Evidence map›Paper›PMID 32234965›Full record

ArticleMolecular & cellular proteomics : MCP2020

Selection of Features with Consistent Profiles Improves Relative Protein Quantification in Mass Spectrometry Experiments.

Tsung-Heng Tsai, Meena Choi, Balazs Banfai, Yansheng Liu, Brendan X MacLean, Tom Dunkley, Olga Vitek

Abstract read
In one paragraph

Article in Molecular & cellular proteomics : MCP, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing 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

20 citing papers in PubMed.

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  17. Age-Related Changes in the Matrisome of the Mouse Skeletal Muscle.International journal of molecular sciences · 2021
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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

7 authors.

Tsung-Heng TsaiKhoury College of Computer Sciences, Northeastern University, Boston, Massachusetts.
Meena ChoiKhoury College of Computer Sciences, Northeastern University, Boston, Massachusetts.
Balazs BanfaiRoche Pharmaceutical Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, Basel, Switzerland.
Yansheng LiuDepartment of Pharmacology, Yale Cancer Biology Institute, Yale University School of Medicine, West Haven, Connecticut.
Brendan X MacLeanDepartment of Genome Sciences, University of Washington, Seattle, Washington.
Tom DunkleyRoche Pharmaceutical Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, Basel, Switzerland.
Olga VitekKhoury College of Computer Sciences, Northeastern University, Boston, Massachusetts. Electronic address: o.vitek@northeastern.edu.

Funding

MassIVE.quant: a curated and scalable community resource for quantitative proteomicsR01LM013115 · NLM · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI BANDEIRA, NUNO · 2019 to 2022
$1.3M
National Science Foundation DBI-1759736NLM NIH HHS 1R01LM013115– 01NLM NIH HHS R01 LM013115
6 · The paper itself

Abstract

In bottom-up mass spectrometry-based proteomics, relative protein quantification is often achieved with data-dependent acquisition (DDA), data-independent acquisition (DIA), or selected reaction monitoring (SRM). These workflows quantify proteins by summarizing the abundances of all the spectral features of the protein (

Indexed as

Databases, ProteinMass SpectrometryProtein Processing, Post-TranslationalProteinsProteomicsReproducibility of ResultsSensitivity and SpecificitySoftwareProteinsbioinformaticsbiostatisticscomputational biologylabel-free quantificationmass spectrometrymultiple reaction monitoringquantificationselected reaction monitoringStatisticstargeted mass spectrometry

Identifiers

PMID32234965
PMCPMC7261813

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Registered trials

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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.