Bioinformatics

Dr. Nagarjun Vijay analyzes large genomic datasets to understand the patterns of evolution and the processes driving these patterns. To answer questions related to this, he uses computational approaches to obtain new insights. How does the accumulation of genetic changes along the genome affect the phenotype? What is the role of changes in gene expression and the regulatory program in evolution? When during evolution did various novelties arise and diversify? Evolution of RNA modifications (like splicing and editing) provide interesting new insights about the genotype-phenotype link. He is pursuing such questions using computational approaches, including generating new datasets. In addition to this, he is focused on exploring new strategies and tools to analyze large-scale datasets (not necessarily genomic).

Dr. Ishaan Gupta works on developing novel experimental and computational tools to understand the nature of information encoding in the genetic material. Considering the abstraction that a cell is self perpetuating autonomous computing device; its Hardisk is the DNA, its RAM is the RNA and its interfaces are the proteins; his work mainly focuses on different modes of transcription; biological equivalent of loading a program from the Hardisk to the RAM; in order to understand how individual cell types work in concert to form complex human tissues and how these processes are regulated during the course of organogenesis and disease.

Dr. Vineet Sharma primarily focuses on Metagenomics, Bioinformatics and Systems Biology. Metagenomics has emerged as a culture independent approach to directly extract and sequence the microbes from their environment which cannot be cultured by conventional methods. He is carrying out interesting and challenging metagenomics projects at his lab. His current research interests are Metagenomic analysis of various environments such as human gut, soil, sediment, complete genome sequencing and analysis of novel bacteria, development of computational tools for metagenomic and genomic data analysis, metabiolic pathway reconstruction in newly sequenced genomes and human genome analysis.

Dr. Kushal Shah works on applying various techniques of signal processing and random processes to predict the origin of replication in DNA sequences. In particular, he and his collaborators have been able to predict the replication origin for the malaria causing parasite, P. falciparum, which has also been experimentally verified [PTI News].

Dr. Rati Sharma aims to use and curate public datasets to predict interactions between network components such as genes, proteins, promoters, etc. One of her recent projects has been to use a machine learning based approach to coarse-grain gene-gene interaction networks in order to find gene communities/hubs with most prominent inter-community interactions.