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Course, academic year 2016/2017
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Bioinformatics Algorithms - NTIN084
Czech title: Bioinformatické algoritmy
Guaranteed by: Department of Theoretical Computer Science and Mathematical Logic (32-KTIML)
Faculty: Faculty of Mathematics and Physics
Actual: from 2014
Semester: winter
E-Credits: 6
Hours per week, examination: winter s.:2/2 C+Ex [hours/week]
Capacity: unlimited
Min. number of students: unlimited
State of the course: taught
Language: Czech
Teaching methods: full-time
Guarantor: RNDr. František Mráz, CSc.
Class: Informatika Mgr. - Teoretická informatika
Classification: Informatics > Theoretical Computer Science
Annotation -
Last update: RNDr. František Mráz, CSc. (21.05.2013)

In recent decades, biology has raised a lot of challenging mathematical problems aiming at deciphering the language of DNA sequences. Bioinformatics is a rapidly developing area of computer science driving further biological developments. This course is focused on explaining the main algorithmic principles applicable to the solution of various biological problems. This shall provide the students with a solid foundation to understand more easily also other parts of this emerging field. The lecture is for students of computer science without background in biology.
Aim of the course -
Last update: RNDr. František Mráz, CSc. (19.04.2013)

An introduction to bioinformatics and algorithms used within bioinformatics.

Literature -
Last update: T_KTI (23.04.2013)

Jones N. C. and Pevzner P. A.: An Introduction to Bioinformatics Algorithms, MIT Press, 2004

Pevzner P. A.: Computational Molecular Biology: An Algorithmic Approach, MIT Press, 2000

Zvelebil M., Jeremy Baum J. A.: Understanding Bioinformatics, Garland Science, 2007

Syllabus -
Last update: RNDr. František Mráz, CSc. (30.04.2015)

  1. Introduction to bioinformatics:

    • Bioinformatics - its subject, history and main problems
    • Introduction to molecular biology - the structure of DNA and its analysis, genes, proteins

  2. Classical bioinformatics algorithms:

    • Exhaustive search - restriction mapping, motif finding
    • Greedy algorithms - genome rearrangements, motif finding
    • Dynamic Programming Algorithms - similarity of DNA sequences, sequence alignment, alignment with gap penalties, gene prediction
    • Divide-and-conquer algorithms - space and time efficient sequence alignment
    • Graph algorithms - DNA sequencing, protein sequencing and identification, peptide sequencing
    • Combinatorial pattern matching - exact pattern matching, keywords trees, suffix trees, heuristic similarity search, approximate pattern matching, BLAST and FASTA

  3. Advanced bioinformatics algorithms:

    • Hidden Markov Models - decoding algorithm, HMM parameter estimation
    • Randomized algorithms - Gibbs sampling, random projections
    • Computing similarity by compression

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