Functional Area

miRNA Biology:

miRNAs are ~22 nucleotides short RNAs (microRNAs) control the gene expression. They are generated through the canonical biogenesis pathway from long RNA transcript (pri-miRNA) to hairpin stem-loop precursor(pre-miRNA), and finally a mature miRNA duplex. One of the strands from this duplex is loaded into Argonaute (AGO) protein to form AGO-RISC (RNA Inducing Silencing Complex). Gene silencing is done by AGO-RISC complex that binds to 3' UTR region of mRNA which represses the gene expression or degraded the mRNA.

miRNAs are tissue/condition/stage-specific and their expression depends upon the molecular responses in the system which can switch on/off gene expression to regulate biological functions. Also, miRNAs play an important role in the growth and development of an organism. Alteration in miRNA expression causes several adverse effects which could lead to abnormal/disease conditions in animals as well as plants. So, studying the biology of this short RNA molecule (miRNAs) is very important to provide a potential marker that could enhance the life of human health as well as the quality of crop plants.

Regulomics:

Initially, Genomics have been started a long ago to study molecular responses in an organism system. The emergence of high-throughput data generation techniques was able to rise sudden growth in genomics. Advance in Next-generation sequencing (NGS) technologies were provided a milestone after the publishing of the first draft of the human genome. After this, NGS is continuously used for data generation and available in various platforms like RNA-Seq, Whole-genome sequencing (WGS), target sequencing, etc. Whole-genome sequencing technology gradually increased the importance of epigenomics and functional genomics as well as regulomics to study gene regulation beyond the traditional approach of transcription to translation. The non-coding region plays an important role in the regulation of genome integrity utmost level in the genome organization.

Big-data data handling, management, and analysis:

The deluge of high-throughput data from genomics is continuously required up-to-date computational methods to low down the exerting pressure of downstream analysis. From the past few years, biological big-data is increasing exponentially from different biomedical/biological experiments and doubling every year. It is quite challenging to gain a profound insight in terms of biological aspects from a large amount of big high-throughput data. To derive a piece of meaningful information from a single study data is sophisticated and manageable, but it is quite difficult for large-scale data through the traditional method. It is necessary to tackle and provides solutions for handling, gathering & manipulation, analyzing and interpreting important biological insights from such big-data.

The advances in DNA and RNA sequencing is generating data at faster rate with high coverage and cost-effective. It is helpful to study SNP variations across the genome, transcriptome and epigenomic profiling. In addition to this technology, a new technique emerges called single-cell RNA-Seq (scRNA-Seq) generate large cell specific sequencing data. Such types of big-data provide the platform to explore the human genome genetic variation in large population, association studies of the gene to disease and comparative genomics. The varieties of work was studied related to humans from all over the world due to its eager need towards treatment for human diseases as well as the development of a large number of tools/software and databases for the same.

Some important skills and background about me:

As I am working as a computational biologist and bioinformatician in the area of Plants and Animals.

My focusing areas are Genomics, Epigenomics, regulomics.

In genomics, I have done NGS data handling and work in RNA-seq data and small RNA-seq, methylation data, Chip-seq.

Database development and Big-data handling and analysis.

Programming, statistical analysis, Neo4j and Hadoop cluster management and Hadoop programming in pyspark.

Detailed CV is available below link