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  4. A Knowledge-Based Framework for the Alignment of Prokaryotic Genomes
 
Zitierlink URN
https://nbn-resolving.de/urn:nbn:de:gbv:46-diss000012987

A Knowledge-Based Framework for the Alignment of Prokaryotic Genomes

Veröffentlichungsdatum
2005-06-21
Autoren
Wetjen, Tom  
Betreuer
Herzog, Otthein  
Gutachter
Stoye, Jens  
Zusammenfassung
The comparison of prokaryotic genomes is an important analysis step in bioinformatics which can help to discover genes and their functions, regulatory mechanisms, and phylogenetic relationships. Alignments generated with tools available for this task commonly show a relative low coverage and sensitivity,since only strong local similarities (i.e., the longest similarities with thehighest similarity values) are integrated into the alignment. The reason is the high number of global mutations (e.g., rearrangements and duplications) which frequently occur in prokaryotes which causes ambiguity during the assignment of similar subsequences of the genomes. This thesis describes a new knowledge-based framework which integrates biological knowledge of prokaryotic genome architecture into the alignment process of whole genomes in order to increase its quality (i.e., sensitivity, coverage, and specificity). The approach validates local similarities found between a reference genome for which knowledge exists and a query genome for which knowledge is not necessarily available with respect to the genome architecture of the reference genome. The framework allows for a manual and a automatical integration of more knowledge of common genome architecture into the reference model. The application of the knowledge-based framework on the set of local similarities found with a relaxed similarity threshold can significantly increase the sensitivity and the coverage of the resulting alignment in comparison to other approaches while the specificity remains nearly as good as with a strict similarity value.
Schlagwörter
Genome

; 

Alignment

; 

Prokaryotes

; 

Knowledge-based

; 

Constraint Satisfaction

; 

Data Mining
Institution
Universität Bremen  
Fachbereich
Fachbereich 03: Mathematik/Informatik (FB 03)  
Dokumenttyp
Dissertation
Zweitveröffentlichung
Nein
Sprache
Englisch
Dateien
Lade...
Vorschaubild
Name

E-Diss1298_Dissertation.pdf

Size

18.62 MB

Format

Adobe PDF

Checksum

(MD5):714a85a864088cb2e6ad5dbc8ea7a3b7

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