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Quality criteria for finding genes with high mRNA-protein expression correlation and coexpression correlation
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics.
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics.
2012 (English)In: Gene, ISSN 0378-1119, E-ISSN 1879-0038, Vol. 497, no 2, 228-236 p.Article in journal (Refereed) Published
Abstract [en]

mRNA expression is widely used as a proxy for protein expression. However, their true relation is not known and two genes with the same mRNA levels might have different abundances of respective proteins. A related question is whether the coexpression of mRNA for gene pairs is reflected by the corresponding protein pairs. We examined the mRNA-protein correlation for both expression and coexpression. This analysis yielded insights into the relationship between mRNA and protein abundance, and allowed us to identify subsets of greater mRNA-protein coherence. The correlation between mRNA and protein was low for both expression and coexpression, 0.12 and 0.06 respectively. However, applying the best-performing quality measure, high-quality subsets reached a Spearman correlation of 0.31 for expression, 034 for coexpression and 0.49 for coexpression when restricted to functionally coupled genes. Our methodology can thus identify subsets for which the mRNA levels are expected to be the strongest correlated with protein levels.

Place, publisher, year, edition, pages
2012. Vol. 497, no 2, 228-236 p.
Keyword [en]
mRNA expression, mRNA coexpression, Protein expression, Protein coexpression, mRNA-protein expression concordance, Microarray
National Category
Bioinformatics and Systems Biology
Research subject
Biochemistry with Emphasis on Theoretical Chemistry
Identifiers
URN: urn:nbn:se:su:diva-76043DOI: 10.1016/j.gene.2012.01.029ISI: 000302584300012OAI: oai:DiVA.org:su-76043DiVA: diva2:525799
Note

2

Available from: 2012-05-09 Created: 2012-05-08 Last updated: 2017-12-07Bibliographically approved
In thesis
1. Data integration for robust network-based disease gene prediction
Open this publication in new window or tab >>Data integration for robust network-based disease gene prediction
2013 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

For many complex diseases the cause/mechanism can be tied not to a single gene and in order to cope with the complexity a systems wide approach is needed. By combining evidence indicative of functional association it is possible to infer networks of protein functional coupling. The reliability of these networks is dependent on having sufficient data and on the data being informative.

By combining evidence from multiple species, functional coupling networks can reach higher coverage and accuracy. Genes in different species derived from the same gene by a speciation event are orthologous and likely to have a conserved function. In order to enable the transfer of information across species we inferred orthology with the InParanoid algorithm and made the inferences available to the public in the associated database.

Identification of genes involved in diseases is an important biomedical goal. Based on the "guilt by association" principle, we implemented an approach, Maxlink, for identifying and prioritizing novel disease genes. By searching the FunCoup network for genes functionally coupled to cancer genes we identified some 1800 novel cancer gene candidates showing characteristics of cancer genes.

While proteins are the active components, mRNA is often used as a proxy due to the difficulty of measuring protein abundance. We examined the relationship between mRNA and protein, using properties of expression profiles to identify subsets of genes with higher mRNA-protein concordance.

If technical and biological differences between patient/control studies of gene expression have a large impact, the results of studies of the same disease might be inconsistent. To determine this impact we examined the consistency in differential (co)expression between different studies of cancer, as well as non-cancer studies. Such consistency could generally be found, even between studies of different diseases, but only when common pitfalls of gene expression analysis are avoided.

Place, publisher, year, edition, pages
Stockholm: Department of Biochemistry and Biophysics, Stockholm University, 2013. 71 p.
National Category
Bioinformatics and Systems Biology
Research subject
Biochemistry with Emphasis on Theoretical Chemistry
Identifiers
urn:nbn:se:su:diva-87962 (URN)978-91-7447-629-3 (ISBN)
Public defence
2013-04-12, Magnélisalen, Kemiska övningslaboratoriet, Svante Arrhenius väg 16 B, Stockholm, 10:00 (English)
Opponent
Supervisors
Note

At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 5: Manuscript.

Available from: 2013-03-21 Created: 2013-02-27 Last updated: 2013-03-18Bibliographically approved

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Östlund, GabrielSonnhammer, Erik L. L.
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