Evidence map›Paper›PMID 28562641›Full record

ArticlePloS one2017

PGCA: An algorithm to link protein groups created from MS/MS data.

David Kepplinger, Mandeep Takhar, Mayu Sasaki, Zsuzsanna Hollander, Derek Smith, Bruce McManus, W Robert McMaster, Raymond T Ng, Gabriela V Cohen Freue

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2017. 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
0.1field-weighted citation impact, top 52% 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, 1 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

9 authors at 3 institutions in 1 country.

David KepplingerDepartment of Statistics, University of British Columbia, Vancouver, British Columbia, Canada.
Mandeep TakharNCE CECR PROOF Centre of Excellence, Vancouver, British Columbia, Canada.
Mayu SasakiNCE CECR PROOF Centre of Excellence, Vancouver, British Columbia, Canada.
Zsuzsanna HollanderNCE CECR PROOF Centre of Excellence, Vancouver, British Columbia, Canada.
Derek SmithUniversity of Victoria - Genome BC Proteomics Centre, Victoria, British Columbia, Canada.
Bruce McManusNCE CECR PROOF Centre of Excellence, Vancouver, British Columbia, Canada.
W Robert McMasterDepartment of Medical Genetics, University of British Columbia, Vancouver, British Columbia, Canada.
Raymond T NgNCE CECR PROOF Centre of Excellence, Vancouver, British Columbia, Canada.
Gabriela V Cohen FreueDepartment of Statistics, University of British Columbia, Vancouver, British Columbia, Canada.
Prevention of Organ Failure · CAUniversity of British Columbia · CAUniversity of Victoria · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The quantitation of proteins using shotgun proteomics has gained popularity in the last decades, simplifying sample handling procedures, removing extensive protein separation steps and achieving a relatively high throughput readout. The process starts with the digestion of the protein mixture into peptides, which are then separated by liquid chromatography and sequenced by tandem mass spectrometry (MS/MS). At the end of the workflow, recovering the identity of the proteins originally present in the sample is often a difficult and ambiguous process, because more than one protein identifier may match a set of peptides identified from the MS/MS spectra. To address this identification problem, many MS/MS data processing software tools combine all plausible protein identifiers matching a common set of peptides into a protein group. However, this solution introduces new challenges in studies with multiple experimental runs, which can be characterized by three main factors: i) protein groups' identifiers are local, i.e., they vary run to run, ii) the composition of each group may change across runs, and iii) the supporting evidence of proteins within each group may also change across runs. Since in general there is no conclusive evidence about the absence of proteins in the groups, protein groups need to be linked across different runs in subsequent statistical analyses. We propose an algorithm, called Protein Group Code Algorithm (PGCA), to link groups from multiple experimental runs by forming global protein groups from connected local groups. The algorithm is computationally inexpensive and enables the connection and analysis of lists of protein groups across runs needed in biomarkers studies. We illustrate the identification problem and the stability of the PGCA mapping using 65 iTRAQ experimental runs. Further, we use two biomarker studies to show how PGCA enables the discovery of relevant candidate protein group markers with similar but non-identical compositions in different runs.

Indexed as

AlgorithmsAmino Acid SequenceBiomarkersHeart TransplantationHumansMuscular DystrophiesProteinsProteomicsSequence Homology, Amino AcidTandem Mass SpectrometryBiomarkersProteins

Identifiers

PMID28562641
PMCPMC5451011
OpenAlexW2619393833

What OpenQuestion holds

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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.