Genomics and bioinformatics have been traditionally dominated by a handful of “model” study species. Analysis pipelines and bioinformatic software were initially developed with these species in mind, are biased towards these species, and often fail to perform well on other distantly-related genomes. In particular, genome annotation analysis pipelines are often restricted to annotation mapping based on the known annotations available in major databases. Often many bioinformatic tools assume data has the same genomic characteristics as those of the model organisms.
Genomic and transcriptomic studies have recently moved beyond the scope the traditional model organisms and are more frequently based on de novo approaches. New genomes and de novo transcriptomes heavily rely on automated computational pipelines to annotate their genes/transcripts; manual annotation and review is conducted on only a small subset of sequences available, thus erroneous annotations may be propagated through the databases. Redundancies, discrepancies and ambiguities, are common in the annotation of biological sequence data. Given the breadth of biological variation in nature, existing annotation software may not be ideal for some species, and may produce erroneous results. Most annotation tools also lack the functionality to deal with outdated and obsolete annotations, which are commonly produced during de novo annotation due to artifacts in the databases.
This project will investigate the accuracy of traditional bioinformatics approaches to gene functional assignment in non-models by bioinformatically tracking the propagation of annotations and evaluating the effect of erroneous annotation over time. It will then develop software solutions to improve accuracy of genome/transcriptome annotation in non-models, which are not restricted by genomic features, and are generic enough to be used in any species.
Eligibility and How to Apply:
Please note eligibility requirement:
• Academic excellence of the proposed student i.e. 2:1 (or equivalent GPA from non-UK universities [preference for 1st class honours]); or a Masters (preference for Merit or above); or APEL evidence of substantial practitioner achievement.
• Appropriate IELTS score, if required.
• Applicants cannot apply for this funding if currently engaged in Doctoral study at Northumbria or elsewhere.
For further details of how to apply, entry requirements and the application form, see https://www.northumbria.ac.uk/research/postgraduate-research-degrees/how-to-apply/
Please note: Applications should include a covering letter that includes a short summary (500 words max.) of a relevant piece of research that you have previously completed and the reasons you consider yourself suited to the project. Applications that do not include the advert reference (e.g. SF20/…) will not be considered.
Deadline for applications: 1st July for October start, or 1st December for March start
Start Date: October or March
Northumbria University takes pride in, and values, the quality and diversity of our staff. We welcome applications from all members of the community. The University holds an Athena SWAN Bronze award in recognition of our commitment to improving employment practices for the advancement of gender equality.
Please direct enquiries to Dr Katherine James ([email protected]
James and Olson (2019). The tapeworm interactome: inferring confidence scored protein-protein interactions from the proteome of Hymenolepis microstoma. bioRxiv doi:10.1101/668988.
Johanson, Martin, Fraser and James (2019). The synarcual of the little skate, Leucoraja erinacea: novel development among the vertebrates. Frontiers in Ecology and Evolution, 7: 2296-701.
Olson, Zarowiecki, James, Baillie, Bartl, Burchell, Chellappoo, Jarero, Tan, Holroyd and Berriman (2018). Genome-wide transcriptome profiling and spatial expression analyses identify signals and switches of development in tapeworms. EvoDevo 9: 21.
James, Wipat and Hallinan (2012). Is newer better? - Evaluating the effects of data curation on integrated analyses in Saccharomyces cerevisiae. Integrative Biology (United Kingdom), 4: 715—727.