Evidence map›Paper›PMID 23398688›Full record

ArticleBMC bioinformatics2013

A survey of protein interaction data and multigenic inherited disorders.

Antonio Mora, Katerina Michalickova, Ian M Donaldson

Abstract read
In one paragraph

Article in BMC bioinformatics, 2013. 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

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2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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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

3 authors.

Antonio MoraDepartment for Molecular Biosciences, University of Oslo, P.O. Box 1041 Blindern, 0316, Oslo, Norway.
Katerina Michalickova
Ian M Donaldson

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMultigenic diseases are often associated with protein complexes or interactions involved in the same pathway. We wanted to estimate to what extent this is true given a consolidated protein interaction data set. The study stresses data integration and data representation issues.

resultsWe constructed 497 multigenic disease groups from OMIM and tested for overlaps with interaction and pathway data. A total of 159 disease groups had significant overlaps with protein interaction data consolidated by iRefIndex. A further 68 disease overlaps were found only in the KEGG pathway database. No single database contained all significant overlaps thus stressing the importance of data integration. We also found that disease groups overlapped with all three interaction data types: n-ary, spoke-represented complexes and binary data - thus stressing the importance of considering each of these data types separately.

conclusionsAlmost half of our multigenic disease groups could potentially be explained by protein complexes and pathways. However, the fact that no database or data type was able to cover all disease groups suggests that no single database has systematically covered all disease groups for potential related complex and pathway data. This survey provides a basis for further curation efforts to confirm and search for overlaps between diseases and interaction data. The accompanying R script can be used to reproduce the work and track progress in this area as databases change. Disease group overlaps can be further explored using the iRefscape plugin for Cytoscape.

Indexed as

AlgorithmsDatabases, GeneticDatabases, ProteinGenetic Diseases, InbornHumansHyperglycinemia, NonketoticLiddle SyndromeMultiprotein ComplexesNephritis, HereditaryProtein Interaction MappingMultiprotein Complexes

Identifiers

PMID23398688
PMCPMC3598893

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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.