Evidence map›Paper›PMID 32037841›Full record

ArticleJournal of proteome research2020

Matrix-Matched Calibration Curves for Assessing Analytical Figures of Merit in Quantitative Proteomics.

Lindsay K Pino, Brian C Searle, Han-Yin Yang, Andrew N Hoofnagle, William S Noble, Michael J MacCoss

Abstract read
In one paragraph

Article in Journal of proteome research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.

0numbers the graph read from it
0cells of the map it votes in
38citing 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

38 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Review
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article
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

6 authors.

Lindsay K PinoDepartment of Genome Sciences, University of Washington, Seattle, Washington 98195, United States.ORCID 0000-0003-1857-7222
Brian C SearleDepartment of Genome Sciences, University of Washington, Seattle, Washington 98195, United States.ORCID 0000-0001-8760-6731
Han-Yin YangDepartment of Genome Sciences, University of Washington, Seattle, Washington 98195, United States.
Andrew N HoofnagleDepartment of Laboratory Medicine, University of Washington, Seattle, Washington 98195, United States.
William S NobleDepartment of Genome Sciences, University of Washington, Seattle, Washington 98195, United States.ORCID 0000-0001-7283-4715
Michael J MacCossDepartment of Genome Sciences, University of Washington, Seattle, Washington 98195, United States.ORCID 0000-0003-1853-0256

Funding

TrainingP41GM103533 · NIGMS · UNIVERSITY OF WASHINGTON · PI YATES III, JOHN R · 2012 to 2021
$20.7M
Molecular Phenotyping in Alzheimer's DiseaseRF1AG053959 · NIA · STANFORD UNIVERSITY · PI LARSON, ERIC B, MACCOSS, MICHAEL · 2016 to 2017
$4.1M
Skyline Targeted Proteomics EnvironmentR01GM103551 · NIGMS · UNIVERSITY OF WASHINGTON · PI MACCOSS, MICHAEL · 2012 to 2020
$3.4M
Quantifying proteins in plasma to democratize personalized medicine for patients with type 1 diabetesU01DK121289 · NIDDK · UNIVERSITY OF WASHINGTON · PI HOOFNAGLE, ANDREW N · 2019 to 2021
$1.9M
Optimization and joint modeling for peptide detection by tandem mass spectrometryR01GM121818 · NIGMS · UNIVERSITY OF WASHINGTON · PI NOBLE, WILLIAM STAFFORD · 2017 to 2020
$1.3M
Molecular phenotyping of the yeast replicative life span response to genetic and environmental modulatorsF31AG055257 · NIA · UNIVERSITY OF WASHINGTON · PI PINO, LINDSAY K · 2016 to 2018
$119k
Methods to elucidate quantitative phosphorylation dynamics in the IGF-1 signaling pathwayF31GM119273 · NIGMS · UNIVERSITY OF WASHINGTON · PI SEARLE, BRIAN CHIH-SENG · 2017 to 2018
$57k
NIA NIH HHS F31 AG055257NIA NIH HHS RF1 AG053959NIDDK NIH HHS U01 DK121289NIGMS NIH HHS F31 GM119273NIGMS NIH HHS P41 GM103533NIGMS NIH HHS R01 GM103551NIGMS NIH HHS R01 GM121818
6 · The paper itself

Abstract

Mass spectrometry is a powerful tool for quantifying protein abundance in complex samples. Advances in sample preparation and the development of data-independent acquisition (DIA) mass spectrometry approaches have increased the number of peptides and proteins measured per sample. Here, we present a series of experiments demonstrating how to assess whether a peptide measurement is quantitative by mass spectrometry. Our results demonstrate that increasing the number of detected peptides in a proteomics experiment does not necessarily result in increased numbers of peptides that can be measured quantitatively.

Indexed as

PeptidesProteomicsCalibrationMass SpectrometryProteinsPeptidesProteinscalibration curvefigures of meritlabel-free quantificationmass spectrometryquantitative proteomics

Identifiers

PMID32037841
PMCPMC7175947

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.