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RESEARCH
GENOMIC ARCHITECTURE & REGULATORY DYNAMICS
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:
DATA SCIENCE & AI
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Dynamics of Homeostatic Dysregulation Driving Disease Progression
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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
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Ex: Our study of the TP53 target gene networks in apoptosis and senescence, identified by integrating TF binding and gene expression changes:
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Methods for Identifying upstream regulators of gene signatures (RNA-seq, microarrays)
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Early Carcinogenesis of Renal cancer using single-cell omics (scRNA-seq, ATAC-seq) - Collaboration with the Vanharanta lab (MRC-CU)
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Cancer Translatomics
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Understanding translational potential through Ribosome profiling (Ribo-seq, TIS-seq, iCLIP) - Collaboration with Narita (CRUK Cambridge Institute) and Hodson labs (Stem Cell Institute)
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Proteomics
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Regulatory Dynamics of the Non-Coding (epi)Genome in Disease
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Understanding combinatorial Histone and chromatin modifications (Histone ChIP-seq, ChIP-exo)
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DNA Methylation based cancer detection (methylation arrays) - Collaboration with Massie lab
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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)
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Enhancer Promoter Interaction prediction using Machine and Deep learning
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Chromatin Dynamics
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Transcriptional regulation via long-distance chromatin interactions and enhancer-promoter looping (Hi-C, CHi-C and scHi-C)
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Functional consequences of dynamic changes in chromatin 2D/3D structure: Hubs, Loops, TADs and A/B compartments (Hi-C)
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Functional genomics of DNA structural features such as iMotifs, G-quadruplexes, D-loops and R-loops
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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.
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Artificial Intelligence methods for integrative 'Omics':
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Machine Learning & Deep Learning for regulatory (epi)genomics
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Piranha
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Deep Learning for Biomedical Image analysis
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linked Data, Knowledge Graphs and graph embedding based models for data integration, modelling and biomedical data mining
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Senesceome-KG
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ML and DL models for early detection and diagnosis of cancer
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EMethylNET
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MethylBoostER
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Biomedical Natural Language Processing
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NLP for extracting gene regulatory relationships from literature (PubMEd)
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Biomedical Data Integration
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Methods for statistical & computational biomedical data integration
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SpiderSeqR
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COBRA-tft
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Deep Neural Graphs for regulatory epi-genomics and pathway bioinformatics
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Web technologies & Integrative data resources
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INTERFEROME: IFN regulated target gene resource
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ReMOAT: Microarray re-annotation resource
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ISGverse: Updated and curated Interferon Stimulated Gene Resource
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InterferonScape: IFN target gene identification (STAT and IRF targets) and analysis of IFN networks in disease.
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Information Visualisation for Biomedicine
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Use of JavaScript, Processing, and Shiny to develop bespoke interactive data visualization
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Biomedical Complex systems
We develop Systems Biology approaches to decipher the complexities of Immunity and Cancer.
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Systems Immunology
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Complex Systems analysis methods to understand immune and inflammatory responses in cancer and other diseases
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Immune evasion mechanisms and modulation of adaptive immune responses
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Pattern/Danger Recognition systems, Interferon and Interleukin pathways and networks
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Metabolic remodelling, immuno-metabolic perturbations, secretomes, and micro-environment Interactions
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Network Biology
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Modelling cellular interactions and information flow
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Reverse engineering gene regulatory networks
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Building integrative and predictive regulatory models, knowledge graphs and network maps of disease processes - Senesceome-KG, InterferonScape
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Developing resources for better pathway bioinformatics and linked data integration using graph-based strategies - Collaboration with Pietro Lio
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Cancer Genomics & Computational Modelling
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Data-driven quantitative (Mathematical/Statistical) modelling and inference
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Impact of mutations (CNVs) and genetic aberrations (CNAs) on cancer pathways and networks (Whole Genome or Exome Sequencing)
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Drug responses in cancer
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