Research

Transcriptomic data analysis, MD simulation run and Evolutionary computation can be performed in the form of research collaboration. Interested researcher can contact us (asom [at] allduniv.ac.in).

Networks and Systems Biology

Cancer Bioinformatics: We are dedicated to identifying prognostic and diagnostic biomarkers involved in various cancer formation and progression. Through our bioinformatics expertise in large-scale transcriptomic data handling and network biology analysis, we are focused on exploring both coding and non-coding genes and their underlying mechanism in formation and development of Gynecological and Lung cancers.  Especially we intend to unravel the shared and unique molecular mechanisms of different types of Gynecological and Lung cancer.

Stem Cell Bioinformatics: We are involved to understand the underlying mechanisms of human pluripotency by combining network biology and integrative bioinformatics approach. We intend to bring a consensus on how pluripotency is established and maintained by analyzing RNA-seq data. So far, we have uncovered the genes those are critical for establishment and maintenance of pluripotency in human, and decoded molecular markers and transcriptional circuitry of naive and primed states of human pluripotency. Further we envision to decipher the mRNA-lncRNA-miRNA interaction landscape in human pluripotency.

Host-Pathogen Protein-Protein Interactions

We are focused on the construction and analysis of protein–protein interactions between human and pathogenic bacteria, utilizing large-scale experimentally reported inter-species and intra-species interactome data, using machine learning and deep learning methods that may provide crucial insights into the mechanisms of pathogenicity and can help to develop novel therapeutic targets.

Computational Genomics and Structural Bioinformatics

To study riboswitches (non-coding regulatory RNAs) as potential drug target using computational and structural bioinformatics approaches. The ongoing research include identification, distribution, modeling of structures and manipulation of riboswitches in some prokaryotic genomes, so that they can be identified and established as potential drug targets thus playing a pivotal role in therapeutic applications. Further, we intend to find the insights into the conformational changes and binding mechanism of riboswitch via molecular dynamics simulations and inhibitor finding for the specific riboswitch.

Evolutionary Bioinformatics

Wolbachia is a genus of gram-negative bacteria which infects arthropod species. We intend to study the evolution of Wolbachia supergroups using computational evolutionary methods. The study will help to understand the compatibility of different species of Wolbachia with their host. Reconstructing gene and species phylogenies will suggest their current existence and will help to understand the participation of those genes during speciation according to their host. We also try to identify the role of horizontal gene transfer(HGT)/ recombination events in Wolbachia  supergroup evolution.