We study genomic regulatory logic, chromatin dynamics and cellular information flow in both normal physiology and pathological 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 Disease Progression 
    • Computational methods for identifying TF direct targets (by integrating TF binding with gene expression: ChIP-seq & RNA-seq or microarrays) and studying their influence on disease phenotypes
    • Ex: Our study of the TP53 target gene networks in apoptosis and senescence, identified by integrating TF binding and gene expression changes:
    • Methods for Identifying upstream regulators of gene signatures (RNA-seq, microarrays)
    • Early Carcinogenesis of Renal cancer using single-cell omics (scRNA-seq, ATAC-seq) - Collaboration with the Vanharanta lab (MRC-CU)
  • Cancer Translatomics
    • Understanding translational potential through Ribosome profiling (Ribo-seq, TIS-seq, iCLIP) - Collaboration with Narita (CRUK Cambridge Institute) and Hodson labs (Stem Cell Institute)
    • Proteomics
  • Regulatory Dynamics of the Non-Coding (epi)Genome in Disease
    • Understanding combinatorial Histone and chromatin modifications (Histone ChIP-seq, ChIP-exo)
    • DNA Methylation based cancer detection (methylation arrays) - Collaboration with Massie lab
    • Regulatory potential of Enhancer-Promoter-Insulator interactions, Enhancer rewiring, chromatin accessibility, nucleosome dynamics and TF footprinting (ChIP-seq & ATAC-seq, scATAC-seq) - Collaborations with Freazza Lab (University of Cologne), Vanharanta lab (MRC-CU), Basu lab (Stem Cell Institute)
    • Enhancer Promoter Interaction prediction using Machine and Deep learning
  • 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 biomedical research.

  • Artificial Intelligence methods for integrative 'Omics':
    • Machine Learning & Deep Learning for regulatory (epi)genomics​
      • Piranha
    • Deep Learning for Biomedical Image analysis
    • linked Data, Knowledge Graphs and graph embedding based models for data integration, modelling and biomedical data mining
      • Senesceome-KG​
    • ML and DL models for early detection and diagnosis of cancer
      • EMethylNET
      • MethylBoostER
  • Biomedical Natural Language Processing
    • NLP for extracting gene regulatory relationships from literature (PubMEd)
  • Biomedical Data Integration
    • Methods for statistical & computational biomedical data integration​
    • Deep Neural Graphs for regulatory epi-genomics and pathway bioinformatics
    • Web technologies & Integrative data resources
      • INTERFEROME: IFN regulated target gene resource
      • ReMOAT: Microarray re-annotation resource
      • ISGverse: Updated and curated Interferon Stimulated Gene Resource
      • InterferonScape: IFN target gene identification (STAT and IRF targets) and analysis of IFN networks in disease.
  • Information Visualisation for Biomedicine
    • Use of JavaScript, Processing, and Shiny to develop bespoke interactive data visualization

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 disease processes - Senesceome-KG​, InterferonScape
    • Developing resources for better pathway bioinformatics and linked data integration using graph-based strategies - Collaboration with Pietro Lio 
  • 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