Bioinformatics
A bioinformatician is a scientist who applies computing, statistics, and biology to store, analyze, and interpret large biological datasets such as DNA, RNA, or protein sequences. They typically have training in both life sciences and computer science and work at the point where “big data” meets molecular biology and genetics.
Our Course Curriculum
- Introduction to Linux and Bash (Bourne-again shell) on Linux
- Code version control and documentation (Git/GitHub)
- Introduction to R, RStudio, and R Syntax
- Statistical Analysis and Visualization with R
- Basic descriptive statistics
- Dimensionality: Reduction strategies and Clustering
- Bioconductor on R
- Sequence Data Generation
- Sequence analysis and Visualization
- Transcriptome Data Analysis
- Basic Multi-Omics Strategies (Genomics + Transcriptomics)
- Final Projects
Class Duration – 4 Weeks
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Where You Would Work
- Research institutions and universities – supporting basic research in genetics, evolution, systems biology, and disease mechanisms.
- Hospitals and clinical labs – analyzing sequencing data for diagnostics (for example, cancer genomics, rare disease, pathogen surveillance).
- Pharmaceutical and biotech companies – identifying drug targets, analyzing clinical trial biomarkers, and supporting precision medicine and synthetic biology.
- Public health and agriculture – tracking outbreaks, studying pathogen genomes, breeding disease‑resistant crops, or improving livestock genetics.
- Tech and data‑science companies – building bioinformatics software platforms, cloud pipelines, and databases used by labs and clinicians.
Student Testimonial
Typical Roles and Responsibilities
- Data pipeline development
- Build and maintain analysis workflows for next‑generation sequencing and other high‑throughput experiments (quality control, alignment, variant calling, expression quantification).
- Automate routine analyses using programming languages such as Python or R and workflow tools.
- Algorithm and tool creation
- Develop or refine algorithms for tasks like genome assembly, variant detection, gene‑expression analysis, or single‑cell data integration.
- Package methods into reusable software, command‑line tools, or web applications for other scientists.
- Database and infrastructure management
- Design and curate biological databases that store genomic, proteomic, or clinical data, ensuring data integrity, documentation, and standards compliance.
- Work with cloud and high‑performance computing environments to handle very large datasets securely and efficiently.
- Analysis and interpretation
- Perform statistical and computational analyses to answer biological or clinical questions (e.g., which mutations drive a cancer, which genes are differentially expressed).
- Visualize results and translate them into biologically meaningful insights for wet‑lab scientists, clinicians, or business stakeholders.
- Collaboration and communication
- Work in cross‑disciplinary teams with biologists, clinicians, statisticians, and software engineers.
- Contribute to papers, reports, and presentations, and often advise on experimental design so that data can be analyzed robustly later.
