Bioinformatic Algorithms, UNIL 2017

Instructors: Prof. Christophe Dessimoz (CD) & Prof. David Gfeller (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): http://moodle2.unil.ch/course/view.php?id=6217

 
Date

Lecture (9-10.25am)
Amphipôle 189

Lecturer

Practical (10.35-12am)
Amphipôle 189

2 March 2017

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

CD

Worksheet 1: Setting up environment, basic algorithm.

9 March 2017

Exact sequence matching I: read mapping, indexing

CD

Answer questions, discuss solutions.

16 March 2017

Exact sequence matching II: hashing, edit distances.

CD

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

23 March 2017

Approximate sequence matching: dynamic programming

CD

Answer questions, discuss solutions.

30 March 2017

Alignment significance: permutation test, parameter estimation

CD

Worksheet 3: Sequence alignment with dynamic programming

6 Apr 2017

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

DG

Answer questions, discuss solutions.

13 Apr 2017

Networks II: Clustering.

DG

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

Easter break

27 Apr 2017

Networks III: Neural networks.

DG

Answer questions, discuss solutions.

4 May 2017

Hidden Markov models. Forward-backward algorithm.

CD

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

11 May 2017

Conclusion. Review key ideas, next steps, feedback.

CD

Answer questions, discuss solutions.

Last modified on February 13th, 2017.