Python in Bioinformatics

Syllabus Overview

PYTHON PROGRAMMING FOR BIOINFORMATICS

4 Weeks Program (16 Classes | 1 Hour per Day)

Level: PG / Advanced

Focus: 30% Theory | 70% Hands-on

Week 1 — Python Fundamentals & Programming Basics

Class 1: Introduction to Python & Bioinformatics Applications

Objective: Understand Python fundamentals and its role in bioinformatics

✓ What is Python? Features and advantages

✓ Python in bioinformatics, genomics, transcriptomics, proteomics

✓ Installing Python (Anaconda), Jupyter Notebook

✓ Overview of IDEs and environments

✓ Introduction to basic syntax

Hands-on:

✓ Launch Jupyter Notebook

✓ Write first Python program

✓ Basic arithmetic and print statements

Class 2: Variables, Data Types & Operators

Objective: Learn core programming building blocks

✓ Variables and naming conventions

✓ Data types: int, float, string, boolean

✓ Type conversion

✓ Operators: arithmetic, logical, relational

Hands-on:

✓ Perform biological data-based calculations

✓ Temperature, pH, GC percentage calculations

Class 3: Control Structures

Objective: Implement decision-making and looping

✓ Conditional statements (if, elif, else)

✓ Loops: for, while

✓ Loop control: break, continue

Hands-on:

✓ Sequence length filtering

✓ GC content-based conditional analysis

Class 4: Functions & Introduction to Modules

Objective: Create reusable and modular code

✓ Defining functions

✓ Function arguments and return values

✓ Built-in functions

✓ Importing modules (math, random)

Hands-on:

✓ Write functions for GC content and reverse complement

✓ Modularize scripts

Assignment (Week 1):

Write a Python script that takes a DNA sequence as input and calculates length, GC content, and nucleotide frequency using functions and conditional statements.

Week 2 — Data Structures & File Handling for Biological Data

Class 1: Python Data Structures

Objective: Store and manipulate biological data efficiently

✓ Lists, tuples, sets, dictionaries

✓ Indexing and slicing

✓ Dictionary usage for sequence counts

Hands-on:

✓ Store FASTA sequences in lists

✓ Codon frequency using dictionaries

Class 2: String Manipulation for Sequences

Objective: Perform sequence-level operations

✓ String methods

✓ Pattern searching

✓ Translation and transcription logic

Hands-on:

✓ DNA → RNA transcription

✓ Reverse complement generation

Class 3: File Handling (FASTA, FASTQ, TXT, CSV)

Objective: Read and write biological data files

✓ File modes: read, write, append

✓ Reading FASTA and text files

✓ Writing output files

Hands-on:

✓ Read FASTA file and extract sequences

✓ Save analysis results to file

Class 4: Exception Handling & Script Automation

Objective: Build robust bioinformatics scripts

✓ Try–except blocks

✓ Error handling in file processing

✓ Introduction to command-line scripts

Hands-on:

✓ Handle missing files and incorrect formats

✓ Simple automation script

Assignment (Week 2):

Write a Python program that reads a FASTA file, calculates GC content for each sequence, and writes the results to an output file with proper error handling.

Week 3 — Python Libraries for Bioinformatics & Data Analysis

Class 1: Introduction to NumPy & Pandas

Objective: Handle numerical and tabular biological data

✓ NumPy arrays and operations

✓ Pandas DataFrames

✓ Importing and exporting CSV/Excel files

Hands-on:

✓ Load gene expression data

✓ Perform basic statistics

Class 2: Data Cleaning & Manipulation

Objective: Prepare biological datasets for analysis

✓ Handling missing values

✓ Filtering and sorting data

✓ Grouping and aggregation

Hands-on:

✓ Clean water quality / gene expression datasets

✓ Subset biologically relevant data

Class 3: Data Visualization Using Matplotlib & Seaborn

Objective: Visualize biological data effectively

✓ Line plots, bar plots, histograms

✓ Scatter plots

✓ Customizing plots

Hands-on:

✓ GC content distribution plots

✓ Gene expression visualization

Class 4: Introduction to Biopython

Objective: Use bioinformatics-specific Python tools

✓ Overview of Biopython

✓ Seq, SeqIO, AlignIO modules

✓ Working with FASTA, GenBank

Hands-on:

✓ Parse FASTA and GenBank files

✓ Basic sequence analysis using Biopython

Assignment (Week 3):

Analyze a biological dataset using Pandas and visualize results using Matplotlib. Additionally, parse a FASTA file using Biopython and summarize sequence statistics.

Week 4 — Advanced Bioinformatics Applications & Mini Project

Class 1: Sequence Analysis & Alignment Automation

Objective: Automate sequence-level bioinformatics tasks

✓ Pairwise alignment concepts

✓ Running external tools via Python

✓ Introduction to BLAST automation

Hands-on:

✓ Pairwise sequence alignment using Biopython

Class 2: Biological Statistics & Basic Machine Learning Concepts

Objective: Apply statistics to biological data

✓ Mean, median, standard deviation

✓ Correlation analysis

✓ Introduction to ML concepts in bioinformatics

Hands-on:

✓ Statistical analysis of expression data

Class 3: Workflow Development & Reproducible Research

Objective: Build reproducible bioinformatics pipelines

✓ Script structuring

✓ Logging and documentation

✓ Introduction to notebooks for reproducibility

Hands-on:

✓ Create a mini analysis workflow

Class 4: Mini Project & Presentation

Objective: Integrate all learned concepts

✓ End-to-end Python-based bioinformatics analysis

✓ Interpretation and reporting

Hands-on:

✓ Mini project execution

✓ Presentation and discussion

Final Assignment (Week 4):

Develop a complete Python-based bioinformatics mini project (e.g., sequence analysis, gene expression analysis, or biological data visualization) and submit a short report along with code and results.