Computational Biology with Python: From Algorithms to Bioinformatics Applications

This repository is a Rosalind-based open educational and research portfolio connecting Python programming, core algorithms, bioinformatics, Biopython workflows, and ML-ready biological sequence analysis.

Purpose

The goal is to show how foundational computer science concepts such as strings, sorting, dynamic programming, graph algorithms, probability, and file parsing support real computational biology tasks including sequence analysis, motif finding, genome assembly, phylogenetics, proteomics, and biological data processing.

Background

This portfolio builds on 202 solved Rosalind problems across:

• Python Village
• Algorithmic Heights
• Bioinformatics Stronghold
• Bioinformatics Armory
• Bioinformatics Textbook Track

The project restructures those solved problems into a prerequisite-aware curriculum rather than presenting them only as isolated solutions.

Learning path

1. Python for Bioinformatics
2. Algorithmic Foundations: Arrays, Sorting, and Searching
3. Algorithmic Foundations: Graph Algorithms
4. Biological Sequence Basics
5. Sequence Statistics, Motifs, and k-mers
6. Genetics and Probability
7. Dynamic Programming and Sequence Alignment
8. String Matching and Genome Indexing
9. Graphs and Genome Assembly
10. RNA Structure and Combinatorial Biology
11. Proteomics and Mass Spectrometry
12. Phylogenetics and Evolution
13. Practical Bioinformatics with Biopython
14. ML-ready Bioinformatics Bridge

Repository structure

• original_rosalind_tracks/ — original solved Rosalind problems organized by track
• modules/ — teaching modules connecting algorithmic foundations to bioinformatics applications
• notebooks/ — tutorial notebooks
• teaching/ — workshop-style teaching material
• mini_projects/ — extensions connecting bioinformatics to machine learning
• curriculum_map.md — module-by-module curriculum map
• problem_dependency_map.md — prerequisite and concept dependency map
• solved_problem_index.md — index of solved Rosalind problems by module and teaching role

Module Index

The repository is organized into thirteen teaching-oriented modules. Each module connects original Rosalind problems to broader computational biology concepts.

Module	Topic	Focus
Module 00	Python for Bioinformatics	Python syntax, strings, files, dictionaries, and basic text processing
Module 01	Algorithmic Foundations: Arrays, Sorting, Searching	Recurrence, binary search, sorting, heaps, inversions, and selection
Module 02	Algorithmic Foundations: Graphs	BFS, connectivity, DAGs, shortest paths, SCCs, and graph representation
Module 03	Biological Sequence Basics	DNA, RNA, reverse complements, translation, ORFs, splicing, and protein basics
Module 04	Sequence Statistics, Motifs, and k-mers	GC content, motifs, consensus sequences, profile matrices, and k-mer features
Module 05	Genetics and Probability	Mendelian inheritance, allele frequencies, genetic drift, combinatorics, and probability
Module 06	Dynamic Programming and Sequence Alignment	Recurrence, LCS, edit distance, global/local alignment, and affine gaps
Module 07	String Matching and Genome Indexing	KMP, tries, suffix structures, BWT, Boyer-Moore, and Z-algorithm
Module 08	Graphs and Genome Assembly	Overlap graphs, de Bruijn graphs, Eulerian paths, and genome reconstruction
Module 09	RNA Structure and Combinatorial Biology	RNA matchings, Catalan/Motzkin recurrences, wobble pairs, and structure counting
Module 10	Proteomics and Mass Spectrometry	Protein mass, peptide spectra, spectral convolution, and peptide reconstruction
Module 11	Phylogenetics and Evolution	Distance matrices, Newick trees, character tables, quartets, and split distance
Module 12	Practical Bioinformatics with Biopython	FASTA/FASTQ handling, GenBank retrieval, quality filtering, and ORF workflows
Module 13	ML-ready Bioinformatics Bridge	GC features, k-mer vectors, motif features, similarity scores, and feature matrices

Setup

Clone the repository:

git clone https://github.com/aakibinesar/computational-biology-python.git
cd computational-biology-python

Create a virtual environment:

python -m venv .venv

Activate the virtual environment.

On Windows:

.venv\Scripts\activate

On macOS/Linux:

source .venv/bin/activate

Install the required dependencies:

pip install -r requirements.txt

Launch Jupyter Notebook:

jupyter notebook

Alternatively, launch JupyterLab:

jupyter lab

Current Development

This repository is being developed in stages.

• Repository structure: complete
• Original Rosalind solution archive: added
• Curriculum map: complete first version
• Problem dependency map: complete first version
• Solved problem index: complete grouped first version
• Module README files: complete first version
• Tutorial notebooks: complete first version
• ML-ready sequence feature mini-project: complete first version
• Teaching materials: complete first version

For a detailed completion summary, see project_status.md. For planned future improvements, see roadmap.md.

Acknowledgement

This repository is an independent educational portfolio based on my completed Rosalind problem-solving work.

Rosalind problem names and IDs are used for reference and curriculum mapping. The original problems belong to the Rosalind platform. This repository contains my own solution archive, explanations, teaching materials, notebooks, and extensions, and is not affiliated with or endorsed by Rosalind.