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Algorithms for Sequence Analysis


Course overview

Sequence information is ubiquitous in many application domains. DNA sequencing data are one example that motivates this lecture, but the focus of this course is on algorithms and concepts that are not specific to bioinformatics. This lecture addresses classic as well as recent advanced algorithms for the analysis of large sequence databases. Topics include: full text search without index; approximate pattern matching; index structures such as suffix trees and suffix arrays, Burrows-Wheeler transformand the FM index; data compression; multiple sequence alignment; and min hashing.

General info

Teaching assistent: Fawaz Dabbaghie

Prerequisites: Bioinformatics I+II or comparable lectures, basic Python programming skills and basic knowledge in data structures.

Requirements to pass the course:

  • Successfully working on the assignments
  • 50% Theory points
  • 50% Practice points
  • Passing the exam


Start of the lecture: 05/05/2020

Lectures: Tuesday 12 pm, Zoom

Office hour: TBD

Exercise: TBD

Exam: First week of August 2020. (Room TBA)

Re-exam: TBD

Lecture materials

You can find an introduction to Python3 and its data structures here.

Lecture time: Tuesdays, meeting open at 12:00, questions can start at 12:15 for students who come a bit later. Zoom meeting link

Tutorial zoom meeting link here. Backup link if we take more than 45 minutes here.

Tutorials will be bi-weekly on Wednesday at 3 pm CET. First tutoria is going to be on the 13th of May at 3 pm CET.

For questions and registration, email:

You can find some very useful lecture notes here.

Lecture material: