The Integrative Bioinformatics group provides visual analytics and machine learning solutions for multi-omics data integration and analysis towards the discovery of diagnostic and therapeutic targets and predictive models for haematological malignancies.
In our research group, we apply new single-cell techniques in individual or multi-omics format for understanding hematopoietic variability and identifying unknown or rare subpopulations in bone marrow (BM) and whole blood (WB). These techniques are also used to characterize cell diversity within tumours and the clonal evolution of haematological malignancies in samples from untreated and treated patients, providing valuable information for the diagnosis, prognosis, and future treatments, as well as explaining why current therapies may fail. Having access to the publicly available single-cell data and integrate them to our in-house data, we apply visual analytic and computational methods to investigate the transcriptome and epigenetic variabilities across different cell types in BM and WB.
Transposable elements (TEs), also known as "jumping genes", and their functional roles in haematological malignancies is the other main interest of the Integrative Bioinformatics group. We study the magnitude of TE-mediated aberrant gene expression in haematological malignancies with the goal of delineating the perturbed regulatory mechanisms or epigenetic pathways responsible for this phenomenon.
Teaching
7BBG2014 Bioinformatics, Interpretation and Data Quality in Genome Analysis
- Title: NGS Galaxy Workshop (14 hours workshop)
7BBGGMBE Molecular pathology of cancer and application in cancer diagnosis, screening and treatment
- Title: Chromosome Conformation Capture Technologies (1 hour lecture)
7MMOC003 Fundamentals of Translational Cancer Medicine
- Title: Human transposable elements and their role in disease (1 hour lecture)
Group lead
Senior Lecturer in Bioinformatics