We study genomic regulatory logic, chromatin dynamics and cellular information flow in both normal cells and during carcinogenic perturbations through the joint integration of data-sets from a variety of complementary biological layers generated by high-throughput technologies:


  • Dynamics of Homeostatic Dysregulation Driving Carcinogenic Growth and Progression
    • Computational methods for identifying TF direct targets (by integrating TF binding with gene expression: ChIP-sep & RNA-seq or microarrays) and studying their influence on carcinogenic phenotypes.
    • Our study of the TP53 target gene networks in apoptosis and senescence, identified by integrating TF binding and gene expression changes:
    • Identifying upstream regulators of gene signatures (RNA-seq, microarrays)
    • Cancer single-cell transcriptomics (scRNA-seq
  • Cancer Translatomes 
    • Understanding translational potential through Ribosome profiling (Ribo-seq, TIS-seq, iCLIP)
    • Proteomics
  • Regulatory Dynamics of the Non-Coding (epi)Genome in Carcinogenesis
    • Combinatorial Histone and chromatin modifications (Histone ChIP-seq, ChIP-exo)
    • DNA Methylation (methylation arrays)
    • Regulatory potential of Enhancer-Promoter-Insulator interactions, chromatin accessibility, nucleosome dynamics and TF footprinting (ChIP-seq & ATAC-seq, scATAC-seq)
    • Transcriptional activity  using (GRO-seq) and (PRO-seq)
    • ncRNA and the modulation of transcription (lncRNA-seq, small RNA-seq)
  • Chromatin Dynamics
    • Transcriptional regulation via long-distance chromatin interactions and enhancer-promoter looping (Hi-C, CHi-C and scHi-C)
    • Functional consequences of dynamic changes in chromatin 2D/3D structure: Hubs, Loops, TADs and A/B compartments (Hi-C)
    • Functional genomics of DNA structural features such as iMotifs, G-quadruplexes, D-loops and R-loops
    • Effects of DNA-RNA hybrid structures such as R-loops through modulation of transcriptional regulation, and their contribution to genome instability and  replicative stress (DRIP-seq)
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We utilize the latest advances in data science and artificial intelligence and apply these to cancer research.

  • Artificial Intelligence methods for cancer 'Omics':
    • Machine Learning & Deep Learning for regulatory & cancer genomics
    • Transfer learning & Multi-task learning
    • Deep Learning for Biomedical Image analysis
    • Deep Neural Networks for early detection and diagnosis of cancer
      • M2C2
  • Biomedical Natural Language Processing
    • NLP for gene regulation
  • Biomedical Data Integration
    • Methods for statistical & computational biomedical data integration
    • Linked Data, Knowledge Graphs and Deep Neural Graphs
    • Web technologies & Integrative data resources
      • INTERFEROME: IFN regulated target gene resource
      • ReMOAT: Microarray reannotation resource
  • Information Visualisation for Biomedicine
    • Use of JavaScript, Processing, and Shiny to develop bespoke interactive data visualizations

Biomedical Complex systems

We develop Systems Biology approaches to decipher the complexities of Immunity and Cancer.

  • Systems Immunology
    • Complex Systems analysis methods to understand immune and inflammatory responses in cancer and other diseases
    • Immune evasion mechanisms and modulation of adaptive immune responses
    • Pattern/Danger Recognition systems, Interferon and Interleukin pathways and networks
    • Metabolic remodelling, immuno-metabolic perturbations, secretomes, and micro-environment Interactions
  • Network Biology
    • Modelling cellular interactions and information flow
    • Reverse engineering gene regulatory networks
    • Building integrative and predictive regulatory models, knowledge graphs and network maps of carcinogenic processes
    • Developing resources for better pathway bioinformatics and linked data integration using graph-based strategies
  • Cancer Genomics & Computational Modelling
    • Data-driven quantitative (Mathematical/Statistical) modelling and inference
    • Impact of mutations (CNVs) and genetic aberrations (CNAs) on cancer pathways and networks (Whole Genome or Exome Sequencing)
    • Drug responses in cancer