Novel methodology for predicting the functional impact of mutational variation in the human genome.

Lead Research Organisation: University of Bristol
Department Name: Engineering Mathematics

Abstract

Rapid developments in next-generation sequencing technologies has lead to a substantial
increase in identified genetic mutations, many of which may be implicated in disease. The
focus of the PhD programme is to construct algorithmically-based methods for predicting the
functional consequence of genetic variants, a problem where we have already achieved
some state-of-the-art results in certain contexts. Particular themes will be novel
methodology associated with data integration, rare variant combinations and probabilistic
inference. We expect to use the methods developed with data from the
Genomics England (100,000 genomes) project.

Publications

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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/N509619/1 01/10/2016 30/09/2021
1838073 Studentship EP/N509619/1 21/11/2016 28/05/2020 James Fearn
 
Description An algorithmic method has been implemented (MKL div-dc) to combine multiple heterogeneous data sources, that is features of the human genome, into a Support Vector Machine to predict oncogenic single nucleotide variants. It has been found that this method improves over the previous methodology CScape in coding and non-coding regions of the genome.
Exploitation Route This work can be built on by improving the data integration methodology, MKL div-dc. The 'Phastcons' conservation score was found to be the most relevant feature. Future work should explore improvements to the Phastcons conservation score methodology & its application to oncogenic single nucleotide variant prediction.
Sectors Digital/Communication/Information Technologies (including Software),Healthcare,Pharmaceuticals and Medical Biotechnology

 
Title Phylogenetic Relevance Vector Machine (phyRVM) 
Description An extension to the relevance vector machine which takes into account the genetic non-independence of living organisms by analytically incorporating the phylogenetic covariance matrix into the likelihood. All of Pagel's phylogenetic comparative parameters (Lambda, Delta and Kappa) are incorporated into this model. 
Type Of Material Computer model/algorithm 
Year Produced 2019 
Provided To Others? No  
Impact None