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InParanoid 6: eukaryotic ortholog clusters with inparalogs
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics.
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics.
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics.
2008 (English)In: Nucleic Acids Research, ISSN 0305-1048, E-ISSN 1362-4962, Vol. 36, D263-D266 p.Article in journal (Refereed) Published
Abstract [en]

The InParanoid eukaryotic ortholog database (http://InParanoid.sbc.su.se/) has been updated to version 6 and is now based on 35 species. We collected all available 'complete' eukaryotic proteomes and Escherichia coli, and calculated ortholog groups for all 595 species pairs using the InParanoid program. This resulted in 2 642 187 pairwise ortholog groups in total. The orthology-based species relations are presented in an orthophylogram. InParanoid clusters contain one or more orthologs from each of the two species. Multiple orthologs in the same species, i.e. inparalogs, result from gene duplications after the species divergence. A new InParanoid website has been developed which is optimized for speed both for users and for updating the system. The XML output format has been improved for efficient processing of the InParanoid ortholog clusters.

Place, publisher, year, edition, pages
2008. Vol. 36, D263-D266 p.
Keyword [en]
Animals, Cluster Analysis, Databases, Protein, Gene Duplication, Humans, Internet, Phylogeny, Proteins/*genetics, Proteomics
National Category
Bioinformatics and Systems Biology
Research subject
Biochemistry with Emphasis on Theoretical Chemistry
Identifiers
URN: urn:nbn:se:su:diva-17878DOI: 10.1093/nar/gkm1020ISI: 000252545400048PubMedID: 18055500OAI: oai:DiVA.org:su-17878DiVA: diva2:184399
Available from: 2009-01-21 Created: 2009-01-21 Last updated: 2017-12-13Bibliographically 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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Sjölund, ErikÖstlund, GabrielSonnhammer, Erik L. L.
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