I’m Abhijit, a bioinformatician working at the intersection of clinical genomics, computational biology, and software. I turn complex sequencing data into interpretable insights and practical tools.
My goal is to bridge biological complexity and usable technology, from robust analysis workflows to tools that help people make better sense of genomic evidence.
At Genomiki Solutions, I work across diagnostic NGS analysis, clinical variant interpretation, microbial and transcriptomic analysis, and development of bioinformatics products and workflows. I enjoy connecting the analytical details with the bigger clinical or research question.
I hold an MSc in Bioinformatics from Chandigarh University. During my dissertation at CSIR–IHBT, I explored gene co-expression and Bayesian network approaches to understand gene relationships in Arabidopsis thaliana.
Explore the domains I work in and the kinds of problems I like solving. These are focus areas, not a claim that every item is a separate public repository.
Evidence-driven SNV and indel prioritization, phenotype matching, annotation, and structured interpretation support using clinical knowledge resources.
Exploring LLM-assisted workflows that organize genomic evidence and references while keeping reviewability and expert oversight central.
Investigating copy-number profiles and genomic scar signals, with attention to panel design, reference baselines, quality control, and interpretation limits.
Computational workflows for microbial sequence analysis and antimicrobial resistance research, from data processing to interpretable results.
Gene expression analysis, co-expression modules, functional enrichment, and Bayesian network approaches for exploring biological relationships.
Pipeline automation and report-oriented analysis for microbiome workflows, connecting computational outputs with clear, useful interpretation.
Want to see code? Browse my public repositories ↗
A practical mix of command-line workflows, statistical analysis, genomic knowledge resources, and reproducible software practices.
The questions I’m interested in taking further through hands-on experiments, reading, and collaborative projects.
How can models help with variant evidence, structural variation, and more useful genomic representations?
Expanding computational biology skills toward cellular states, tissue context, and complex datasets.
Building evidence-grounded support tools with transparent sources and a human expert in the loop.
Turning repeatable analysis tasks into clear, maintainable workflows that other researchers can understand.
I’m open to connecting with bioinformaticians, researchers, and builders working on genomics, computational biology, and thoughtful applications of AI.