Bioinformatic Algorithms, UNIL 2019

Instructors: Prof. Christophe Dessimoz (CD) & Prof. David Gfeller (DG)
Assistants: Dr. David Dylus & David Moi

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): http://moodle2.unil.ch/course/view.php?id=6217

 
Date

Lecture (9-10.25am)
Amphipôle 336

Lecturer

Practical (10.35-12am)
Amphipôle 336

28 Feb 2019

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

CD

Worksheet 1: Setting up environment, basic algorithm.

7 Mar 2019

Exact sequence matching I: read mapping, indexing

CD

Answer questions, discuss solutions.

14 Mar 2019

Exact sequence matching II: hashing, edit distances.

CD

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

21 Mar 2019

Approximate sequence matching: dynamic programming

CD

Answer questions, discuss solutions.

28 Mar 2019

Alignment significance: permutation test, parameter estimation

CD

Worksheet 3: Sequence alignment with dynamic programming

4 Apr 2019

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

DG

Answer questions, discuss solutions.

11 Apr 2019

Networks II: Clustering.

DG

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

18 Apr 2019

Networks III: Neural networks.

DG

Answer questions, discuss solutions.

Easter break

2 May 2019

Hidden Markov models. Forward-backward algorithm.

CD

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

9 May 2019

Conclusion. Review key ideas, next steps, feedback.

CD

Answer questions, discuss solutions.

Last modified on February 11th, 2019.