Evidence map›Paper›PMID 37500632›Full record

ArticleNature communications2023

MSBooster: improving peptide identification rates using deep learning-based features.

Kevin L Yang, Fengchao Yu, Guo Ci Teo, Kai Li, Vadim Demichev, Markus Ralser, 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 149 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
149citing 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

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

  1. Pooled it
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  6. bioRxiv : the preprint server for biology · 2026
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  18. SLC16A6 is a tyrosine transporter for the melanosome.bioRxiv : the preprint server for biology · 2026
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89 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.

Kevin L YangDepartment of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.ORCID 0000-0002-8569-4579
Fengchao YuDepartment of Pathology, University of Michigan, Ann Arbor, MI, USA. yufe@umich.edu.ORCID 0000-0002-7695-3698
Guo Ci TeoDepartment of Pathology, University of Michigan, Ann Arbor, MI, USA.ORCID 0000-0003-3212-4051
Kai LiDepartment of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
Vadim DemichevDepartment of Biochemistry, Charité Universitätsmedizin, Berlin, Germany.
Markus RalserDepartment of Biochemistry, Charité Universitätsmedizin, Berlin, Germany.ORCID 0000-0001-9535-7413
Alexey I NesvizhskiiDepartment of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA. nesvi@med.umich.edu.ORCID 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
Proteogenomics of Cancer Training ProgramT32CA140044 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI RAO, ARVIND, SARTOR, MAUREEN AGNES · 2010 to 2024
$3.9M
NCI NIH HHS T32 CA140044NCI NIH HHS U24 CA210967NCI NIH HHS U24 CA271037NIGMS NIH HHS R01 GM094231
6 · The paper itself

Abstract

Peptide identification in liquid chromatography-tandem mass spectrometry (LC-MS/MS) experiments relies on computational algorithms for matching acquired MS/MS spectra against sequences of candidate peptides using database search tools, such as MSFragger. Here, we present a new tool, MSBooster, for rescoring peptide-to-spectrum matches using additional features incorporating deep learning-based predictions of peptide properties, such as LC retention time, ion mobility, and MS/MS spectra. We demonstrate the utility of MSBooster, in tandem with MSFragger and Percolator, in several different workflows, including nonspecific searches (immunopeptidomics), direct identification of peptides from data independent acquisition data, single-cell proteomics, and data generated on an ion mobility separation-enabled timsTOF MS platform. MSBooster is fast, robust, and fully integrated into the widely used FragPipe computational platform.

Indexed as

Deep LearningTandem Mass SpectrometryAlgorithmsChromatography, LiquidDatabases, ProteinPeptidesPeptides

Identifiers

PMID37500632
PMCPMC10374903

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.