Understanding how function relates to multiple layers of inactions between biological entities is one of the key goals of bioinformatics research, in particular in such areas as systems biology. However, the realisation of this objective is hampered by the sheer volume and multi-level heterogeneity of potentially relevant information. This work addressed this issue by developing a set of integration pipelines and analysis methods as part of an Ondex data integration framework. The integration process incorporated both relevant data from a set of publically available databases and information derived from predicted approaches, which were also implemented as part of this work. These methods were used to assemble integrated datasets that were of relevance to the study of the model plant species Arabidopsis thaliana and applicable for the network-driven analysis. A particular attention was paid to the evaluation and comparison of the different sources of these data. Approaches were implemented for the identification and characterisation of functional modules in integrated networks and used to study and compare networks constructed from different types of data. The benefits of data integration were also demonstrated in three different bioinformatics research scenarios. The analysis of the constructed datasets has also resulted in a better understanding of the functional role of genes identified in a study of a nitrogen uptake mutant and allowed to select candidate genes for further exploration.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:581989 |
| Date | January 2012 |
| Creators | Lysenko, Artem |
| Publisher | University of Nottingham |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://eprints.nottingham.ac.uk/27798/ |
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