS U24 CA210967NIGMS NIH HHS R01 GM094231
6 · The paper itself

Abstract

There is a need to better understand and handle the 'dark matter' of proteomics-the vast diversity of post-translational and chemical modifications that are unaccounted in a typical mass spectrometry-based analysis and thus remain unidentified. We present a fragment-ion indexing method, and its implementation in peptide identification tool MSFragger, that enables a more than 100-fold improvement in speed over most existing proteome database search tools. Using several large proteomic data sets, we demonstrate how MSFragger empowers the open database search concept for comprehensive identification of peptides and all their modified forms, uncovering dramatic differences in modification rates across experimental samples and conditions. We further illustrate its utility using protein-RNA cross-linked peptide data and using affinity purification experiments where we observe, on average, a 300% increase in the number of identified spectra for enriched proteins. We also discuss the benefits of open searching for improved false discovery rate estimation in proteomics.

Indexed as

AlgorithmsComputational BiologyDatabases, ProteinHEK293 CellsHumansPeptide FragmentsProtein Processing, Post-TranslationalProteomeProteomicsTandem Mass SpectrometryPeptide FragmentsProteome

Identifiers

PMID28394336
PMCPMC5409104

What OpenQuestion holds

Textmetadata
LicenceTDM
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.