Clustering methods and algorithms in genomics and evolution


Cedric Notredame


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 Wheeler 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.

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09/01/20 - 18:00CMAGLECTUREHMM modeling 1 - Markov ChainsL
15/01/20 - 15:00CMAGLECTUREHMM modeling 2 - Hidden Markov ModelsL
22/01/20 - 15:00CMAGLECTUREHMM modeling 3 - Pair HMML
29/01/20 - 15:00CMAGLECTUREBWT Algorithm L
07/02/20 - 17:30CMAGLECTUREEXAM
17/01/20 - 17:30CMAGPRACTICALSModelling DataP
24/01/20 - 17:30CMAGPRACTICALSViterbi Decoding of Existing Data with Known ModelP
31/01/20 - 17:30CMAGPRACTICALSUsing Viterbi to Train a Model on available dataP
05/02/20 - 15:00CMAGPRACTICALSProject TutoringP


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

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