• Conducted comprehensive whole genome sequencing analyses to accurately pinpoint gene insertion sites.• Performed intricate single-cell data analyses to uncover and validate critical biomarkers, enhancing the understanding of cellular heterogeneity and disease mechanisms.• Executed differential gene expression and pathway enrichment analyses to elucidate the systemic effects of gene knockout, providing insights into the resultant pathway-level alterations.• Analyzed ChIP-seq, ATAC-seq, and RNA-seq datasets to investigate the regulatory influence of transcription factor binding on pathway modulation, contributing to the understanding of gene regulatory networks.• Analyzed long-read sequencing data to identify and characterize alternate transcripts induced by gene knockout.• Applied protein modeling, docking, molecular dynamics simulations, and machine learning to study protein-receptor binding and allosteric mechanisms, providing insights into protein function and its potential therapeutic targets.

As a Bioinformatics scientist, I have a strong background in performing protein structural analysis and designing computational methods for Next-Generation Sequencing (NGS) data analysis. Achievements1. Coauthor in one of the posters on Evolution of AAV capsid2. Coauthor of two provisional patentsExperience:1. Lead computational protein structural analysis, resulting in the construction of libraries utilized by bench scientists to evolve proteins for enhanced efficiency.2. Developed Machine learning workflows to enhance selection efficiency of protein variants, which showed an increased high throughput assay success rate when compared to the standard workflow.3. Designed computational workflow to improve gene therapy platform at Codexis.4. Developed and optimized computational methods and workflows to assess IonTorrent and MiSeq NGS data quality, enabling the identification of key mutations and associated barcodes, accompanied by statistical calculations to evaluate their biological significance.5. Coded a python workflow to align brain slice images and extract essential biological information, streamlining data processing and analysis.6. Analyzed publicly available NGS data to provide crucial insights for in-house decision-making on project goals, facilitating informed strategies and resource allocation.7. Coded and evaluated the workflow to Integrate new protein modeling and Hidden Markov Model-based computational methods into Codexis Codevolver technology, resulting in significant performance enhancements.

Bioinformatics method’s developed during my PhD (*Machine learning models)1. DCI - https://dci.bb.iastate.edu/ (Predicts dynamic communities in protein)2. APOP - https://apop.bb.iastate.edu/ (Predicts allosteric pockets in protein)3. Packman - https://packman.bb.iastate.edu/ (Protein hinge prediction)4. HDANM - https://hdanm.bb.iastate.edu/ (Predicts global motions of protein)5. MutDDG* - https://mutddg.bb.iastate.edu/ (Predicts stabilizing mutations using protein sequence data)

Constructed a computational workflow to trace molecular changes associated with Brain evolution in mammals.

1. Constructed NGS workflow in python to process cancer patient NGS data to identify clinically relevant variants.2.Applied Deseq2 and EdgeR perform gene differential expression analysis.3. Create a database which contains all human variant dataset, multiple Ensembl annotations, and clinically relevant data associated with variants.