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The practicals found below have primarily been developed for specific teaching duties of the group. However, you may find some of it useful for other purposes, so now it is made freely available to you here!

Practicals from the DTC statistics module 2017

The links provide a folder with different files. Look for a .pdf-file labeled instructions or project description or something similar to get started.

Markov Random Fields – Denoise an black-white image using Python

Gibbs sampling – Find Motifs in DNA-sequences using MATLAB

Clustering – Identify clusters in Wikipedia articles on birds using R

Diffusion – Solve stochastic differential equation modelling animals or stocks using your favorite programming language

Finance – Financial Forecasting with Linear and Non-Linear models using your favorite programming language

Hidden Markov Models – Modelling sequence evolution along a phylo-genetic tree using Python

Variational Auto-encoder – Perform fast variational inference with neural networks using MATLAB or Python

Practicals from Advanced Bioinformatics Module 2016

Annotation 17.3.2016 – Finding protein coding genes

Coalescent 16.3.2016 – Simulation and visualisation of coalescent trees

Comparative Biology 22.3.16  – Inferring conservation and phylogenetic footprinting

Integrative Genomics 21.3.16 – De Bruijn graph assembly from short read sequences

Phylogenetics 15.3.2016  – Introduction to phylogenies (combinatorics and maximum parsimony)

Recombination 18.3.2016 – Estimation of recombination rates and hotspots in a population using linkage disequilibrium and haplotype mapping

Substitution models 14.3.2016 – Introduction to DNA substitution models

What is a practical?

In our terminology, a practical is a problem set which mostly focuses on the computational aspects. An example could be to download the some software and apply it to analyse a data set.

This distinguishes it from exercises, which is usually solved using pen and paper.

You may also be interested in the exercises.

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