Evidence map›Paper›PMID 37438352›Full record

ArticleNature communications2023

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, Alexey I Nesvizhskii

Abstract read
In one paragraph

Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 206 papers.

0numbers the graph read from it
0cells of the map it votes in
206citing 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

206 citing papers in PubMed.

  1. Trial
  2. Preprocessing Large-Scale Proteomics Data.Methods in molecular biology (Clifton, N.J.) · 2027
    Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. A Spatiotemporal Atlas of the Androgen Receptor Proximal Interactome.bioRxiv : the preprint server for biology · 2026
    Article
  11. Article
  12. Article
  13. Article
  14. Review
  15. Biodegradation of components from an oxidized polyethylene by aApplied and environmental microbiology · 2026
    Article
  16. Article
  17. Article
  18. bioRxiv : the preprint server for biology · 2026
    Article
  19. Review
  20. Article

146 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

7 authors.

Fengchao YuDepartment of Pathology, University of Michigan, Ann Arbor, MI, USA. yufe@umich.edu.ORCID http://orcid.org/0000-0002-7695-3698
Guo Ci TeoDepartment of Pathology, University of Michigan, Ann Arbor, MI, USA.ORCID http://orcid.org/0000-0003-3212-4051
Andy T KongDepartment of Pathology, University of Michigan, Ann Arbor, MI, USA.
Klemens FröhlichProteomics Core Facility, Biozentrum, University of Basel, Basel, Switzerland.ORCID http://orcid.org/0000-0003-1445-0750
Ginny Xiaohe LiDepartment of Pathology, University of Michigan, Ann Arbor, MI, USA.
Vadim DemichevDepartment of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany.
Alexey I NesvizhskiiDepartment of Pathology, University of Michigan, Ann Arbor, MI, USA. nesvi@med.umich.edu.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
Michigan Center for Translational Cancer Proteogenomics-Diversity SupplementU24CA271037 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Saravana Mohan Dhanasekaran, Alexey I Nesvizhskii · 2022 to 2026
$4.4M
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 CA210967NCI NIH HHS U24 CA271037NIGMS NIH HHS R01 GM094231
6 · The paper itself

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.

Indexed as

ProteomicsTandem Mass SpectrometryChromatography, LiquidDatabases, FactualReproducibility of Results

Identifiers

PMID37438352
PMCPMC10338508

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.