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Prof David Westhead
Dr J Boyes
No more applications being accepted
Competition Funded PhD Project (European/UK Students Only)
About the Project
Genetic regulatory networks control critical biological processes, from development and cellular differentiation to the response to environmental cues and stress. In eukarytotes however the networks are substantially unknown and difficult to predict, since they may differ substantially in different cell types and because enhancer or supressor activities for individual genes may be driven from distal genomic regions. In recent years there has been an explosion in high throughput data generation relevant to genetic regulation. This has been primarily based on next generation DNA sequencing, and includes DNase/ATAC-seq, ChIP-seq for chromatin modification and transcription factor binding, RNA-seq for gene expression measurement and increasingly 3D chromosome structure data. This project aims to apply machine learning methods to these large, heterogeneous data sets to predict genetic regulatory networks. It follows on from recent work in the group, where we have developed penalized regression methods integrate DNase-seq, ChIP-seq and RNA-seq data into regulatory models for individual genes. It will employ methods such as neural networks and support vector machines, where we have existing expertise, and an important aspect of the project will be assessment of the utility of the prediction models in analysing the biological consequences of regulatory (non-coding) DNA changes, for instance in genome wide association studies of disease phenotypes and the interpretation of mutational data, for instance from cancer cells.
Funding Notes
Project is eligible for funding under the BBSRC White Rose DTP: Doctoral Studentships in Artificial Intelligence, Machine Learning and Data Driven Economy.
Successful candidates will receive funding for 4 years, covering UK/EU fees and research council stipend (£14,777 for 2018-19).
Candidates should have, or be expecting, a 2.1 or above at undergraduate level in a relevant field. If English is not your first language, you will also be required to meet our language entry requirements. The PhD is to start in Oct 2018.
Apply online: https://studentservices.leeds.ac.uk/pls/banprod/bwskalog_uol.P_DispLoginNon
Include project title and supervisor name, and upload a CV and transcripts.
Successful candidates will receive funding for 4 years, covering UK/EU fees and research council stipend (£14,777 for 2018-19).
Candidates should have, or be expecting, a 2.1 or above at undergraduate level in a relevant field. If English is not your first language, you will also be required to meet our language entry requirements. The PhD is to start in Oct 2018.
Apply online: https://studentservices.leeds.ac.uk/pls/banprod/bwskalog_uol.P_DispLoginNon
Include project title and supervisor name, and upload a CV and transcripts.
References
Toenhake CG, Fraschka SA-K, Vijayabaskar MS, Westhead DR, van Heeringen SJ, Bártfai R Chromatin Accessibility-Based Characterization of the Gene Regulatory Network Underlying Plasmodium falciparum Blood-Stage Development. Cell Host&Microbe 23 557-569, 2018.
Zainul Abidin FN, Westhead DR Flexible model-based clustering of mixed binary and continuous data: application to genetic regulation and cancer. Nucleic Acids Res 45 e53-, 2017.
Shar NA, Vijayabaskar MS, Westhead DR Cancer somatic mutations cluster in a subset of regulatory sites predicted from the ENCODE data Molecular Cancer 15, 2016.
Goode DK, Obier N, Vijayabaskar MS, Lie-A-Ling M, Lilly AJ, Hannah R, Lichtinger M, Batta K, Florkowska M, Patel R, Challinor M, Wallace K, Gilmour J, Assi SA, Cauchy P, Hoogenkamp M, Westhead DR, Lacaud G, Kouskoff V, Göttgens B, Bonifer C Dynamic Gene Regulatory Networks Drive Hematopoietic Specification and Differentiation Developmental Cell 36 572-587, 2016.
Zainul Abidin FN, Westhead DR Flexible model-based clustering of mixed binary and continuous data: application to genetic regulation and cancer. Nucleic Acids Res 45 e53-, 2017.
Shar NA, Vijayabaskar MS, Westhead DR Cancer somatic mutations cluster in a subset of regulatory sites predicted from the ENCODE data Molecular Cancer 15, 2016.
Goode DK, Obier N, Vijayabaskar MS, Lie-A-Ling M, Lilly AJ, Hannah R, Lichtinger M, Batta K, Florkowska M, Patel R, Challinor M, Wallace K, Gilmour J, Assi SA, Cauchy P, Hoogenkamp M, Westhead DR, Lacaud G, Kouskoff V, Göttgens B, Bonifer C Dynamic Gene Regulatory Networks Drive Hematopoietic Specification and Differentiation Developmental Cell 36 572-587, 2016.
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Career overview
Professor David R Westhead is a Professor of Bioinformatics at the University of Leeds, within the School of Molecular and Cellular Biology. He completed his MA and DPhil at the University of Cambridge and the University of Oxford, respectively, both in 1992. Following his studies, he undertook postdoctoral work at Zeneca plc, Proteus Molecular Design Ltd., and the European Bioinformatics Institute under the supervision of Professor Janet Thornton FRS. He was appointed as a Lecturer in Bioinformatics in 1998, became a Senior Lecturer in 2003, and was promoted to Professor in 2006. From 2011 to 2018, he served as the Head of the School of Molecular and Cellular Biology.
Research interests
Professor Westhead''s research focuses on prediction methods for biological problems using machine learning and statistical methods. The main application areas include genetic regulation and cancer, working with genome-scale data sets derived from various technologies, particularly high-throughput sequencing. The motivation is to address biological problems through data and computational methods, such as understanding the molecular networks underlying cancer and their relation to prognosis and therapy. Professor Westhead collaborates extensively with data-generating groups, including local partnerships with St. James University Hospital and national networks in haematological oncology. The research group is part of the Leeds Institute of Data Analytics (LIDA) and the Leeds Omics group. Current projects include targeting transfer RNA-derived fragments during KSHV infection and investigating virus manipulation of host non-coding RNA regulatory networks. The ultimate aim of the research is to contribute to stratified medicine by identifying key cancer-driving processes in individual patients and targeting these with specific therapies.
Bioinformatics
Computational Biology
Mathematical Biology
Statistics
Genetic Regulation
Cancer
Machine Learning
Genome Scale Data
High-Throughput Sequencing
Molecular Networks
Prognosis
Therapy
Haematological Oncology
Stratified Medicine
Data Analysis
Gene Expression
Transcription Factor Binding
Chromatin Modification
Blood Cell Development
Leukaemia
Lymphoma
Cancer Classification
Molecular Mechanisms
Treatment
Epigenetic Drugs
Precision Medicine
3D Genome Structure.
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