liu.seSearch for publications in DiVA
Change search
ReferencesLink to record
Permanent link

Direct link
Ontology annotation treebrowser: an interactive tool where the complementarity of medical subject headings and gene ontology improves the interpretation of gene lists
Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics . Linköping University, The Institute of Technology.
Biological Sciences, AstraZeneca R&D Lund, Sweden.
Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics . Linköping University, The Institute of Technology.
2006 (English)In: Applied Bioinformatics, ISSN 1175-5636, Vol. 5, no 4, 225-236 p.Article in journal (Refereed) Published
Abstract [en]

Gene expression and proteomics analysis allow the investigation of thousands of biomolecules in parallel. This results in a long list of interesting genes or proteins and a list of annotation terms in the order of thousands. It is not a trivial task to understand such a gene list and it would require extensive efforts to bring together the overwhelming amounts of associated information from the literature and databases. Thus, it is evident that we need ways of condensing and filtering this information. An excellent way to represent knowledge is to use ontologies, where it is possible to group genes or terms with overlapping context, rather than studying one-dimensional lists of keywords. Therefore, we have built the ontology annotation treebrowser (OAT) to represent, condense, filter and summarise the knowledge associated with a list of genes or proteins.

The OAT system consists of two disjointed parts; a MySQL® database named OATdb, and a treebrowser engine that is implemented as a web interface. The OAT system is implemented using Perl scripts on an Apache web server and the gene, ontology and annotation data is stored in a relational MySQL® database. In OAT, we have harmonized the two ontologies of medical subject headings (MeSH) and gene ontology (GO), to enable us to use knowledge both from the literature and the annotation projects in the same tool. OAT includes multiple gene identifier sets, which are merged internally in the OAT database. We have also generated novel MeSH annotations by mapping accession numbers to MEDLINE entries.

The ontology browser OAT was created to facilitate the analysis of gene lists. It can be browsed dynamically, so that a scientist can interact with the data and govern the outcome. Test statistics show which branches are enriched. We also show that the two ontologies complement each other, with surprisingly low overlap, by mapping annotations to the Unified Medical Language System®.

We have developed a novel interactive annotation browser that is the first to incorporate both MeSH and GO for improved interpretation of gene lists. With OAT, we illustrate the benefits of combining MeSH and GO for understanding gene lists. OAT is available as a public web service at:

Place, publisher, year, edition, pages
2006. Vol. 5, no 4, 225-236 p.
National Category
Engineering and Technology
URN: urn:nbn:se:liu:diva-12888OAI: diva2:17297
Available from: 2008-01-28 Created: 2008-01-28 Last updated: 2009-11-07Bibliographically approved
In thesis
1. Characterization of protein families, sequence patterns, and functional annotations in large data sets
Open this publication in new window or tab >>Characterization of protein families, sequence patterns, and functional annotations in large data sets
2008 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Bioinformatics involves storing, analyzing and making predictions on massive amounts of protein and nucleotide sequence data. The thesis consists of six papers and is focused on proteins. It describes the utilization of bioinformatics techniques to characterize protein families and to detect patterns in gene expression and in polypeptide occurrences. Two protein families were bioinformatically characterized - the membrane associated proteins in eicosanoid and glutathione metabolism (MAPEG) and the Tripartite motif (TRIM) protein families.

In the study of the MAPEG super-family, application of different bioinformatic methods made it possible to characterize many new members leading to a doubling of the family size. Furthermore, the MAPEG members were subdivided into families. Remarkably, in six families with previously predominantly mammalian members, fish representatives were also now detected, which dated the origin of these families back to the Cambrium ”species explosion”, thus earlier than previously anticipated. Sequence comparisons made it possible to define diagnostic sequence patterns that can be used in genome annotations. Upon publication of several MAPEG structures, these patterns were confirmed to be part of the active sites.

In the TRIM study, the bioinformatic analyses made it possible to subdivide the proteins into three subtypes and to characterize a large number of members. In addition, the analyses showed crucial structural dependencies between the RING and the B-box domains of the TRIM member

Ro52. The linker region between the two domains, denoted RBL, is known

to be disease associated. Now, an amphipathic helix was found to be a

characteristic feature of the RBL region, which also was used to divide the family into three subtypes.

The ontology annotation treebrowser (OAT) tool was developed to detect functional similarities or common concepts in long lists of proteins or genes, typically generated from proteomics or microarray experiments. OAT was the first annotation browser to include both Gene Ontology (GO) and Medical Subject Headings (MeSH) into the same framework. The complementarity of these two ontologies was demonstrated. OAT was used in the TRIM study to detect differences in functional annotations between the subtypes.

In the oligopeptide study, we investigated pentapeptide patterns that were over- or under-represented in the current de facto standard database of protein knowledge and a set of completed genomes, compared to what could be expected from amino acid compositions. We found three predominant categories of patterns: (i) patterns originating from frequently occurring families, e.g. respiratory chain-associated proteins and translation machinery proteins; (ii) proteins with structurally and/or functionally favored patterns; (iii) multicopy species-specific retrotransposons, only found in the genome set. Such patterns may influence amino acid residue based prediction algorithms. These findings in the oligopeptide study were utilized for development of a new method that detects translated introns in unverified protein predictions, which are available in great numbers due to the many completed and ongoing genome projects.

A new comprehensive database of protein sequences from completed genomes was developed, denoted genomeLKPG. This database was of central importance in the MAPEG, TRIM and oligopeptide studies. The new sequence database has also been proven useful in several other studies.

Place, publisher, year, edition, pages
Institutionen för fysik, kemi och biologi, 2008. 85 p.
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1159
Bioinformatics, sequence analysis, patterns, protein families
National Category
Bioinformatics (Computational Biology)
urn:nbn:se:liu:diva-10565 (URN)978-91-85523-01-6 (ISBN)
Public defence
2008-02-15, Planck, Fysikhuset, Linköpings Universitet, Linköping, 10:15 (English)
Available from: 2008-01-28 Created: 2008-01-28 Last updated: 2010-01-13Bibliographically approved

Open Access in DiVA

No full text

Other links

Link to articleLink to Ph.D. Thesis

Search in DiVA

By author/editor
Bresell, AndersPersson, Bengt
By organisation
Bioinformatics The Institute of Technology
Engineering and Technology

Search outside of DiVA

GoogleGoogle Scholar
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Total: 123 hits
ReferencesLink to record
Permanent link

Direct link