Programming course "Algorithms in Bioinformatics"
- Daniel Maticzka, Omer Alkhnbashi
General Aim and Major Purpose
The aim of the practical course is to acquire and implement a manageable amount of algorithms
out of a sub-branch of bioinformatics. Based on a literature search for each algorithm, a comprehensive description
and implementation using Python will be the aim of the course.
Therefore, the programs should be implemented, such that command-line calls and the usability as modules is realized.
Within one session, the (almost) finished program-codes will be cross-validated and evaluated by the courses' participants.
English is the course language -> Presentations, Documentations and the Comments within the code have to be in english!
- Literature Search
- Description of the Algorithms (in LaTeX-Format)
- well documented source code (Python)
- Input-failure robust parameter parsing
- 24.Oct 2016
|Introduction / Overview and Distribution of Topics
|Presentations (10-20 min)
| Introduction to the project (implement the algorithms using Python)
| Hand-in of code section "Sequence Alignment"
Multiple Sequence-Alignment and Phylogeny/Clustering
- Needleman-Wunsch - Algorithm for n = 3 sequences
- UPGMA / WPGM (Unweighted / Weighted Pair Group Method using Arithmetic mean)
Basics of RNA folding
Introductional Presentations - Whatabout
- Background about the topics / algorithms / modules (Wherefor? Why? For which reason?)
- Prepare a coarse overview to each Algorithm.
(What is special? Where is the advantage? ...)
- Focus on: "I basically understand, how it works!"
- Prepare the mini-talks, even if you have problems in understanding - we will discuss them together!
- Maximum 10 mins per algorithm! Keep it short!
Course materials will be made public here, when the time for the different items has come!
- Peter Clote and Rolf Backofen. Computational Molecular Biology: An Introduction.
Jon Wiley & Sons, Chichester, August 2000.
- Richard Durbin, Sean Eddy, Anders Krogh and Graeme Mitchison. Biological sequence analysis -
Probabilistic models of proteins and nucleic acids. Cambridge University Press, Cambridge, 1998.
- Dan Gusfield. Algorithms on Strings, Trees, and Sequences. Cambridge University Press, Cambridge, 1997.