Course detail

Advanced Bioinformatics

FIT-PBIAcad. year: 2020/2021

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).

Supervisor

Ing. Matej Lexa, Ph.D.

Department

Department of Computer Systems (UPSY)

Learning outcomes of the course unit

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.

Prerequisites

Not applicable.

Co-requisites

Not applicable.

Recommended optional programme components

Not applicable.

Recommended or required 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.
Xinkun Wang, Next-Generation Sequencing Data Analysis, ISBN: 978-1482217889, CRC Press, 2016.
UniProt URL: http://www.expasy.uniprot.org/
Gene Ontology URL: http://www.geneontology.org
Protein Data Bank URL: http://www.pdb.org/

Planned learning activities and teaching methods

Not applicable.

Assesment methods and criteria linked to learning outcomes

Project, computer labs assignments.
Exam prerequisites:
None.

Language of instruction

Czech

Work placements

Not applicable.

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.

Classification of course in study plans

  • Programme MITAI Master's

    specialization NADE , any year of study, winter semester, 4 credits, elective
    specialization NGRI , any year of study, winter semester, 4 credits, elective
    specialization NNET , any year of study, winter semester, 4 credits, elective
    specialization NVIZ , any year of study, winter semester, 4 credits, elective
    specialization NCPS , any year of study, winter semester, 4 credits, elective
    specialization NSEC , any year of study, winter semester, 4 credits, elective
    specialization NEMB , any year of study, winter semester, 4 credits, elective
    specialization NHPC , any year of study, winter semester, 4 credits, elective
    specialization NISD , any year of study, winter semester, 4 credits, elective
    specialization NIDE , any year of study, winter semester, 4 credits, elective
    specialization NISY , any year of study, winter semester, 4 credits, elective
    specialization NMAL , any year of study, winter semester, 4 credits, elective
    specialization NMAT , any year of study, winter semester, 4 credits, elective
    specialization NSEN , any year of study, winter semester, 4 credits, elective
    specialization NVER , any year of study, winter semester, 4 credits, elective
    specialization NSPE , any year of study, winter semester, 4 credits, elective

  • Programme IT-MGR-2 Master's

    branch MBI , 2. year of study, winter semester, 4 credits, compulsory

  • Programme MITAI Master's

    specialization NBIO , 2. year of study, winter semester, 4 credits, compulsory

Type of course unit

 

Lecture

20 hours, 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 hours, 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 hours, 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.