I'm a Genomics Researcher
M.S. Bioinformatics @ Northeastern University | Bioinformatics Research Assistant @ Cox Lab, Brigham & Women's Hospital / Harvard Medical School | Specializing in multi-omics data analysis, spatial transcriptomics, and neurodegeneration research
From wet lab to computational biology
Earned B.E. in Biotechnology from Siddaganga Institute of Technology, building a strong foundation in molecular biology, genomics, and bioinformatics. Gained hands-on experience with PCR, gel electrophoresis, SDS-PAGE, microbiological screening, and biomaterials research.
Joined Theomics International as Data Scientist, managing diverse NGS analysis projects including RNA-Seq, WGS, miRNA, metagenomics, and ChIP-seq. Developed robust analytical pipelines and delivered actionable biological insights across 30+ projects.
Pursued M.S. in Bioinformatics at Northeastern University, deepening expertise in computational methods, statistics, and machine learning applied to biological data. Coursework spanned genomics, transcriptomics, data mining, and multi-omics integration.
Currently at Cox Lab (Brigham & Women's Hospital / Harvard Medical School), investigating the gut-brain axis in Alzheimer's disease through bulk RNA-seq analysis of microglial transcriptional changes. Developing WGS workflows for Bacteroides genomes and expanding into spatial transcriptomics.
Comprehensive expertise across the bioinformatics pipeline
DESeq2, edgeR, limma-voom for differential expression, pathway enrichment, and visualization
10x Visium analysis with Scanpy, Squidpy, spatial statistics (Moran's I), neighborhood enrichment
Seurat workflows, trajectory inference, cell-type annotation, integration methods
GATK, freebayes, SnpEff for variant detection, annotation, and functional impact prediction
Taxonomic profiling, functional annotation, diversity analysis for microbiome studies
Peak calling, motif analysis, chromatin accessibility assessment
DESeq2, ggplot2, limma, tximport, ComplexHeatmap for publication-ready analysis and visualization
Scanpy, Pandas, NumPy, Matplotlib, Seaborn for data manipulation and scientific computing
Bash scripting, command-line tools, HPC job scheduling (SLURM)
Data querying, clinical trial databases (AACT), Snowflake data warehousing
Scikit-learn, classification, clustering, ensemble methods for predictive modeling
Hypothesis testing, regression, survival analysis, experimental design
Nextflow, nf-core pipelines for reproducible, scalable analysis workflows
Docker, Conda for environment management and reproducibility
AWS, HPC clusters for large-scale data processing and analysis
Git, GitHub for code management, collaboration, and project tracking
IGV, UCSC Genome Browser for genomic visualization and exploration
Cytoscape, pathway databases (KEGG, Reactome) for systems biology
Brigham & Women's Hospital / Harvard Medical School β Cox Lab
July 2025 - December 2025 β’ Boston, MA
Theomics International Private Limited
August 2021 - July 2023 β’ Bengaluru, India
Computational biology research across neuroscience, cancer, and genomics
Problem: Understanding spatial organization of tumor, immune, and stromal cells in breast cancer tissue.
Solution: Built end-to-end spatial transcriptomics pipeline using Scanpy and Squidpy for 10x Genomics Visium data.
Identified and spatially validated tertiary lymphoid structures (TLS) using CCL19/CCL21/CXCL13/LTB chemokine signatures, Moran's I spatial autocorrelation (I > 0.4), and neighborhood enrichment analysis showing immune cell clustering patterns.
Problem: Predicting cervical cancer risk from clinical features with high class imbalance.
Solution: Constructed ensemble model combining logistic regression, decision trees, and random forests with comprehensive preprocessing.
Achieved improved predictive accuracy through F1-score optimization, handling class imbalance with SMOTE, and implementing hyperparameter tuning to enhance model generalization.
Problem: Identifying molecular mechanisms of Urolithin A therapeutic effects in epileptic models.
Solution: Applied high-throughput transcriptomics analysis with RNA-seq pipelines for QC, alignment, and differential expression.
Identified CG7768/PPIF-VDAC1 as key therapeutic targets and mapped biological networks revealing that UA treatment mitigates seizure-associated metabolic and synaptic dysfunctions through VDAC1-mediated pathways.
Problem: Understanding miRNA dysregulation in congenital heart disease (ASD, VSD, TOF).
Solution: Structured Nextflow-powered nf-core/smrnaseq pipeline to process smRNA-seq data from CHD samples.
Identified 295 differentially expressed miRNAs and built miRNA-gene-pathway networks revealing dysregulation in PI3K-AKT, HIF1, and FOXO signaling. Over 80% of computationally identified DE miRNAs were validated by qRT-PCR results.
Pathogens, 11(7), 810 (2022)
https://doi.org/10.3390/pathogens11070810Research Square (Preprint, 2024)
https://doi.org/10.21203/rs.3.rs-4987164/v1I'm open to bioinformatics research opportunities, collaborations, and discussions about computational biology, neuroscience, and omics data analysis.