Use this URL to cite or link to this record in EThOS:
Title: Computational functional annotation of crop genomics using hierarchical orthologous groups
Author: Warwick Vesztrocy, Alexander George
ISNI:       0000 0004 8508 0635
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2019
Availability of Full Text:
Access from EThOS:
Full text unavailable from EThOS. Please try the link below.
Access from Institution:
Improving agronomically important traits, such as yield, is important in order to meet the ever growing demands of increased crop production. Knowledge of the genes that have an effect on a given trait can be used to enhance genomic selection by prediction of biologically interesting loci. Candidate genes that are strongly linked to a desired trait can then be targeted by transformation or genome editing. This application of prioritisation of genetic material can accelerate crop improvement. However, the application of this is currently limited due to the lack of accurate annotations and methods to integrate experimental data with evolutionary relationships. Hierarchical orthologous groups (HOGs) provide nested groups of genes that enable the comparison of highly diverged and similar species in a consistent manner. Over 2,250 species are included in the OMA project, resulting in over 600,000 HOGs. This thesis provides the required methodology and a tool to exploit this rich source of information, in the HOGPROP algorithm. The potential of this is then demonstrated in mining crop genome data, from metabolic QTL studies and utilising Gene Ontology (GO) annotations as well as ChEBI terms (Chemical Entities of Biological Interest) in order to prioritise candidate causal genes. Gauging the performance of the tool is also important. When considering GO annotations, the CAFA series of community experiments has provided the most extensive benchmarking to-date. However, this has not fully taken into account the incomplete knowledge of protein function - the open world assumption (OWA). This will require extra negative annotations, for which one such source has been identified based on expertly curated gene phylogenies. These negative annotations are then utilised in the proposed, OWA-compliant, improved framework for benchmarking. The results show that current benchmarks tend to focus on the general terms, which means that conclusions are not merely uninformative, but misleading.
Supervisor: Dessimoz, C. ; Redestig, H. ; Oliveri, P. Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available