Bioinformatician · Computational Biology Researcher · MSc Biomedical Informatics
I am a computational biology researcher building cross-disciplinary expertise in transcriptomics, structural bioinformatics, and high-performance data pipelines. My work focuses on developing reproducible workflows across multiple domains of bioinformatics while strengthening both biological insight and computational rigor.
My interest in biotechnology started with a fascination for how rapidly the medical field was advancing, that science could fundamentally change how diseases are treated. I want to be part of work that creates real impact on people's lives by bridging computational tools with high-impact research.
- Protein structure trajectory modeling using deep learning-generated ensembles
- Comparative analysis of BioEmu structural ensembles vs. molecular dynamics simulations
- Building reproducible pipelines on Linux-based HPC environments
End-to-end RNA-seq analysis pipeline for NUDT21 knockdown including gene set enrichment analysis.
Key results
- 11,450 genes significant (FDR < 0.05)
- Ribosome biogenesis & RNA processing pathways strongly affected
- Results suggest that NUDT21 regulates core proliferative and RNA-processing programs
Germline variant discovery workflow following GATK Best Practices.
Key results
- Successful complete germline variant calling workflow using GATK4
- Generated high-confidence SNP and INDEL callsets from aligned WGS data
- Annotated variants using Funcotator to extract gene-level information
De novo genome assembly using short-read sequencing data followed by structural and functional annotation.
Key results
- A 3.45 Mb draft genome assembled across 100 contigs, consistent with expected genome size for C. thermocellum
- Prokka identified 2,980 coding sequences (CDS), 52 tRNAs, 4 rRNAs, and 1 tmRNA
- 738 high-confidence matches (24.77%) in BLASTP against Swiss-Prot
Trajectory inference of protein isoforms using RMSD-based clustering and structural similarity analysis. Comparative analysis between deep learning-generated structural ensembles (BioEmu) and molecular dynamics simulations to evaluate model credibility and accuracy.
Open to research roles, internships, and full-time positions in bioinformatics and computational biology.