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GenomeLKPG: A comprehensive proteome sequencedatabase for taxonomy studies
Linköping University, Department of Physics, Chemistry and Biology, Biomolecular and Organic Electronics . Linköping University, The Institute of Technology.
Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics . Linköping University, The Institute of Technology.
2008 (English)Article in journal (Refereed) Submitted
Abstract [en]

Background: In order to perform taxonomically unbiased analyses of protein relationships, there is a need ofcomplete proteomes rather than databases with bias towards well characterized protein families. However, nocomprehensive resource of completed proteomes is currently available. Instead, the proteomes need to be down-loaded manually from di®erent servers, all using different filename conventions and fasta header formats.

Results: We have developed a semi-automatic algorithm that retrieves complete proteomes from multiple FTP-servers and maps the species-speci¯c sequence entries to the NCBI taxonomy. The compiled data is provided ina sequence database named genomeLKPG.

Conclusions: The usefulness of genomeLKPG is proven in several published taxonomical studies.

Place, publisher, year, edition, pages
National Category
Natural Sciences
URN: urn:nbn:se:liu:diva-52933OAI: diva2:285932
Available from: 2010-01-13 Created: 2010-01-13 Last updated: 2010-01-13
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

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