shibila621/pylifes

A graduate-level elective course: Python Programming for Life Science Students

PyLifeS

This repository contains course materials prepared for the graduate-level elective course Python Programming for Life Science Students to be offered during January-April 2024.

Classes are on Fridays from 09.30 to 11.30 AM in Room No. 102, SLS Annex building of UoH (University of Hyderabad). The first class begins on 19 January 2024.

Instructor	Prerequisites	Credits	Duration
Raghunathan Ramakrishnan, TIFR Hyderabad ([email protected])	Exposure to biology at the UG level	2	12 x 2 hours (Combined theory and hands-on sessions)

NOTE: This repository has a wiki providing a lot of useful information!

🚧 This repository will be updated as the course progresses. 🚧

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Click the image below to access the presentation PDF.

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Following is the tentative syllabus.

Part 1: Python basics

• Check your computers, operating systems, Python installation/configuration, Jupyter notebooks
• Data structures, functions, loops, input/output, file handling
• Data visualization with matplotlib
• Data analysis with pandas

Part 2: Scientific Programming

• Numpy & Scipy
• Modeling biological data with exponential and logarithmic functions, growth functions
• Solving differential equations with Python: Enzyme kinetics (Michaelis-Menten Enzyme Kinetics, Lineweaver–Burk plot), Modeling infectious diseases (SIR model), Homeostasis

Part 3: Introduction to Biopython

• Biopython data structures: Seq, SeqRecord, SeqFeature, and others. Reading and writing sequence data from and to different file formats using Biopython
• Sequence manipulation and analysis using Biopython: translation, reverse complement, sequence alignment, motif finding, and more
• BLAST searches and analysis using Biopython
• Sequence annotation tools and data visualization

Part 4: Working with Biological Databases

• Overview of biological databases and their role in bioinformatics
• Retrieving and parsing data from common biological databases
• Sequence and annotation data from biological databases

Part 5: Advanced Biopython Topics

• Genomics: Next-generation sequencing data
• Structural biology: parsing PDB files, working with structural data, and visualization
• Machine learning and data mining
• Bioinformatics workflows

Evaluation:

3 x Combined theory and practicals test (best 2 will be considered)

References Books:

1. Understanding Bioinformatics, Marketa Zvelebil, and Jeremy O. Baum (Garland Science, 2008)
2. Bioinformatics Algorithms: Design and Implementation in Python, Miguel Rocha University of Minho, Braga, Portugal Pedro G. Ferreira (Academic Press, 2018)
3. Computing Skills for Biologists: A toolbox, Stefano Allesina & Madlen Wilmes (Princeton University Press, 2019)
4. Python for the Life Sciences: A Gentle Introduction to Python for Life Scientists, Alexander Lancaster, Gordon Webster (Springer, 2019)
5. Linux for Developers, William Bo Rothwell (Pearson, 2018). See the chapters about GitHub.