Bioinformatics Algorithms Course Syllabus

Overview: This course offers a hands-on introduction to core bioinformatics algorithms, guiding you through sequence analysis, genome assembly, and evolutionary inference using real biological data and Python implementations. With approximately 60-70 hours of content across 8 modules, each taking about a week to complete, you'll build algorithmic understanding while gaining practical skills in parsing biological data, performing alignments, assembling genomes, and reconstructing phylogenetic trees. The course concludes with a capstone project integrating multiple techniques into a functional genomics workflow.

Module 1: Introduction to Bioinformatics & Sequence Data

Estimated time: 8 hours

• Biological sequence formats (FASTA, FASTQ)
• Scoring matrices (PAM, BLOSUM)
• Parsing DNA/RNA sequences from FASTA files
• Computing simple sequence similarity scores

Module 2: Pairwise Alignment with Dynamic Programming

Estimated time: 8 hours

• Global alignment using Needleman–Wunsch algorithm
• Local alignment using Smith–Waterman algorithm
• Implementation of affine gap penalties
• Python implementation of alignment algorithms

Module 3: Heuristic Alignment & BLAST

Estimated time: 8 hours

• Overview of the BLAST algorithm
• Word-size seeding and high-scoring segment pairs (HSPs)
• Using Biopython for BLAST searches
• Parsing BLAST output from custom databases

Module 4: Multiple Sequence Alignment

Estimated time: 8 hours

• Progressive alignment methods (ClustalW)
• Iterative refinement techniques
• Consistency-based alignment strategies
• Visualizing conserved motifs across aligned proteins

Module 5: Genome Assembly Algorithms

Estimated time: 8 hours

• Overlap–layout–consensus approach
• De Bruijn graph-based assembly
• Error correction in sequencing reads
• Building de Bruijn graphs and extracting contigs

Module 6: Hidden Markov Models in Bioinformatics

Estimated time: 8 hours

• Components of hidden Markov models (HMMs)
• Viterbi and forward–backward algorithms
• Profile HMMs for protein family detection
• Training an HMM for gene prediction on bacterial sequences

Module 7: Phylogenetic Inference & Tree Reconstruction

Estimated time: 8 hours

• Distance-based methods: UPGMA and neighbor-joining
• Character-based methods: maximum parsimony and maximum likelihood
• Constructing phylogenetic trees from aligned sequences
• Using scikit-bio for tree comparison and visualization

Module 8: Advanced Topics & Capstone Project

Estimated time: 10 hours

• Sequence clustering techniques
• Basics of variant calling
• Scalable algorithms for big genomic data
• End-to-end annotation of a draft bacterial genome
• Detection of gene models and variant sites

Prerequisites

• Basic proficiency in Python programming
• Familiarity with fundamental biological concepts (DNA, RNA, proteins)
• Understanding of basic data structures and algorithms

What You'll Be Able to Do After

• Implement core bioinformatics algorithms from scratch in Python
• Analyze biological sequences using dynamic programming and heuristic methods
• Assemble genomes using de Bruijn graph approaches
• Apply hidden Markov models for gene and protein family prediction
• Reconstruct and interpret phylogenetic trees from sequence data