Bioinformatic Algorithms, UNIL 2022
Instructors: Prof. Christophe Dessimoz (CD) & Prof. David Gfeller
(DG)
Assistants: Dr. Alex Warwick Vesztrocy and Simon Eggenschwiler
Location: Amphipole 336
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):
https://moodle.unil.ch/course/view.php?id=22623
Link to lecture broadcast for remote attendees
https://unil.zoom.us/j/4792189192
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Date
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Lecture (9-10.25am)
Amphipole 336
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Lecturer
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Practical (10.35-12am)
Amphipole 336
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3 Mar 2022
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Introduction. How computers work; fundamentals of algorithm
and complexity theory.
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CD
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Worksheet 1: Setting up environment, basic algorithm.
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10 Mar 2022
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Exact sequence matching I: read mapping, indexing
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CD
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Answer questions, discuss solutions.
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17 Mar 2022
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Exact sequence matching II: hashing, edit distances.
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CD
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Worksheet 2: Binary search and exact string matching;
genomic signatures
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24 Mar 2022
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Approximate sequence matching: dynamic programming
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CD
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Answer questions, discuss solutions.
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31 Mar 2022
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Alignment significance: permutation test, parameter
estimation
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CD
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Worksheet 3: Sequence alignment with dynamic programming
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7 Apr 2022
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Networks I: Introduction to different types of networks.
Graph representation and data structure. Graph and tree
traversals.
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DG
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Answer questions, discuss solutions.
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14 Apr 2022
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Networks II: Clustering.
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DG
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Worksheet 4: Networks, Dijkstra's algorithm, viewing
networks with cytoscape
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Easter break
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28 Apr 2022
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Networks III: Neural networks.
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DG
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Answer questions, discuss solutions.
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05 May 2022
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Hidden Markov models. Forward-backward algorithm.
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CD
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Worksheet 5: Neuronal network to predict transmembrane
residues in a protein
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12 May 2022
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Conclusion. Review key ideas, next steps, feedback.
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CD
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Answer questions, discuss solutions.
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