Algorithms, databases and tools in bioinformatics - NDBI044
Title: Bioinformatické algoritmy, databáze a nástroje
Guaranteed by: Department of Software Engineering (32-KSI)
Faculty: Faculty of Mathematics and Physics
Actual: from 2020
Semester: winter
E-Credits: 5
Hours per week, examination: winter s.:2/2, C+Ex [HT]
Capacity: unlimited
Min. number of students: unlimited
4EU+: no
Virtual mobility / capacity: no
State of the course: taught
Language: Czech
Teaching methods: full-time
Teaching methods: full-time
Guarantor: doc. RNDr. David Hoksza, Ph.D.
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Annotation -
Last update: RNDr. Filip Zavoral, Ph.D. (26.05.2023)
The lecture will introduce currently used algorithms and approaches for DNA, RNA and protein analysis. In particular, it will cover algorithms for sequence similarity modeling and structures of biomolecules, phylogenetic analysis and structure prediction. Students will also learn what the major repositories of related biological data exist, what the structure of these data is, and how to work with them at the programmatic level.
Course completion requirements -
Last update: doc. RNDr. David Hoksza, Ph.D. (05.02.2024)

Written exam to the extent covered by the syllabus.

Attendance at tutorials.
Literature - Czech
Last update: T_KSI (18.04.2015)
  • Z: Zvelebil, M., Baum J.: Understanding Bioinformatics, Garland Science; 1 edition, 2007, 0815340249
  • Z: Durbin, R., et al.: Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids, Cambridge University Press, 1998, 0521629713
  • D: Mount, D.W.: Bioinformatics: Sequence and Genome Analysis, Second Edition, Cold Spring Harbor Laboratory Press, 2004, 0879696087
  • D: Gusfield, D.: Algorithms on Strings, Trees and Sequences - Computer Science and Computational Biology, Cambridge University Press, 1997, 0521585198
  • D: Jones, N.C., Pevzner, P.A.: An Introduction to Bioinformatics Algorithms, The MIT Press, 2004, 0262101068

Syllabus -
Last update: doc. RNDr. David Hoksza, Ph.D. (26.05.2023)
  • Introduction. Existing nucleotide and protein databases.
  • Sequence similarity of DNA, RNA, and proteins.
  • Efficient searching in protein sequence databases.
  • Searching motifs.
  • Multiple sequence similarity.
  • Construction of phylogenetic trees.
  • Algorithms for learning the similarity of protein structures.
  • Protein structure prediction.