Hello, I'm Divya Ranjan Pradhan, a Bioinformatician and Computational Biology researcher with a strong interest in genomics, transcriptomics, machine learning, and bioinformatics tool development.
I hold a Bachelor's degree in Bioinformatics from BJB Autonomous College and a Master's degree in Bioinformatics from Odisha University of Agriculture and Technology (OUAT). My academic and research journey has focused on integrating biological sciences with computational approaches to address complex problems in life sciences.
During my Master's dissertation at the ICAR–Indian Institute of Agricultural Biotechnology (IIAB), Ranchi, I developed an artificial intelligence–based framework for the prediction and classification of plant disease resistance proteins. This project involved large-scale biological data curation, feature engineering, machine learning model development, and deployment of a user-friendly web server for the scientific community.
My technical expertise includes RNA-seq analysis, differential gene expression analysis, NGS data analysis, machine learning, scientific programming, Linux-based computational workflows, and bioinformatics database management. I enjoy developing computational solutions that make biological research more accessible, efficient, and reproducible.
Currently, I am working as a Young Professional-I at ICAR–IIAB, contributing to genomics and multi-omics research focused on identifying genes associated with drought and salinity tolerance in Dinanath Grass (Pennisetum pedicellatum).
My research interests lie at the intersection of bioinformatics, computational biology, genomics, transcriptomics, and machine learning. I am particularly interested in developing predictive models and bioinformatics tools that can accelerate biological discovery and support data-driven research.
Beyond research, I enjoy building scientific software, learning emerging technologies, and exploring innovative applications of artificial intelligence in biology. I strongly believe that curiosity, persistence, and continuous learning are the foundations of scientific progress.
If you would like to collaborate, discuss research opportunities, or connect professionally, please feel free to reach out via email or LinkedIn.
Next Generation Sequencing Technologies - Data Analysis And Applications, IIT Kharagpur (NPTEL)
Skills: Linux · Next-Generation Sequencing (NGS) · Bioinformatics · Data Analytics · Biological Databases · Sequence Analysis · R programming · Data Analysis · Shell Scripting
Bioinformatics for Biologists: Analysing and Interpreting Genomics Datasets, Wellcome Connecting Science
Skills: Linux · Next-Generation Sequencing (NGS) · Bioinformatics · Biological Databases · Nextflow · Bash · Data Analysis · Shell Scripting
Linux Fundamentals for Bioinformaticians, Udemy
Skills: Linux · Bash scripting · Bioinformatics · Bash · Command Line Interface · Shell Scripting
Development of web-based bioinformatics applications and analytical pipelines using Python, Streamlit, and modern computational frameworks to support biological research.
Analysis of genomic, transcriptomic, and protein sequence datasets using modern bioinformatics tools and computational approaches. Experience includes RNA-seq analysis, differential expression analysis, functional annotation, phylogenetics, and biological database integration.
Development and evaluation of machine learning models for biological sequence classification, feature engineering, predictive modeling, and data-driven biological research using Scikit-learn and PyTorch.
Processing and analysis of Next Generation Sequencing datasets including RNA-seq workflows, quality control, read mapping, differential expression analysis, and downstream biological interpretation.
Programming and automation using Python, R, Bash, and Linux environments for large-scale biological data processing, workflow development, and computational analysis.
I am currently expanding my expertise in advanced bioinformatics, machine learning, transcriptomics, and multi-omics data analysis. My current interests include single-cell omics, genome-wide association studies (GWAS), deep learning for biological data, and scalable bioinformatics workflow development using modern computational tools.