Evidence map›Paper›PMID 25627479›Full record

ArticleBMC bioinformatics2015

Candidate prioritization for low-abundant differentially expressed proteins in 2D-DIGE datasets.

Umesh K Nandal, Wytze J Vlietstra, Carsten Byrman, Rienk E Jeeninga, Jeffrey H Ringrose, Antoine H C van Kampen, Dave Speijer, Perry D Moerland

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PubMed Full text at PMC Publisher
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Article in BMC bioinformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

8 authors.

Umesh K NandalBioinformatics Laboratory, Academic Medical Center, University of Amsterdam, PO Box 22700, DE Amsterdam, 1100, The Netherlands. u.k.nandal@amc.uva.nl.
Wytze J VlietstraBioinformatics Laboratory, Academic Medical Center, University of Amsterdam, PO Box 22700, DE Amsterdam, 1100, The Netherlands. wytze.vlietstra@gmail.com.
Carsten ByrmanBioinformatics Laboratory, Academic Medical Center, University of Amsterdam, PO Box 22700, DE Amsterdam, 1100, The Netherlands. carsten@byrman.demon.nl.
Rienk E JeeningaLaboratory of Experimental Virology, Department of Medical Microbiology, Center for Infection and Immunity Amsterdam (CINIMA), Academic Medical Center, University of Amsterdam, PO Box 22700, DE Amsterdam, 1100, The Netherlands. r.jeeninga@amc.uva.nl.
Jeffrey H RingroseLaboratory of Experimental Virology, Department of Medical Microbiology, Center for Infection and Immunity Amsterdam (CINIMA), Academic Medical Center, University of Amsterdam, PO Box 22700, DE Amsterdam, 1100, The Netherlands. J.H.Ringrose@uu.nl.
Antoine H C van KampenBioinformatics Laboratory, Academic Medical Center, University of Amsterdam, PO Box 22700, DE Amsterdam, 1100, The Netherlands. a.h.vankampen@amc.uva.nl.
Dave SpeijerDepartment of Medical Biochemistry, Academic Medical Center, University of Amsterdam, PO Box 22700, DE Amsterdam, 1100, The Netherlands. d.speijer@amc.uva.nl.
Perry D MoerlandBioinformatics Laboratory, Academic Medical Center, University of Amsterdam, PO Box 22700, DE Amsterdam, 1100, The Netherlands. p.d.moerland@amc.uva.nl.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTwo-dimensional differential gel electrophoresis (2D-DIGE) provides a powerful technique to separate proteins on their isoelectric point and apparent molecular mass and quantify changes in protein expression. Abundantly available proteins in spots can be identified using mass spectrometry-based approaches. However, identification is often not possible for low-abundant proteins.

resultsWe present a novel computational approach to prioritize candidate proteins for unidentified spots. Our approach exploits noisy information on the isoelectric point and apparent molecular mass of a protein spot in combination with functional similarities of candidate proteins to already identified proteins to select and rank candidates. We evaluated our method on a 2D-DIGE dataset comparing protein expression in uninfected and HIV-1 infected T-cells. Using leave-one-out cross-validation, we show that the true-positive rate for the top-5 ranked proteins is 43.8%.

conclusionsOur approach shows good performance on a 2D-DIGE dataset comparing protein expression in uninfected and HIV-1 infected T-cells. We expect our method to be highly useful in (re-)mining other 2D-DIGE experiments in which especially the low-abundant protein spots remain to be identified.

Indexed as

Cells, CulturedElectrophoresis, Gel, Two-DimensionalHIV-1HIV InfectionsHumansPeptide FragmentsProteinsProteomicsSpectrometry, Mass, Matrix-Assisted Laser Desorption-IonizationT-LymphocytesTwo-Dimensional Difference Gel ElectrophoresisPeptide FragmentsProteins

Identifiers

PMID25627479
PMCPMC4384356

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LicenceCC BY
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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.