# Programming course "Algorithms in Bioinformatics"

## Tutors

## 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!

### Requirements

- Literature Search
- Description of the Algorithms (in LaTeX-Format)
- well documented source code (Python)
- Input-failure robust parameter parsing

### Dates

- 22.Oct 2018 Introduction / Overview and Distribution of Topics
- 12.Nov.2018 Presentations (10-20 min)
- 19.Nov.2018 Introduction to the project (implement the algorithms using Python)

## Topics

### Pairwise Sequence-Alignment

- Needleman-Wunsch-Algorithm
- Gotoh-Algorithm

### Multiple Sequence-Alignment and Phylogeny/Clustering

#### Optimal Approach

- Needleman-Wunsch - Algorithm for n = 3 sequences

#### Heurisitc Approach

- Sum-of-Pairs-Algorithm
- Feng-Doolittle-Algorithm
- 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!

## Material

Course materials will be made public here, when the time for the different items has come!

## Literature

- 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.