Bioinformatic Algorithms, UNIL 2018

Instructors: Prof. Christophe Dessimoz (CD) & Prof. David Gfeller (DG)
Assistants: Dr. David Dylus & Dr. Leonardo de Oliveira Martins (DG)

15h lecture, 15h practical, 10h personal work (3 ECTS)

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):


Lecture (9-10.25am)
Amphipôle 336


Practical (10.35-12am)
Amphipôle 336

1 March 2018

Introduction. How computers work; fundamentals of algorithm and complexity theory.


Worksheet 1: Setting up environment, basic algorithm.

8 March 2018

Exact sequence matching I: read mapping, indexing


Answer questions, discuss solutions.

15 March 2018

Exact sequence matching II: hashing, edit distances.


Worksheet 2: Binary search and exact string matching; genomic signatures

22 March 2018

Approximate sequence matching: dynamic programming


Answer questions, discuss solutions.

29 March 2018

Alignment significance: permutation test, parameter estimation


Worksheet 3: Sequence alignment with dynamic programming

Easter break

12 Apr 2018

Networks I: Introduction to different types of networks. Graph representation and data structure. Graph and tree traversals.


Answer questions, discuss solutions.

19 Apr 2018

Networks II: Clustering.


Worksheet 4: Networks, Dijkstra's algorithm, viewing networks with cytoscape

26 Apr 2018

Networks III: Neural networks.


Answer questions, discuss solutions.

3 May 2018

Hidden Markov models. Forward-backward algorithm.


Worksheet 5: Neuronal network to predict transmembrane residues in a protein


17 May 2018

Conclusion. Review key ideas, next steps, feedback.


Answer questions, discuss solutions.

Last modified on February 9th, 2018.