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
Department
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
Syllabus
- Introduction
- Primary and derived bioinformatic data
- Genomes and genome analysis methods
- Uniprot and sequence analysis methods
- Statistical, information-theory and linguistic aspect of data
- Coding algorithms for biological sequence analysis
- PDB and structural data analysis
- Gene Ontology and functional data analysis
- Integration of data from multiple sources for genomics and proteomics
- Tools and libraries for software development (Biopython)
- Visualization tools (PyMol)
- Bioinformatics and nanotechnology: DNA computing, sequencing by hybridization
- Recent trends
Exercise in computer lab
13 hours, compulsory
Teacher / Lecturer
Syllabus
- Biological sequence analysis
- Genome Browser, Biomart
- Biopython a PyMol
- R/Bioconductor
- Integration of bioinformatic data
Project
6 hours, compulsory
Teacher / Lecturer
Syllabus
Design and implementation of an integrated computational tool for bioinformatics and its presentation on a mini-conference.