Bioinformatic Algorithms, UNIL 2026
Instructors: Prof. Christophe Dessimoz (CD) & Prof. David Gfeller
(DG)
Assistants: Dr. Stefano Pascarelli, Dana Moreno
Location: Amphipole 336 (except May 26: Amphipole 202)
19h lecture, 20h practical, 20h personal work (4 ECTS for MSc / 3 ECTS for PhD)
The course aims at improving the student’s programming skills by gaining a
deep understanding of some of the key algorithms in bioinformatics, with a
special emphasis on sequence and graph algorithms. Students will learn
widely applicable concepts, such as asymptotic time complexity, binary
search, suffix trees, dynamic programming, hashing, hidden Markov models,
and neuronal networks.
Practicals and home assignments are essential parts of the course. The
language of the course is Python, though the concepts covered in the course
are applicable to all computer languages.
Assessment:
20 min oral examination
Prerequisites:
The course assumes familiarity with basic programming concepts (variable
and function declaration, arrays, for-loops, conditional statements, etc.).
Algorithms are introduced from a practical angle so the mathematical
formalism is kept at a minimum.
Moodle page
(for Q&A, slides, worksheets, etc):
https://moodle.unil.ch/course/view.php?id=30087
|
Date
|
Lecture
Amphipole 336
|
Lecturer
|
Practical
Amphipole 336
|
|
24 Feb 2026 9.00-12.00
|
Introduction. How computers work; fundamentals of algorithm
and complexity theory.
|
CD
|
Worksheet 1: Setting up environment, basic algorithm.
|
|
3 Mar 2026 9.00-12.00
|
Exact sequence matching I: read mapping, indexing
|
CD
|
Answer questions, discuss solutions.
|
|
10 Mar 2026 9.00-12.00
|
Exact sequence matching II: hashing, edit distances.
|
CD
|
Worksheet 2: Binary search and exact string matching;
genomic signatures
|
|
17 Mar 2026 9.00-12.00
|
Approximate sequence matching: dynamic programming
|
CD
|
Answer questions, discuss solutions.
|
|
24 Mar 2026 9.00-12.00
|
Alignment significance: permutation test, parameter
estimation
|
CD
|
Worksheet 3: Sequence alignment with dynamic programming
|
|
31 Mar 2026 9.00-12.00
|
Networks I: Introduction to different types of networks.
Graph representation and data structure. Graph and tree
traversals.
|
DG
|
Answer questions, discuss solutions.
|
|
|
Easter break
|
|
14 Apr 2026 9.00-12.00
|
Networks II: Clustering.
|
DG
|
Worksheet 4: Networks, Dijkstra's algorithm, viewing
networks with cytoscape
|
|
21 Apr 2026 9.00-12.00
|
Networks III: Neural networks.
|
DG
|
Answer questions, discuss solutions
|
|
28 Apr 2026 9.00-12.00
|
Network IV: Convolutional neural networks.
|
DG
|
Worksheet 5: Neuronal network to predict transmembrane residues in a protein
|
|
05 May 2026 9.00-12.00
|
Cross-validation, avoiding overfitting
|
DG
|
Answer questions, discuss solutions
|
|
12 May 2026 9.00-12.00
|
Hidden Markov models. Forward-backward algorithm.
|
CD
|
Worksheet 6: Hidden Markov Models
|
|
19 May 2026 9.00-12.00
|
Fast approximations: k-mer, minimizers and min-hashing
|
CD
|
Answer questions, discuss solutions
|
|
26 May 2026 9.00-12.00
|
Conclusion. Review key ideas, next steps, feedback.
|
CD
|
No practical
|