Faculty of Information Technology, BUT

Course details

Advanced Bioinformatics

PBI Acad. year 2019/2020 Winter semester 4 credits

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).


Lexa Matej, Ing., Ph.D. (FI MUNI)

Deputy Guarantor

Language of instruction



Examination (written+oral)

Time span

20 hrs lectures, 13 hrs pc labs, 6 hrs projects

Assessment points

51 exam, 29 labs, 20 projects



Lexa Matej, Ing., Ph.D. (FI MUNI)


Lexa Matej, Ing., Ph.D. (FI MUNI)
Puterová Janka, Ing. (DIFS FIT BUT)

Subject specific learning outcomes and competences

Knowledge of less-common algorithm and analysis methods, better ability to design and implement algorithms for bioinformatics.

Generic learning outcomes and competences

Deeper understanding the role of computers in the analysis and presentation of biological data.

Learning objectives

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.

Why is the course taught

Real word bioinformatics problems often require advanced knowledge. Even though each problem is usually specific, some of the tasks are common, for instance, advanced sequence alignment methods based on suffix trees, next-generation sequencing data processing, differential expression and enrichment analysis, etc. The goal of this course is to familiarize a student with these techniques and prepare him/her for a future profession in the area of bioinformatics.

Study literature

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

Fundamental literature

Syllabus of lectures

  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

Syllabus - others, projects and individual work of students

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

Progress assessment

Project, computer labs assignments.

Exam prerequisites



Thulecturelectures A112 14:0015:50 1MIT 2MIT MBI xx

Course inclusion in study plans

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