Research Fellow, Department of Oncology
- Understanding the molecular drivers of tumour development and progression through application of computational methods to integrate cancer genomics datasets.
- Novel cancer biomarker and drug target identification and characterization with focus on long non-coding RNA and the tumour microenvironment
I am actively seeking cross-disciplinary collaborations to meet the “big data” challenges presented by the above
- Execution of Next Generation Sequencing pipelines on large cancer datasets
- Interpretation of complex cancer datasets
- Multi-dimensional/platform data integration
- Novel cancer target/biomarker discovery, validation and disease positioning.
- Application of machine learning approaches to biological problems
My research interests are primarily in the area of bacterial genomics.
Current research topics include:
- Comparative genomics and phylogenetics of bacterial pathogens, particularly E. coli and Salmonella
- Use of transposon insertion sequencing methods (TraDIS/TnSeq/HITS/InSeq) to identify essential bacterial genes and genes important for survival in particular environments such as during infection of a model system
- Development of user-friendly software tools and online resources for exploring data from -omics technologies. Examples include coliBASE, Xbase and the recently-funded MicrobesNG.
Bacterial Genomics, High-throughput sequencing, Phylogenetics, Transposon Insertion Sequencing (TraDIS/TnSeq/HITS/InSeq), Genome assembly, Variant detection, RNAseq
My interest is using next generation sequencing technologies to understand the genetic basis of disease. Next generation sequencing enables us to detect nearly all the genetic variants in an individual but there are hundreds of thousands of these. The difficulty is in pinpointing which of these contribute to a specific disease. My research explores ways to identify processes that are disrupted in disease from moderate-sized patient groups, assuming that individuals have different variants but that affect a common pathway. I am particularly interested in unravelling the genetic causes of schizophrenia.
- Large-scale human population genomics
- Clinical genomics
- Variant calling from DNA-seq with a reference genome
- Quality Control
- Statistical and mathematical modelling including generalised mixed linear models and population dynamics
- Evolutionary biology
Bioinformatics Core Director, University of Sheffield
- High-throughput technologies such as next generation sequencing (NGS) can routinely produce massive amounts of data that can be used for tasks such as identifying biological samples with aberrant expression patterns or allow us to describe all variants in a genome. However, such datasets pose new challenges in the way the data have to be analyzed, annotated and interpreted which are not trivial and are daunting to the wet-lab biologist. My interests lie in making the analysis and results of high-throughput data accessible to the non-bioinformatician; via specialised training courses and by developing computational pipelines and workflows.I am currently exploring technologies that facilitate reproducible research and promote an open attitude to scientific research, and endeavour to make my talks, code, and analysis available whenever possible.
- R / Bioconductor
- Reproducible Research
- Organising, developing and delivering Bioinformatics training courses
- Analysis of microarray and RNA-seq data
- Variant-calling from DNA-seq data
- Developing pipelines / workflows for the analysis of NGS data
My research centres on identifying and addressing biology related to computational, population, and evolutionary genetics. Current research topics include:
- Identify and measure interbreeding sites between human and ancient hominins.
- Develop tools to infer the geographical origins of human population
- Develop tools to improve personalised medicine
- Develop theoretical framework, tools, and animal models to improve our understanding of complex diseases.
Molecular Evolution, Population Genetics, Genetic epidemiology, Biogeography, Genomics, Paleogenomics, and Epigenetics.
I am interested in understanding normal and and abnormal development.
- Can we find omics profiles that diagnose or predict disease progression?
- Can we integrate model organism, cell line and human cohort omics surveys to yield new understanding of normal and disease processes?
- How can we develop and exploit next generation sequencing to inform our understanding of biology?
- How to integrate models of function and disease across networks of interaction to yield targets for intervention?
Research opportunities include:
- Simplification of molecular profiles to allow integration across biological processes leading to discovery of key processes and targets involved in normal and diseased systems.
- Interpreting molecular profiling of gene high throughput genome sequencing/RNAseq analyses
- Integration and data interpretation challenges across human, model organism and cell line systems
- Building network models of specific diseases that collate and interpret integration across studies.
- Development of tools to group experiments based upon shared phenotypic and molecular attributes. Providing a sharing environment for data to share functional biological models
Reducing the gap between primary data and its interpretation. We perform computational biology using a systems approach to deliver interventions through understanding of processes underlying disease
Research Fellow in Computational Biology, Department of Computer Science
Having years of expertise in bioinformatics and systems biology, my research interests focus on developing computational tools, pipelines, workflows and systems biology models in omics research. I am currently working for AirPROM (€12m EU FP7 Project), which involves the building of individual-based models of cellular interaction, with the aim of using genomic data as the basis for the individual-based model of the cell. My main responsibilities are the analysis and interpretation of high-throughput biological data, with the aim to produce feasible and robust hypothesis for a deeper understanding of the biological systems under study.
- Quantification and inference of gene expression levels using probabilistic models;
- Inference of gene networks using regulatory data and gene expression data;
- Integrated approaches for the analysis of Next Generation Sequencing data.
- Teaching Modelling and simulation of Natural Systems
To understand and define the source of uncertainty in quantitative biology is a key aspect for improving sensitivity and accuracy in the analysis of high throughput genomic data. My research interests focus on developing computational tools, pipelines, appropriate experimental designs and protocols to assist in improving accuracy and sensitivity in the analysis of biological data.
- Propagation of uncertainty, associated to low-level data, in downstream analysis of microarray data
- Quantification and inference of gene expression levels using probabilistic models
- Inference of gene networks using regulatory data and gene expression data
- Integrated approaches for the analysis of Next Generation Sequencing data
My research is predominantly focused on different aspects of plant-microbe Interactions. Coming from a molecular biology background, I process sequencing data sets and metabolomic data to identify genes, genomic features and metabolic signatures that are involved in the molecular-genetic basis underpinning pathogen virulence and plant immunity. Examples include identifying disease-promoting effectors of Bremia lactucae by de novo assembly of RNAseq from mixed Bremia/host tissue, as well as improving the genome sequence assembly and annotation of Botrytis cinerea. My current project investigates the molecular and epigenetic basis of the onset and long-term (transgenerational) maintenance of plant immune priming.
Next Generation Sequencing data analysis, Genome assembly, Epigenetics, Plant-Microbe Interactions, non-model systems.
Lecturer in Bioinformatics, Department of Molecular Biology and Biotechnology
How do cells integrate information to make decisions about what genes should be expressed at a given time and in a given place? How do these processes malfunction to produce disease states? The correct regulation of gene expression is essential for the proper functioning of the cell, and incorrect regulation of genes is central to the mechanisms of many diseases. My interests rest in understanding how the many levels of eukaryotic gene regulation work together to perform these functions, using computational and functional genomics tools.
Examples of recent projects:
- The Polycomb Repressive Complex is targeted to CpG islands by KDM2b
- Reconfiguration of the 3D structure of the genome by a disease causing SNP
- Activation of the WNT pathway by androgen ablation therapy in prostate cancer.
- Association of RNA binding actors with coding and non-coding transcripts
Genome-wide measurement of gene expression at all stages of regulation:
- Transcription (ChIP-seq, ChIP-exo, 4/5/HiC)
- RNA processing (HITS-CLIP/iCLIP)
- RNA stability (RNA-seq, small RNA-seq)
- Translation (Ribosome Profiling)
Lecturer in Genomic Medicine, Department of Neuroscience
We focus on translating the complex patterns of genomic data generated in the lab to enable the development of personalized medicines that can benefit patients suffering from complex diseases. Of computational interest, this involves integrating genomics information of various data types in order to build algorithms that predict clinical outcome and identifying genetic biomarkers through feature selection. Of biomedical interest, we survey the genomic landscape of disease subtypes, as well preclinical models, to better understand how we can stratify patients for different treatments. Both of these aims enable us to work towards the ultimate goal of developing data driven approaches for personalizing medicine.
Examples of recent projects:
- Prognostic impact of genomic instability and immunogenicity in cancer and pulmonary diseases
- Personalizing drug combinations based on genomic features
- Probabilistic modelling of drug response
- Assessment of variant calling metrics for clinical diagnostics (eg. 100K Genome Project)
- Molecular stratification of dementia and Alzheimer’s cases
- Genomic applications in medicine (I co-lead the MSc in Genomic Medicine)
- Statistical inference and machine learning prediction for pharmaco-genomic applications
- Performance assessment of diagnostic assays
- Integrative genomics for patient stratification