Students/Projects Trainees

Former Students/Projects Trainees

Knowledge Discovery in Disease Biology:
As more sophisticated technologies in life science/healthcare are constantly being developed, an enormous amount of data has been generated and deposited on various platforms. Looking from the perspective of a particular disease, these data appear to be the pieces of a broken puzzle. This poses a challenge for data scientists to put these parts together and draw conclusive information out of it and to put forward the potential therapeutic targets for the disease. For example, in order to understand the disease mechanism, the data from heterogeneous resources could be collected and analyzed in relation to the molecular pathways centralized to the disease. The behaviour of such pathways can be studied under normal vs diseased conditions for identifying the probable drug targets for the disease. The data resources that can be considered for such studies are genome-wide expression/mutation studies of patients and healthy controls, patients’ specific genetic mutations, expression of non-coding DNA elements, clinical characteristics, and integrated pathway information.
 
Understanding Sequence-Function Relationship in Proteins/DNA:
The behaviour of a protein inside or outside the cell is defined by its interaction with the elements present in the surrounding environment, which include small metabolites to the macromolecules such as RNA, DNA, or proteins. The interactions of a protein with these elements are determined by how it folds in 3-dimensional space, and this three-dimensional folding of a protein largely depends on the linear sequence of amino acids. In summary, it can be stated that the function of a protein is determined by its interaction, the interaction of a protein is determined by its structure and the structure of a protein is determined by its sequence. As determining the 3D structure of a protein is a challenging task (computationally as well experimentally), we deploy various ML-based to understand the molecular interactions of protein from its sequence properties. This has large application in terms of predicting potential interactions of a protein with that of small molecules (drug-like/metabolites) and macromolecules such as RNA, DNA, or proteins.

Neurological Disorders:
We are interested in investigating the mechanism and the molecular markers for neuronal senescence and age-associated neurodegenerative diseases. Currently, we are working towards the computational modelling of Parkinson’s disease from an integrated analysis of the GWAS, gene expression, proteome, and pathway data to explain the disease and non-disease states in terms of the gene/protein interaction networks. We are also working on the characterization of the role of Topoisomerase 2 beta (Top2b) and its associated genes in neuronal senescence by modulating the expression of the Top2b enzyme through genome editing techniques.

Genome Sequences:

7 Whole-genome sequences of Severe Acute Respiratory Syndrome Coronavirus 2 isolate (SARS-CoV-2) from Hyderabad, India#

 MT415320.1; MT415321.1; MT415322.1; MT415323.1; MT477885.1; MT457402.1; MT457403.1

Tools and Databases:

1. RTGR-iTGV: Induced Tomato genomic variation database#: http://psd.uohyd.ac.in/itgv/
2. RTGR-TGV: Tomato genomic variation database#: http://psd.uohyd.ac.in/tgv/

Microarray Datasets:

1. E-MTAB-3977 - Transcription profiling by array of Human SUP-T1cells infected with HIV and/or treated with curcum: 
   https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-3977/
2. E-MTAB-4552 - Expression profiling of cerebellar granule neurons from Rattus norvegicus cultured for four week: 
   https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-4552/

#Work in collaboration