Document Type


Degree Name

Master of Science (MSc)



Program Name/Specialization

Integrative Biology


Faculty of Science

First Advisor

Gabriel Moreno-Hagelsieb

Advisor Role

Thesis Supervisor


Using computational techniques to identify orthology and operon structure, it is possible to find functional interactions between genes, which, together, define the genetic interactome. These large networks contain information about the relationships between phenotypes in organisms as genes responsible for related abilities are often co-regulated and reasserting of these genes can be detected in the operon structure. However, these networks are too large to analyse by hand In order to practically analyse the networks, a computational tool, gisql, was developed and, using this tool, the connectivity patterns in the genetic interactome can be analysed to understand high-level organisation of the genome and to narrow the list of candidate genes for wet lab analysis. The many strains of Escherichia coli are interesting subjects as there are many sequenced strains and they show highly variable pathogenic abilities. Analysis shows that the pathogenic genes have a strong tendency to connect to genes ubiquitous in the E. coli pan-genome. The Rhizobiales, including Sinorhizobium meliloti and Ochrobactrum anthropi, are multi-chromosomal eukaryote-associated bacteria and a significant history of horizontal transfer. Regions of the pSymB megaplasmid of S. meliloti which cannot be deleted via transposon-targeted homologous recombination were shown to be significantly more connected to the main chromosome. Targets for functional complementation of deletions in pSymB in S. meliloti using genes from O. anthropi were identified and unusual connectivity patterns of orthologs were identified. Finally, a putative cytokinin receptor in the Rhizobiaceæ, likely involved in symbiosis with plant hosts, was identified. Thanks to the flexibility of gisql, these analyses were straight-forward and fast to develop.

Convocation Year


Included in

Genomics Commons