Module 1

Python Programming Fundamentals for Biologists

Classes 1–3 | From Zero Coding to Your First Biological Analysis

This module introduces Python programming from the very beginning. Students will learn essential programming concepts through examples involving DNA sequences, nucleotide composition, and biological data processing.

Class 1: Introduction to Python and Your First Biological Program

Introduction to Python and its applications in modern biological research, understanding programming environments, getting started with Jupyter Notebook, Google Colab, and VS Code, working with variables, integers, floating-point numbers, and strings, performing basic calculations, understanding error messages, and writing and executing the first Python program.

Practical Exercise

Write a Python program to calculate DNA sequence length and count individual nucleotides (A, T, G, and C).

Class 2: Working with DNA Sequences and Python Data Structures

Understanding Python strings, indexing and slicing DNA sequences, using built-in string methods, introduction to lists and dictionaries, storing and retrieving biological information, calculating GC content, and identifying sequence motifs.

Practical Exercise

Develop a DNA sequence feature calculator that determines sequence length, nucleotide composition, GC percentage, and the presence of selected DNA motifs.

Class 3: Conditional Statements, Loops, and Functions

Introduction to logical operations, conditional statements using if/else, repetitive operations using for loops, writing reusable functions with def and return, validating input sequences, handling common programming errors, and organising code into simple analytical workflows.

Practical Exercise

Build and test reusable Python functions for analysing multiple DNA sequences and identifying sequence features automatically.

Module 2

Biological Sequence Analysis and Scientific Data Processing

Classes 4–7 | From Biological Files to Scientific Figures

This module introduces essential Python libraries used in computational biology. Participants will learn how to process biological sequence files, compare sequences, manipulate datasets, and generate clear scientific visualisations.

Class 4: Biological Sequence Analysis Using Biopython

Introduction to Biopython and its applications in bioinformatics, understanding biological sequence objects, working with Seq and SeqRecord, reading and writing FASTA files using SeqIO, processing multiple biological sequences, and extracting sequence information for downstream analysis.

Practical Exercise

Develop a FASTA-processing workflow that reads multiple DNA sequences, calculates sequence characteristics, and generates a structured summary table.

Class 5: Introduction to Sequence Comparison and Alignment

Understanding why biological sequences are compared, introduction to pairwise sequence alignment, interpreting alignment scores and sequence similarity, conceptual introduction to BLAST, multiple sequence alignment, phylogenetic trees, and protein structure files (PDB).

Practical Exercise

Perform an introductory pairwise sequence alignment and interpret the biological meaning of the alignment results.

Class 6: Biological Data Analysis Using NumPy and pandas

Introduction to NumPy arrays and pandas DataFrames, importing CSV files, exploring biological datasets, selecting and filtering observations, handling missing values, grouping and summarising data, merging sample metadata with biological measurements, and exporting processed datasets.

Practical Exercise

Import biological sample metadata, clean and filter the dataset, calculate summary statistics, merge multiple tables, and export a final analysis-ready CSV file.

Class 7: Scientific Data Visualisation Using Python

Understanding the principles of scientific data visualisation, introduction to Matplotlib and Seaborn, creating bar plots, histograms, scatter plots, and box plots, comparing biological sample groups, customising figure labels and legends, exporting scientific figures, and an overview of interactive visualisation using Plotly.

Practical Exercise

Generate two clearly labelled scientific figures from biological data, save them, and prepare short captions explaining the observed patterns.

Module 3

Exploratory Omics Analysis and Independent Research Project

Classes 8–10 | From Biological Data to Research Interpretation

The final module introduces exploratory analysis of high-dimensional biological datasets and brings together the programming skills developed throughout the course.

Students will work with teaching datasets, interpret results, and prepare a complete reproducible Python analysis notebook.

Class 8: Introduction to Exploratory Omics Analysis and PCA

Understanding high-dimensional biological datasets, introduction to gene expression matrices, exploring variation across samples, data scaling and standardisation, understanding principal component analysis (PCA), interpreting PCA scores and loadings, explained variance, and visualising biological sample relationships.

Practical Exercise

Perform exploratory PCA on a teaching gene expression dataset, generate a PCA plot, and interpret patterns of variation between samples.

Class 9: Hands-on Omics Data Analysis Project

Introduction to a guided biological data analysis project, exploring gene expression datasets, integrating sample metadata, applying data cleaning and validation methods, summarising biological measurements, creating exploratory visualisations, and interpreting results in the context of a biological research question.

Practical Exercise

Develop an exploratory omics analysis notebook incorporating data import, preprocessing, visualisation, and biological interpretation.

Class 10: Capstone Project, Reproducible Research, and Future Directions

Integrating Python programming concepts into a complete biological data analysis workflow, organising Jupyter notebooks, documenting analysis steps, performing quality checks, identifying analytical limitations, preparing scientific figures and summary tables, and communicating research findings.

The session will also introduce future learning opportunities in bioinformatics, computational genomics, machine learning, and advanced biological data analysis.

Practical Exercise

Complete and present a final Python-based biological data analysis project, including a documented notebook, processed datasets, scientific figures, and a short presentation.

Module 2

Biological Sequence Analysis and Scientific Data Processing