Principal Scientist, Computational Biology

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For the past two decades, I have been engaged in addressing challenging computational problems in artificial intelligence and computational biology, particularly in cancer research. My research interests are at the intersection of cancer, functional genomics, and artificial intelligence, facilitated by the development of freely available software tools.

As the leader of a group of bioinformatics analysts at Cancer Research UK’s Cambridge Institute (CRUK-CI), at the University of Cambridge, I have been involved in hundreds of research projects covering a range of cancers and experimental modalities. I am focused on collaborations with biologists studying genome-wide transcriptional regulation – and dis-regulation – in a variety of cancers. My goal is to understand how changes in transcriptional regulation impact gene expression, enabling the characterization of specific cancer subtypes in order to predict prognosis and identify drug targets and potential; therapeutic agents.

I am interested in integrated analysis of a wide range of genomics data. While some genomic features, such as changes in copy number, can be predictive of transcription, we are far from being able to accurately predict transcript expression levels from examining genomes in isolation. This is especially true in cancer, where different gene expression patterns have been shown to be associated strongly with prognosis and therapeutic response. As a result, I am drawn to experiments that more directly measure active transcription in different cell types, using both bulk and single-cell RNA-sequencing, and integrating these with data from experiments targeting the factors that control transcription levels (notably DNA enrichment assays such as ChIP-seq). Central to this is the analysis of epigenomic data, including various aspects of chromatin state, such as open chromatin (ATAC-seq), histone modifications (methylation and acetylation), and chromatin conformation (HiC), as well as certain features of DNA (copy number alterations and methylation/hydroxy-methylation of cytosines).

The great challenge is to integrate these data modalities in order to model complex regulatory dynamics. I have obtained good results from integrative analysis in my collaborations with research groups studying a variety of cancers. The emphasis of my research interests moving forward is on developing new approaches to integrating analysis of cancer functional genomics data as the quantity, quality, and diversity of regulatory data expands.

More recently, I have been applying machine learning modelling methods to my studies of functional dis-regulation in cancer. It is only now that cancer datasets are beginning to obtain the scale required to realize the full power of deep learning techniques.

I am interested in establishing new collaborations with biologists exploring transcriptional regulation in cancer while continuing to develop new methods and tools for analysing complex, heterogenous functional genomics data. I envision the large-scale deployment of unsupervised deep-learning techniques on repositories of transcriptomic and epigenomic data to discover novel biological features useful to categorize cancer subtypes, predict prognosis, identify drug targets, and optimize the search for therapeutic agents.

Work address: 
Cancer Research UK Cambridge Institute University of Cambridge Li Ka Shing Centre Robinson Way Cambridge CB2 0RE

ZFTA-translocations constitute ependymoma chromatin remodeling and transcription factors.

E-pub date: 28 Feb 2021

Independence of HIF1a and androgen signaling pathways in prostate cancer.

E-pub date: 30 Apr 2020

RNA sequencing: the teenage years.

E-pub date: 30 Jun 2019

Hypoxia inducible factor 1 alpha confers androgen independence in prostate cancer

E-pub date: 01 Apr 2018

Corrigendum to “Integration of Copy Number and Transcriptomics Provides Risk Stratification in Prostate Cancer: A Discovery and Validation Cohort Study” [EBioMedicine 2 (9) (2015) 1133-1144].

E-pub date: 31 Mar 2017

Characterization of DNA-Protein Interactions: Design and Analysis of ChIP-Seq Experiments

E-pub date: 01 Aug 2016

No cell left behind: Residual ovarian spheroids drive recurrence and are sensitive to the pro-oxidant elesclomol

E-pub date: 01 Jul 2016

Choline Kinase Alpha as an Androgen Receptor Chaperone and Prostate Cancer Therapeutic Target.

E-pub date: 31 May 2016

Corrigendum: Progesterone receptor modulates ERα action in breast cancer.

E-pub date: 01 Oct 2015

Integration of copy number and transcriptomics provides risk stratification in prostate cancer: A discovery and validation cohort study.

E-pub date: 01 Sep 2015

Progesterone receptor modulates ERα action in breast cancer.

E-pub date: 16 Jul 2015

5-hydroxymethylcytosine marks promoters in colon that resist DNA hypermethylation in cancer.

E-pub date: 01 Apr 2015

Impact of artifact removal on ChIP quality metrics in ChIP-seq and ChIP-exo data.

E-pub date: 01 Aug 2014

Nuclear ARRB1 induces pseudohypoxia and cellular metabolism reprogramming in prostate cancer.

E-pub date: 17 Jun 2014

The ETS family member GABPα modulates androgen receptor signalling and mediates an aggressive phenotype in prostate cancer.

E-pub date: 01 Jun 2014

Regulation of the androgen receptor function by a metabolic kinase in prostate cancer.

E-pub date: 01 Nov 2013

GATA3 acts upstream of FOXA1 in mediating ESR1 binding by shaping enhancer accessibility.

E-pub date: 31 Jan 2013

Latent regulatory potential of human-specific repetitive elements.

E-pub date: 24 Jan 2013

The androgen receptor induces a distinct transcriptional program in castration-resistant prostate cancer in man.

E-pub date: 14 Jan 2013

Differential Oestrogen Receptor Binding is Associated with Clinical Outcome in Breast Cancer

E-pub date: 01 Aug 2012

Independence of repressive histone marks and chromatin compaction during senescent heterochromatic layer formation.

E-pub date: 27 Jul 2012

942 The androgen receptor induces a distinct transcriptional program in castration resistant prostate cancer in man

E-pub date: 01 Feb 2012

Differential oestrogen receptor binding is associated with clinical outcome in breast cancer.

E-pub date: 04 Jan 2012

The androgen receptor fuels prostate cancer by regulating central metabolism and biosynthesis.

E-pub date: 20 May 2011

BayesPeak–an R package for analysing ChIP-seq data.

E-pub date: 01 Mar 2011

Abstract 1714: Transcriptional networks downstream of the AR identify clinically relevant prostate cancer targets

E-pub date: 31 Mar 2010

Cooperative interaction between retinoic acid receptor-alpha and estrogen receptor in breast cancer.

E-pub date: 15 Jan 2010

BayesPeak: Bayesian analysis of ChIP-seq data.

E-pub date: 21 Sep 2009

Genome-scale validation of deep-sequencing libraries.

E-pub date: 31 Aug 2008

Piwi and piRNAs act upstream of an endogenous siRNA pathway to suppress Tc3 transposon mobility in the Caenorhabditis elegans germline.

E-pub date: 11 Jul 2008

Proceedings of the Fourteenth Annual Conference of the Cognitive Science Society

E-pub date: 01 Jan 1970

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