📚 Syllabus & Course Curriculum
Bioinformatics & Computational Biology
Module-by-module breakdown of Python/R for Bioinformatics: Genomics, Transcriptomics & Proteomics Data, from foundations to a certified capstone project.
Foundations
• Where Python and R each genuinely have the advantage in bioinformatics
• Conda, renv and reproducible environments rather than a shared global install
• Project structure, version control and scripts over interactive one-off commands
Genomics
• BioPython and Biostrings for sequence handling and file parsing
• Reading VCF and BED and using pyranges or GenomicRanges for interval work
• Coordinate conventions and the strand errors that silently corrupt results
Transcriptomics
• Count matrices, DESeq2 and the reason raw counts must not be pre-normalised
• Exploratory analysis with PCA and clustering before testing anything
• Pandas and dplyr for the reshaping most of the work actually consists of
Proteomics
• Search output, FDR at peptide and protein level, and the protein inference problem
• Missing values in proteomics and imputation that must be reported
• Normalisation and statistics on data with far fewer features than transcriptomics
Communication
• ggplot2 and matplotlib or seaborn for figures that survive review
• Volcano plots, heatmaps and MA plots read correctly
• R Markdown and Jupyter for an analysis someone else can rerun
Earn government-registered certification in Python/R for Bioinformatics: Genomics, Transcriptomics & Proteomics Data
e-Certificate and e-Marksheet issued on successful completion.
