Evidence map›Paper›PMID 28248220›Full record

ArticleProteomes2016

Comparing Simplification Strategies for the Skeletal Muscle Proteome.

Bethany Geary, Iain S Young, Phillip Cash, Phillip D Whitfield, Mary K Doherty

Open access · goldAbstract read
In one paragraph

Article in Proteomes, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.2field-weighted citation impact, top 37% of its field
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

0 citing papers in PubMed, 3 citations in OpenAlex.

No citing paper in PubMed yet.

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

5 authors at 3 institutions in 1 country.

Bethany GearyDivision of Health Research, University of the Highlands and Islands, Inverness IV2 3JH, UK. beth.geary@uhi.ac.uk.
Iain S YoungInstitute of Integrative Biology, University of Liverpool, Liverpool L69 7ZB, UK. isyoung@liverpool.ac.uk.
Phillip CashDivision of Applied Medicine, University of Aberdeen, Aberdeen AB25 2ZD, UK. p.cash@abdn.ac.uk.
Phillip D WhitfieldDivision of Health Research, University of the Highlands and Islands, Inverness IV2 3JH, UK. phil.whitfield@uhi.ac.uk.
Mary K DohertyDivision of Health Research, University of the Highlands and Islands, Inverness IV2 3JH, UK. mary.doherty@uhi.ac.uk.
University of the Highlands and Islands · GBUniversity of Aberdeen · GBUniversity of Liverpool · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Skeletal muscle is a complex tissue that is dominated by the presence of a few abundant proteins. This wide dynamic range can mask the presence of lower abundance proteins, which can be a confounding factor in large-scale proteomic experiments. In this study, we have investigated a number of pre-fractionation methods, at both the protein and peptide level, for the characterization of the skeletal muscle proteome. The analyses revealed that the use of OFFGEL isoelectric focusing yielded the largest number of protein identifications (>750) compared to alternative gel-based and protein equalization strategies. Further, OFFGEL led to a substantial enrichment of a different sub-population of the proteome. Filter-aided sample preparation (FASP), coupled to peptide-level OFFGEL provided more confidence in the results due to a substantial increase in the number of peptides assigned to each protein. The findings presented here support the use of a multiplexed approach to proteome characterization of skeletal muscle, which has a recognized imbalance in the dynamic range of its protein complement.

Indexed as

FASPOFFGELprotein equalizationproteome simplificationProteoMinerskeletal muscle

Identifiers

PMID28248220
PMCPMC5217366
OpenAlexW2292902806

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.