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Computationele Biologie (2017–2018)

Peter Dawyndt · Universiteit Gent

Welkom op de Dodona-cursus van het opleidingsonderdeel Computationele biologie (Universiteit Gent, Faculteit Wetenschappen, Master in de Informatica). De programmeeroefeningen in de cursus maken gebruik van Python 3.6 (anaconda distributie aangevuld met BioPython).

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Exercise series

Chapter 11: Was T. Rex just a big chicken?

Chapter 10: Why have biologists still not developed an HIV vaccine?

Chapter 09: How do we locate disease-causing mutations?

Chapter 08: How did yeast become a winemaker?

Chapter 07: Which animal gave us SARS?

Title Class progress Status
Distance between leaves
Limb lengths in a tree
Additive phylogeny
UPGMA
Neighbour joining

Chapter 06: Are there fragile regions in the human genome?

Title Class progress Status
Greedy sort a permutation by reversals
Number of breakpoints in a permutation
Shared k-mers
Chromosome to cycle
Cycle to chromosome
Colored edges
Genome graph to genome
2-break on genome graph
2-break on genome
2-break distance
2-break sorting

Chapter 05: How do we compare biological sequences?

Na de deadline zal de correctheid en de computationele complexiteit (tijd en geheugen) van de volgende oefening geëvalueerd worden op basis van peer review:

  • Global alignment in linear space
Title Class progress Status
Change problem
Longest path in a Manhattan-like grid
Longest common subsequence
Topological ordering of a DAG
Longest path in a DAG
Global alignment
Local alignment
Edit distance
Fitting alignment
Overlap alignment
Global alignment with affine gap penalties
Multiple longest common subsequence
Find a middle edge in linear space
Global alignment in linear space

Chapter 04: How do we sequence antibiotics?

  • handboek
  • videolessen
  • oefeningen waarvan computationele complexiteit zal gebenchmarked worden
    • Number of peptides with a given total mass (benchmark)
    • Leaderboard cyclopeptide sequencing (benchmark)
Title Class progress Status
Translate an RNA string into an amino acid string
Find DNA substrings encoding an amino acid string
Theoretical spectrum of a linear peptide
Theoretical spectrum of a cyclic peptide
Number of peptides with a given total mass
Find a cyclic peptide with a given ideal spectrum
Score a cyclic peptide against a spectrum
Score a linear peptide against a spectrum
Trim a peptide leaderboard
Leaderboard cyclopeptide sequencing
Convolution of a spectrum
Convolution cyclopeptide sequencing
Turnpike problem

Chapter 03: How do we assemble genomes?

Na de deadline zal de correctheid en de computationele complexiteit (tijd en geheugen) van de volgende oefening geëvalueerd worden op basis van peer review:

  • Reconstruct a string from its k-mer composition (benchmark)
Title Class progress Status
K-mer composition
Reconstruct a string from its genome path
Construct an overlap graph from a collection of k-mers
Construct a de Bruijn graph from a string
Construct a de Bruijn graph from a collection of k-mers
Find an Eulerian cycle in a graph
Find an Eulerian path in a graph
Reconstruct a string from its k-mer composition
Find a k-universal circular string
Generate all maximal non-branching paths in a graph
Generate contigs from a collection of k-mers

Chapter 02: Which DNA patterns play the role of molecular clocks?
February 27, 2018 22:00

  • handboek
  • videolessen
  • Python bronnen
  • oefeningen waarvan computationele complexiteit zal gebenchmarked worden
    • Greedy motif search with pseudocounts (benchmark)
    • Gibbs sampler
Title Class progress Status
Motif enumeration
Distance between pattern and strings
Median string
Profile-most probable k-mer
Greedy motif search
Greedy motif search with pseudocounts
Randomized motif search
Gibbs sampler

Chapter 01: Where in the genome does DNA replication begin?

Na de deadline zal de computationele complexiteit (tijd en geheugen) van de volgende twee oefeningen geëvalueerd worden. Voor deze evaluatie komen enkel correcte oplossingen in aanmerking en wordt telkens de laatst ingediende correcte oplossing geëvalueerd.

  • Clump finding (benchmark)
  • Most frequent words with mismatches and reverse complements (benchmark)
Title Class progress Status
Pattern count
Most frequent words
Reverse complement
Pattern occurrences
Clump finding
Minimum skew
Hamming distance
Approximate pattern matching
Most frequent words with mismatches
Most frequent words with mismatches and reverse complements
Frequency array
Pattern to number
Number to pattern
D-neighborhood