Bioinformatic Algorithms, UNIL 2025
Instructors: Prof. Christophe Dessimoz (CD) & Prof. David Gfeller
(DG)
Assistants: Dr. Irene Julca, Daniel Tadros
Location: Amphipole 336 (to be confirmed)
19h lecture, 20h practical, 20h personal work (4 ECTS for MSc / 3 ECTS for PhD)
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=30087
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Date
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Lecture
Amphipole 336
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Lecturer
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Practical
Amphipole 336
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25 Feb 2025 9.00-12.00
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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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4 Mar 2025 9.00-12.00
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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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11 Mar 2025 9.00-12.00
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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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18 Mar 2025 9.00-12.00
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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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25 Mar 2025 9.00-12.00
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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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1 Apr 2025 9.00-12.00
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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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8 Apr 2025 9.00-12.00
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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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15 Apr 2025 9.00-12.00
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Fast approximations: k-mer, minimizers and min-hashing
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CD
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Answer questions, discuss solutions
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|
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Easter break
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29 Apr 2025 9.00-12.00
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Networks III: Neural networks.
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DG
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Worksheet 5: Neuronal network to predict transmembrane residues in a protein
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6 May 2025 9.00-12.00
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Network IV: Convolutional neural networks.
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DG
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No practical
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13 May 2025 9.00-12.00
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Cross-validation, avoiding overfitting
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DG
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Answer questions, discuss solutions
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20 May 2025 9.00-12.00
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Hidden Markov models. Forward-backward algorithm.
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CD
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Worksheet 6: Hidden Markov Models
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27 May 2025 9.00-12.00
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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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