- Understand the types of alignments: global (Needleman–Wunsch), local (Smith–Waterman), and semi-global.
- Learn to use substitution matrices (PAM, BLOSUM).
- Practice with Biopython and external tools (BLAST, Clustal Omega).
- Develop skills for implementing the basic alignment algorithms.
Run
- demo01_pairwise_biopython.py — global and local alignment using Biopython (pairwise2).
- demo02_distance_matrix.py — compute sequence distances (p-distance, Hamming) from FASTA files. Complete and run
- ex02_global_nw.py — implementation skeleton for global alignment (TODO).
- ex03_local_sw.py — implementation skeleton for local alignment (TODO).
Note: Use the datasets downloaded in Lab 1 (from
data/work/<handle>/lab01/).
*Your Pull Request (PR) must include:
- The file
labs/02_alignment/<github_handle>_notes.mdcontaining:- which datasets you used (e.g., TP53 vs. BRCA1),
- a short reflection: “When is global alignment preferred over local alignment?”
- The completed exercises, saved under:
labs/02_alignment/submissions/<github_handle>/ex02_global_nw.pylabs/02_alignment/submissions/<github_handle>/ex03_local_sw.py- The completed checklist from the PR template.
- We will extend the analysis to NGS reads (FASTQ → mapping → variant calling).
- The alignments obtained this week will be used to validate mapping and NGS analyses.
- See Week 3 — NGS Analysis
- Understand the difference between global and local alignment.
- Use Biopython for simple pairwise alignments.
- Implement the basic NW and SW algorithms.
- Interpret results and compare them with BLAST / Clustal outputs.