Evidence map›Paper›PMID 23863870›Full record

ArticleNature communications2013

Genome-scale proteome quantification by DEEP SEQ mass spectrometry.

Feng Zhou, Yu Lu, Scott B Ficarro, Guillaume Adelmant, Wenyu Jiang, C John Luckey, Jarrod A Marto

Abstract read
In one paragraph

Article in Nature communications, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers.

0numbers the graph read from it
0cells of the map it votes in
52citing 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

52 citing papers in PubMed.

  1. Article
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  13. Proteomic Profiling ofMolecules (Basel, Switzerland) · 2020
    Article
  14. Article
  15. Article
  16. Review
  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

7 authors.

Feng ZhouDepartment of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts 02215-5450, USA.
Yu Lu
Scott B Ficarro
Guillaume Adelmant
Wenyu Jiang
C John Luckey
Jarrod A Marto

Funding

Transcriptional control of inhibitory synapse formationP01NS047572 · NINDS · DANA-FARBER CANCER INSTITUTE · PI STILES, CHARLES D · 2004 to 2013
$13.5M
TRANSFUSION BIOLOGY AND MEDICINET32HL066987 · NHLBI · CHILDREN'S HOSPITAL BOSTON · PI JOHN P MANIS · 2001 to 2026
$10.2M
NHLBI NIH HHS T32 HL066987NHLBI NIH HHS T32HL66987-11NINDS NIH HHS P01 NS047572NINDS NIH HHS P01NS047572
6 · The paper itself

Abstract

Advances in chemistry and massively parallel detection underlie DNA-sequencing platforms that are poised for application in personalized medicine. In stark contrast, systematic generation of protein-level data lags well behind genomics in virtually every aspect: depth of coverage, throughput, ease of sample preparation and experimental time. Here, to bridge this gap, we develop an approach based on simple detergent lysis and single-enzyme digest, extreme, orthogonal separation of peptides and true nanoflow liquid chromatography-tandem mass spectrometry that provides high peak capacity and ionization efficiency. This automated, deep efficient peptide sequencing and quantification mass spectrometry platform provides genome-scale proteome coverage equivalent to RNA-seq ribosomal profiling and accurate quantification for multiplexed isotope labels. In a model of the embryonic to epiblast transition in murine stem cells, we unambiguously quantify 11,352 gene products that span 70% of Swiss-Prot and capture protein regulation across the full detectable range of high-throughput gene expression and protein translation.

Indexed as

AnimalsCell DifferentiationChromatography, LiquidEmbryonic Stem CellsGene Expression Regulation, DevelopmentalGenomeGerm LayersMicePeptide FragmentsProtein BiosynthesisProteomeSequence Analysis, ProteinTandem Mass SpectrometryTrypsinPeptide FragmentsProteomeTrypsin

Identifiers

PMID23863870
PMCPMC3770533

What OpenQuestion holds

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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.