Master of Science
Bioinformatics
Boston University
Bachelor of Science with Honors
Biotechnology
M. S. Ramaiah University of Applied Sciences
Education
Boston University
M. S. Ramaiah University of Applied Sciences
Introduced core computational problems in bioinformatics, including sequence analysis, molecular evolution, and gene network analysis. The course combined theoretical concepts with hands-on labs using biological databases and bioinformatics tools to analyze large-scale genomic datasets.
Sequence analysis • Biological databases • Comparative genomics • Computational biology workflows
Developed programming and data analysis skills in Python for biomedical research applications. Covered data manipulation, visualization, and introductory machine learning methods for analyzing biological datasets.
Python • Data analysis • Machine learning basics • Scientific computing
Focused on applied data analysis in R for biological research. Emphasized statistical computing, data visualization, and reproducible analysis workflows used in modern bioinformatics studies.
R programming • Statistical analysis • Data visualization • Reproducible research
Analyzed and critically evaluated recent high-impact research papers demonstrating how bioinformatics approaches are used in clinical and translational research. Included peer discussions, research presentations, and a term paper.
Scientific literature analysis • Translational genomics • Research communication
Explored next-generation sequencing (NGS) technologies and computational methods for analyzing large-scale genomic datasets. Projects included reproducing published RNA-seq, ChIP-seq, and scRNA-seq analyses while building reproducible pipelines.
RNA-seq • ChIP-seq • scRNA-seq • Nextflow • Workflow reproducibility • Linux/HPC
Provided the biological foundation required for computational genomics by examining the structure and function of DNA, RNA, and proteins. Emphasized quantitative perspectives on cellular and molecular processes.
Molecular biology • Protein structure • Gene regulation • Biochemical pathways
Covered the design and implementation of relational databases for biological data. Developed database schemas, wrote complex SQL queries, and explored integration of biological databases for genomic applications.
SQL • Relational database design • Data integration • Biological databases
Applied statistical genetics methods to analyze GWAS data. Performed quality control, population stratification correction, association testing, and polygenic risk score analysis using specialized genetics software.
GWAS • Population genetics • PLINK • Statistical genetics • Linux/HPC
Completed a 400-hour research internship at the National Cancer Institute applying bioinformatics methods to real-world biological datasets. Concluded with a written report and scientific poster (NIH Summer Poster Day 2024) presenting computational analyses.
Applied bioinformatics • Research communication • Data analysis
Designed and implemented relational databases using Entity-Relationship modeling and SQL. Culminated in a full database system project focusing on schema design, normalization, and query optimization.
Database design • SQL • ER modeling • Data management