Clustering methods and algorithms in genomics and evolution

ESCI-BDIB, January 2018

Cedric Notredame





OBJECTIVES

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.




REQUISITES

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.



Send your Questions to: cedric.notredame@crg.eu



DateLocationSessionTitleLinks
1CMAGLECTUREHMM modeling 1 - Markov ChainsL
2CMAGLECTUREHMM modeling 2 - Hidden Markov ModelsL
3CMAGLECTUREHMM modeling 3 - Pair HMML
4CMAGLECTUREBWT Algorithm -L
5CMAGLECTUREGenome Assembly Algorithms L
.
.
1CMAGPRACTICALSModelling DataP
2CMAGPRACTICALSViterbi Decoding of Existing Data with Known ModelP
3CMAGPRACTICALSUsing Viterbi to Train a Model on available dataP
4CMAGPRACTICALSUsing Nextflow to quantify expression dataP
.



REFERENCES

1. Algorithms: Durbin et al., Biological Sequence Analysis, 1999, Oxford Press

2. Algorithms: Python for Biologists: A complete programming course for beginners, Martin Jones,2013 , Createspace Independent Publishing Platform

3. Evolution: Pathy, Protein Evolution, 2007, Blackwell



This Entire Course Was Automatically Generated Using BED, the Bioinformatics Exercise Database. BED is a freeware available on request Cedric Notredame