My research interests focus on developing and implementing computational and statistical methods to identify molecular signatures associated with cancer initiation and progression based on high-throughput transcriptomics and genomics data.
I have broad interests in developing and implementing computational methodologies that can improve our understanding in molecular mechanisms involved in cancer initiation and progression. During my postdoctoral research at Yale School of Medicine, USA, I studied how cancer-associated mutations in splicing factors result in aberrant RNA splicing and gene expression in hematopoietic disorders such as, myelodysplastic syndromes (MDS) and acute myeloid leukemia (AML). Besides, I studied the role of musashi 2 and its RNA-binding targets in myeloid leukemia, employing integrated transcriptome-wide approaches to determine RNA-protein interactome and translatome profile.
I received my graduate training (PhD) at the University of Tuebingen, Germany. Here, I was trained in both lab-techniques and informatics of next-generation sequencing data. My graduate work focused on investigating the prevalence of alternative splicing and nonsense-mediated decay, and the underlying regulatory mechanisms in plants. During my early training in bioinformatics, I developed online tools based on GeSTer (Genome Scanner for Terminators) algorithm, that generates a comprehensive landscape of intrinsic transcription terminators (RNA secondary structures) across the whole prokaryotic genomes.
Currently at the Jackson Laboratory for Genomic Medicine, my research focuses on identifying splicing signatures associated with the dysregulated expression of splicing factors in breast and ovarian cancer. Here, my computational interests are in algorithm and pipeline development for processing the transcriptomics data derived from the cancer models (eg. cell lines, patient-derived xenograft mouse) and patients. Recently, I constructed a pipeline to determine three-dimensional chromatin structures from ChIA-PET (Chromatin Interaction Analysis with Paired-End Tag) sequencing data generated in the laboratory, and strive to further develop scalable methods on local clusters and google cloud machines.