Faculty Member

Profile

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Research Area

Bioinformatics, Genomics, Big data analysis, Machine Learning (ML), Deep Learning, Artificial Intelligence (AI), and Plant Biotechnology

Research Interests

In planta, we are exploring the generation and function of novel components (e.g., non-coding RNAs and genes) of genome regulatory circuits by using both computational and experimental approaches. For this purpose, we are using multi-omics datasets, and our choice of molecules includes Transfer RNA (tRNA)-derived non-coding RNAs (tncRNAs), ribosomal RNA-derived fragments (e.g. rRFs), long non-coding RNAs, Fusion transcripts, and novel gene family in legume crops. In addition to this, we are also developing tools and databases for plant genomics research.

Transfer RNA (tRNA)-derived non-coding RNAs (tncRNAs)

Non-coding RNAs (ncRNAs) are powerful regulators of gene expression at the epigenetic, transcriptional, and post-transcriptional levels in the living system. Transfer RNA (tRNA)-derived non-coding RNAs (tncRNAs) are distinct group of regulatory RNAs, have been reported in all three domains of life, derived from the endonucleolytic cleavage of precursor tRNAs (pre-tRNAs) or mature tRNAs. tncRNAs includes well-known shorter tRNA-derived RNA fragments (~12 to 30 nucleotides [nt]) popularly termed as tRFs or tDRs, and longer tRNA halves or tRHs (~30 to 40 nt). In planta, we are trying to understand their generation and function of tncRNAs including their expression in different tissues, and different stress conditions.

Fusion transcripts

Fusion transcripts, also known as "chimeric transcripts", can be generated because of chromosomal rearrangements at DNA level or by trans-splicing or intergenic cis splicing at RNA level. Gene fusion is believed to be a major factor for controlling morphology, physiology, and phenotypic character in plants as well as a major contributor for adaptive evolution. Fusion transcripts may code for proteins, or may act as long non-coding RNAs, and playing a major role in the genome regulation. We are exploring these novel molecules to understand their generation and function in plants with special focus on different traits for the development of improved verities.

Development of webservers, databases, and computational pipelines

We are analysing multi-omics datasets by developing our own pipelines/methodologies, and web-servers. We are also using Machine Learning, Deep Learning and Artificial Intelligence to decipher the biology of different biomolecules. Further, we are also presenting our results in form of user friendly databases.