Evidence map›Paper›PMID 26873251›Full record

ReviewMolecular & cellular proteomics : MCP2016

A Biologist's Field Guide to Multiplexed Quantitative Proteomics.

Corey E Bakalarski, Donald S Kirkpatrick

Abstract readReview
In one paragraph

Review in Molecular & cellular proteomics : MCP, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing 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

18 citing papers in PubMed.

  1. Article
  2. Review
  3. Perspectives of Proteomics in Respiratory Allergic Diseases.International journal of molecular sciences · 2023
    Review
  4. Review
  5. Article
  6. Review
  7. Proteomic Profiling of IgG1 Producing CHO Cells Using LC/LC-SPS-MSFrontiers in bioengineering and biotechnology · 2021
    Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Review
  13. Proteomics of nucleocytoplasmic partitioning.Current opinion in chemical biology · 2019
    Review
  14. Article
  15. Article
  16. Review
  17. Article
  18. Review
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.

Corey E BakalarskiFrom the Departments of ‡Protein Chemistry and §Bioinformatics and Computational Biology, Genentech, Inc., South San Francisco, California 94080.
Donald S KirkpatrickFrom the Departments of ‡Protein Chemistry and donaldk@gene.com.ORCID 0000-0003-3091-2804

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

High-throughput genomic and proteomic studies have generated near-comprehensive catalogs of biological constituents within many model systems. Nevertheless, static catalogs are often insufficient to fully describe the dynamic processes that drive biology. Quantitative proteomic techniques address this need by providing insight into closely related biological states such as the stages of a therapeutic response or cellular differentiation. The maturation of quantitative proteomics in recent years has brought about a variety of technologies, each with their own strengths and weaknesses. It can be difficult for those unfamiliar with this evolving landscape to match the experiment at hand with the best tool for the job. Here, we outline quantitative methods for proteomic mass spectrometry and discuss their benefits and weaknesses from the perspective of the biologist aiming to generate meaningful data and address mechanistic questions.

Indexed as

Databases, ProteinHumansMass SpectrometryModels, BiologicalProteomics

Identifiers

PMID26873251
PMCPMC4858934

What OpenQuestion holds

Textmetadata
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.