Clustering methods and algorithms in genomics and evolution
An intermediate level course on algorithms in bioinformatics. We will use it to introduce biological modelling using BAyesian statistics such as Markov Chains and Hidden Markov Models. We will also introduce basic algorithmic techniques for fast sequence comparison and clustering used in genome assembly such as Burrow Wheelet Transform based sequence matching.
Practicals will be in python 2.7. They will involve adapting existing scripts rather than programming from scratch. For this reason, students with no practical knowledge of python but with a good grasp of programming languages like Perl, C or JAVA should manage reasonnably well. Students are expected to bring their own lap-top and advised to have a LINUX boot though Virtual MAchine support will be provided.
|1||CMAG||LECTURE||HMM modeling 1 - Markov Chains||L|
|2||CMAG||LECTURE||HMM modeling 2 - Hidden Markov Models||L|
|3||CMAG||LECTURE||HMM modeling 3 - Pair HMM||L|
|4||CMAG||LECTURE||BWT Algorithm -||L|
|5||CMAG||LECTURE||Genome Assembly Algorithms||L|
|2||CMAG||PRACTICALS||Viterbi Decoding of Existing Data with Known Model||P|
|3||CMAG||PRACTICALS||Using Viterbi to Train a Model on available data||P|
|4||CMAG||PRACTICALS||Using Nextflow to quantify expression data||P|
This Entire Course Was Automatically Generated Using BED, the Bioinformatics Exercise Database. BED is a freeware available on request Cedric Notredame