Biomedical Data Science & Machine Learning

Dr. Vikas Bansal

Research areas/focus

Genome annotation is a crucial component of any downstream analyses such as variation and assessing functionality of a sequence. Because of the general assumption that most “intergenic” DNA is non-functional, the effect of variations in the unannotated regions has been ignored largely. It is very likely that many functional features exist in these “intergenic” regions and their differential activity may explain disease phenotypes. Moreover, according to current human proteome, only ~1% of our genome encode for proteins, which work together to underlie most of the structure and function of our cells and organs. Thus ~99% of our non-coding genome comprises the critical information dictating the regulation of protein-coding genes during our life and development.

For instance, more than half of this non-coding DNA is derived from transposable elements (TEs), repetitive DNA segments that are capable of moving and replicating in the genome and has been associated in many human diseases. Notably, along with the gene expression, the activity of these non-coding regions is highly cell-type specific. Therefore, combining disease-specific mutation information with cell type-specific novel and known non-coding functional regions offers the potential to capture genetic complexity, the molecular basis of normal brain function, and the transition to neurological disease.

At DZNE, we aim to develop new computational methods to predict novel non-coding loci in the human genome with their cell type-specificity information and investigate the functional impact of mutations in these novel annotations and known regions in human brain disorders and specifically neurodegenerative diseases.

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