Evidence map›Paper›PMID 33596079›Full record

ArticleJournal of proteome research2021

mokapot: Fast and Flexible Semisupervised Learning for Peptide Detection.

William E Fondrie, William S Noble

Open access · hybridAbstract read
In one paragraph

Article in Journal of proteome research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
40citing papers in PubMed, 1 pooled it
5.7field-weighted citation impact, top 2% of its field
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

40 citing papers in PubMed, 1 synthesis or guideline pooled it, 70 citations in OpenAlex.

  1. Guideline
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  14. A haplotype-resolved view of human gene regulation.bioRxiv : the preprint server for biology · 2025
    Article
  15. Open-Source and FAIR Research Software for Proteomics.Journal of proteome research · 2025
    Review
  16. Article
  17. Article
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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

2 authors at 1 institution in 1 country.

William E FondrieDepartment of Genome Sciences, University of Washington, Seattle, Washington 98195, United States.ORCID 0000-0002-1554-3716
William S NobleDepartment of Genome Sciences, University of Washington, Seattle, Washington 98195, United States.ORCID 0000-0001-7283-4715
University of Washington · US

Funding

INTERDISCIPLINARY TRAINING IN GENOMIC SCIENCEST32HG000035 · NHGRI · UNIVERSITY OF WASHINGTON · PI Bruce Colston Trapnell · 1995 to 2026
$24.2M
TrainingP41GM103533 · NIGMS · UNIVERSITY OF WASHINGTON · PI YATES III, JOHN R · 2012 to 2021
$20.7M
Optimization and joint modeling for peptide detection by tandem mass spectrometryR01GM121818 · NIGMS · UNIVERSITY OF WASHINGTON · PI NOBLE, WILLIAM STAFFORD · 2017 to 2020
$1.3M
NHGRI NIH HHS T32 HG000035NIGMS NIH HHS P41 GM103533NIGMS NIH HHS R01 GM121818
6 · The paper itself

Abstract

Proteomics studies rely on the accurate assignment of peptides to the acquired tandem mass spectra-a task where machine learning algorithms have proven invaluable. We describe mokapot, which provides a flexible semisupervised learning algorithm that allows for highly customized analyses. We demonstrate some of the unique features of mokapot by improving the detection of RNA-cross-linked peptides from an analysis of RNA-binding proteins and increasing the consistency of peptide detection in a single-cell proteomics study.

Indexed as

PeptidesProteomicsAlgorithmsDatabases, ProteinTandem Mass SpectrometryPeptidesbioinformaticsconfidence estimationmachine learningpeptide identificationpercolatorproteomicssingle-cell mass spectrometrysupport vector machineSVMtandem mass spectrometry

Identifiers

PMID33596079
PMCPMC8022319
OpenAlexW3131629713

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.