Course detail

Advanced Bioinformatics

FIT-PBIAcad. year: 2025/2026

During the lectures, the students will get acquainted with areas integrating different bioinformatic data-types. They will study possibilities of data integration to solve specific problems or create specific computational tools. Textbook material will be supplemented by recently published scientific papers. Students will work on individual computational modules in the exercises/projects leading to the creation of an integrated whole-class tool suitable for general bioinformatic analysis (functional annotation, structural prediction, molecule visualization).

Language of instruction

Czech

Number of ECTS credits

4

Mode of study

Not applicable.

Guarantor

Ing. Matej Lexa, Ph.D.

Department

Department of Computer Systems (UPSY)

Entry knowledge

Not applicable.

Rules for evaluation and completion of the course

Project, computer labs assignments.

Aims

To build on the introductory bioinformatics course. Introduce the students to selected, fast-evolving, or otherwise noteworthy areas of bioinformatics. To allow space for creative activities resulting in the creation of a computational tool based on studied principles.
Knowledge of less-common algorithm and analysis methods, better ability to design and implement algorithms for bioinformatics.
Deeper understanding the role of computers in the analysis and presentation of biological data.

Study aids

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Protein Data Bank URL: http://www.pdb.org/
UniProt URL: http://www.expasy.uniprot.org/
Zvelebil M., Baum J.: Understanding bioinformatics. Garland Science, London, 2007 ISBN 978-0815340249 UniProt URL: 

Recommended reading

Jones N.C., Pevzner P.: An introduction to algorithms in bioinformatics. MIT Press, 2004, ISBN 978-0262101066

Zvelebil M., Baum J.: Understanding bioinformatics. Garland Science, London, 2007 ISBN 978-0815340249.

Classification of course in study plans

  • Programme MITAI Master's

    specialization NSEC , 0 year of study, winter semester, elective
    specialization NISY up to 2020/21 , 0 year of study, winter semester, elective
    specialization NNET , 0 year of study, winter semester, elective
    specialization NMAL , 0 year of study, winter semester, elective
    specialization NCPS , 0 year of study, winter semester, elective
    specialization NHPC , 0 year of study, winter semester, elective
    specialization NVER , 0 year of study, winter semester, elective
    specialization NIDE , 0 year of study, winter semester, elective
    specialization NISY , 0 year of study, winter semester, elective
    specialization NEMB , 0 year of study, winter semester, elective
    specialization NSPE , 0 year of study, winter semester, elective
    specialization NEMB , 0 year of study, winter semester, elective
    specialization NBIO , 2 year of study, winter semester, compulsory
    specialization NSEN , 0 year of study, winter semester, elective
    specialization NVIZ , 0 year of study, winter semester, elective
    specialization NGRI , 0 year of study, winter semester, elective
    specialization NADE , 0 year of study, winter semester, elective
    specialization NISD , 0 year of study, winter semester, elective
    specialization NMAT , 0 year of study, winter semester, elective

Type of course unit

 

Lecture

20 hod., optionally

Teacher / Lecturer

Ing. Matej Lexa, Ph.D.

Syllabus

  1. Introduction
  2. Primary and derived bioinformatic data
  3. Genomes and genome analysis methods
  4. Uniprot and sequence analysis methods
  5. Statistical, information-theory and linguistic aspect of data
  6. Coding algorithms for biological sequence analysis
  7. PDB and structural data analysis
  8. Gene Ontology and functional data analysis
  9. Integration of data from multiple sources for genomics and proteomics
  10. Tools and libraries for software development (Biopython)
  11. Visualization tools (PyMol)
  12. Bioinformatics and nanotechnology: DNA computing, sequencing by hybridization
  13. Recent trends

Exercise in computer lab

13 hod., compulsory

Teacher / Lecturer

Ing. Matej Lexa, Ph.D.

Syllabus

  1. Biological sequence analysis
  2. Genome Browser, Biomart
  3. Biopython a PyMol
  4. R/Bioconductor
  5. Integration of bioinformatic data

Project

6 hod., compulsory

Teacher / Lecturer

Ing. Matej Lexa, Ph.D.

Syllabus

Design and implementation of an integrated computational tool for bioinformatics and its presentation on a mini-conference.