Oxford-BMS Research Fellow
I am a computational biologist. I apply computational approaches for the analysis of sequencing data generated by single-cell multi-omics technologies to understand normal and malignant cells in hematopoiesis.
I mainly focus on the single-cell transcriptome and epigenome analyses produced by different single-cell platforms (e.g. Target-Seq, 10x genomics) for studying the complexity of hematopoietic stem and progenitor cell subpopulations. My aim is to apply single-cell multi-omics combining with a novel development of computational and statistical methods including machine learning approaches to translational medicine challenges.
My current work focuses on the integration of multiple single-cell RNA and ATAC genomics datasets from Acute Myeloid Leukemia (AML) patients undergoing clinical trials. Integrative single-cell multi-omic data sets could help to identify distinct cellular compartments, and resolve tumor heterogeneity, providing insights into deregulated pathways, and transcriptional and epigenetic signatures of mutant cells. This could potentially lead to the discovery of new targets and therapies to address the unmet medical need of AML patients.
Heterogeneous disease-propagating stem cells in juvenile myelomonocytic leukemia.
Louka E. et al, (2021), J Exp Med, 218
Erratum: Functional annotation of human long noncoding RNAs via molecular phenotyping (Genome Research (2020) 30 (1060-1072) DOI: 10.1101/gr.254219.119)
Ramilowski JA. et al, (2020), Genome Research, 30
VALERIE: Visual-based inspection of alternative splicing events at single-cell resolution.
Wen WX. et al, (2020), PLoS Comput Biol, 16
Functional annotation of human long noncoding RNAs via molecular phenotyping
Ramilowski JA. et al, (2020), Genome Research, 30, 1060 - 1072
C/EBPα and GATA-2 Mutations Induce Bilineage Acute Erythroid Leukemia through Transformation of a Neomorphic Neutrophil-Erythroid Progenitor.
Di Genua C. et al, (2020), Cancer Cell, 37, 690 - 704.e8