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, forloops, 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 (910.25am)

Lecturer 
Practical (10.3512am)


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. Forwardbackward 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. 