Evidence map›Paper›PMID 29323144›Full record

ArticleScientific reports2018

Defining tissue proteomes by systematic literature review.

Sarah A Hibbert, Matiss Ozols, Christopher E M Griffiths, Rachel E B Watson, Mike Bell, Michael J Sherratt

Abstract read
In one paragraph

Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
–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

7 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Predicting Proteolysis in Complex Proteomes Using Deep Learning.International journal of molecular sciences · 2021
    Article
  6. Article
  7. 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.

Sarah A HibbertDivision of Cell Matrix Biology & Regenerative Medicine, The University of Manchester, Manchester, UK. sarah.hibbert@manchester.ac.uk.ORCID http://orcid.org/0000-0001-9719-6510
Matiss OzolsDivision of Cell Matrix Biology & Regenerative Medicine, The University of Manchester, Manchester, UK.
Christopher E M GriffithsCentre for Dermatology Research, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK.
Rachel E B WatsonCentre for Dermatology Research, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK.
Mike BellWalgreens Boots Alliance, Thane Road, Nottingham, UK.
Michael J SherrattDivision of Cell Matrix Biology & Regenerative Medicine, The University of Manchester, Manchester, UK. michael.sherratt@manchester.ac.uk.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Defining protein composition is a key step in understanding the function of both healthy and diseased biological systems. There is currently little consensus between existing published proteomes in tissues such as the aorta, cartilage and organs such as skin. Lack of agreement as to both the number and identity of proteins may be due to issues in protein extraction, sensitivity/specificity of detection and the use of disparate tissue/cell sources. Here, we developed a method combining bioinformatics and systematic review to screen >32M articles from the Web of Science for evidence of proteins in healthy human skin. The resulting Manchester Proteome ( www.manchesterproteome.manchester.ac.uk ) collates existing evidence which characterises 2,948 skin proteins, 437 unique to our database and 2011 evidenced by both mass spectrometry and immune-based techniques. This approach circumvents the limitations of individual proteomics studies and can be applied to other species, organs, cells or disease-states. Accurate tissue proteomes will aid development of engineered constructs and offer insight into disease treatments by highlighting differences in proteomic composition.

Indexed as

Meta-Analysis as TopicSoftwareHumansProteomeProteomicsSkinProteome

Identifiers

PMID29323144
PMCPMC5765030

What OpenQuestion holds

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LicenceCC BY
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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.