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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| Titel | Voortgang groep | Status | |||
|---|---|---|---|---|---|
| Distance between leaves | |||||
| Limb lengths in a tree | |||||
| Additive phylogeny | |||||
| UPGMA | |||||
| Neighbour joining |
| Titel | Voortgang groep | 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 |
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
| Titel | Voortgang groep | 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 |
- handboek
- videolessen
- oefeningen waarvan computationele complexiteit zal gebenchmarked worden
| Titel | Voortgang groep | 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 |
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)
| Titel | Voortgang groep | 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 |
- handboek
- videolessen
- Python bronnen
- oefeningen waarvan computationele complexiteit zal gebenchmarked worden
- Greedy motif search with pseudocounts (benchmark)
- Gibbs sampler
| Titel | Voortgang groep | 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 |
- handboek
- videolessen
- Python bronnen
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.
| Titel | Voortgang groep | 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 |