Curriculum

The Bioinformatics MS Program equips students with the computational, statistical, and experimental skills needed to analyze complex biological data. With a focus on genomics and transcriptomics, the curriculum integrates molecular biology, computer science, and data science, including machine learning.

Students develop computational expertise in working with key bioinformatics frameworks and tools for data wrangling, statistical analysis, visualization, reproducible science tools and practices, and machine learning:

  • Programming languages: R, Python, SQL, shell scripting
  • Computational Environments: UNIX/Linux, conda, pip, containerization, high performance cluster (HPC)
  • Development Environments: RStudio, VSCode, Computational notebooks (Jupyter, RNotebooks), Relational databases/SQL
  • Reproducibility Practices: Workflow managers (Nextflow/snakemake),  git/GitHub
  • Science Communication: Data visualization (matplotlib/ggplot), RMarkdown, RShiny, Elementary web application programming
  • Sequence Analysis: Next generation sequence alignment (bwa/bowtie/STAR), BLAST, Multiple Sequence Alignment, Sequence motif analysis, Phylogenetic algorithms

Students gain hands-on experience with modern genomics datatypes, with a focus on next generation sequencing (NGS), including:

  • Bulk & single cell RNA-Seq
  • Differential expression analysis
  • ChIP-Seq

          They also become familiar with major bioinformatics databases:

          • Sequence Databases: NCBI, EBI, ENSEMBL
          • Genome Browsers & Visualization: Integrated Genome Viewer (IGV), UCSC Genome Browser
          • Biological Data Repositories: Short Read Archive (SRA), Gene Expression Omnibus (GEO)
          • Disease Databases: Open Targets Platform, Open Targets Genetics, OMIM
          • Biological Annotation Databases: Gene Ontology, BioGRID, STRING, Ingenuity (IPA)

                                            The program emphasizes translational bioinformatics, training students to critically evaluate scientific literature and develop research proposals applying bioinformatics to medical interventions. With a strong foundation in computational biology, graduates are well-prepared for careers in biotechnology, biomedical research, and computational medicine.