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Dr Kate Elliott
Senior Postdoctoral Bioinformatician
Genomic Medicine
My particular interest is identifying genetic variants underlying epilepsy and severe infectious diseases with the aim of translation into clinical medicine. High throughput sequencing (HTseq) allows the simultaneous screening of nearly all of the genes in the genome to identify potentially pathogenic variants. HTseq is being used to whole genome or exome sequence affected individuals (some with their parents) in order to identify such variants. I have been developing a pipeline which processes HTseq data, mapping and calling the reads followed by annotation and filtering to identify which variants are most likely to be pathogenic. This is based on the position of the variants within genes and whether they are likely to disrupt gene production (eg stop gain, frameshift or splice variants), combined with their rarity. Pathogenic variants will likely be extremely rare since they will be removed by natural selection or are de novo mutations (differences seen in a patient but not in their parents). These variants can then be functionally assessed and provide new insights into disease mechanism and treatment.
Background
I obtained a PhD in Drosophila genetics from Imperial College which provided an excellent starting point to understand the genetic machine, its mechanisms and variation. I then went on to complete postdocs in mouse and human genetics which included developing a mouse model of Friedreich ataxia and linkage mapping of the 5q31 atopy locus. I then moved from wet lab genetics to bioinformatics by mapping an ADHD associated breakpoint bioinformatically rather than by cloning in the lab. Since then I have developed many pipelines and bioinformatics tools for the analysis of genetic data. One of these is GeneSniffer which is designed to prioritise genes relative to the online disease relevant information (primarily PubMed) for any condition of interest. My current expertise is focussed on the processing, annotation and filtering of sequencing data to identify pathogenic variants for genomic medicine.