Human retrotransposons are genetic elements that copy themselves into new locations in the genome by way of an RNA intermediate. They are extremely numerous making up at least 45% of human DNA. Retrotransposon insertions are a major source of inter-human genetic variation, and have been known to cause disease. They are also intrinsically difficult to analyse in genomes due to their highly repetitive nature. In humans there are three currently active retro-transposable elements: LINE-1, Alu and SVA. LINE-1 is an independent element and Alu and SVA parasitise the LINE-1 retrotransposition machinery. There are experimental ways of discovering and analysing such elements, but they require significant investment, while human sequence datasets containing potentially usable data are multiplying at an ever increasing rate. In particular there are now many assembled human genome sequences as well as new sources of whole genome high throughput sequencing data, such as the 1000 Genomes Project. For this reason this study is devoted to using bioinformatic approaches to extract new knowledge about human retrotransposons from the existing datasets. Previous efforts, by past members of this research group, have been devoted to analysing the genomic variation of the LINE-1 element itself. However this study focuses on the extraction of presence / absence variation in the LINE-1 -dependent elements, Alu and SVA. In addition to building software to extract this information from a wide variety of data sources, this project has also involved making the information data available to non-specialist researchers in the form of a website. The tools developed and described here utilise generic design principles, enabling rapid, largely automated updating, necessary with the constant expansion of the underlying data.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:617660 |
| Date | January 2014 |
| Creators | Wagstaff, John Francis |
| Contributors | Badge, Richard |
| Publisher | University of Leicester |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://hdl.handle.net/2381/29050 |
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