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The course introduces a multidisciplinary field of bioinformatics. Bioinformatics deals with the methods for storing, retrieving and analyzing biological data, such as nucleic acid (DNA/RNA) and protein sequence, structure, function, pathways and genetic interactions. Students will be introduced to the basic concepts of bioinformatics and computational biology. Hands-on sessions will familiarize students with the details and use of common online tools and resources.
Last update: Hladíková Jana (04.01.2018)
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A credit: a practical project + a protocol An exam: a written test Last update: Svozil Daniel (26.01.2018)
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R:A. Lesk, Introduction to Bioinformatics, Oxford University Press 2014, ISBN 9780199651566 A:P. M. Selzer a kol., Applied Bioinformatics: An Introduction, Springer 2018, ISBN 3319682997 A:J. Pevsner, Bioinformatics and Functional Genomics, Wiley 2017, ISBN 9788126567683 Last update: Svozil Daniel (21.03.2019)
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A credit: a practical project An exam: a written test Last update: Svozil Daniel (17.02.2018)
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1. Genomics, genome mapping and sequencing, human genome project 2. Pairwise sequence alignment - homology and similarity, basic principles of alignment 3. Pairwise sequence alignment - scoring, substitution matrices PAM and BLOSUM, dot plot 4. Searching in sequence databases, BLAST 5. Multiple sequence alignment - scoring and creation 6. Position specific substitution matrices, profiles and Hidden Markov Models, PSI-BLAST 7. Molecular phylogenetic analysis - molecular evolution, phylogenetic trees, models of evolution 8. Molecular phylogenetic analysis - methods of phylogenetic trees construction, evaluationg phylogenetic trees 9. Gene detection in eukaryotes and prokaryotes, prediction of introns and exons 10. DNA microarrays, gene expressionanalysis 11. Structure of biomacromolecules - Protein Databank, protein structure classification - SCOP and CATH 12. Prediction of the secondary protein structure 13. Prediction of the tertiary protein structure - homology modeling, threading, ab initio methods, CASP 14. RNA structure and its prediction Last update: Hladíková Jana (04.01.2018)
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Lecture (video and slides) "Computational molecular biology" at Stanfordu: http://biochem218.stanford.edu/ Videocourse "DNA/Protein Sequence Analysis" by Amy Denton at iTunes: https://itunes.apple.com/itunes-u/dna-protein-sequence-analysis/id472584215?mt=10 Videocourse "Skiena's Computational Biology Lectures" is focused on how the algorithms work than on how to use them: http://www.algorithm.cs.sunysb.edu/computationalbiology/ Book "Applied Statistics for Bioinformatics using R" by Wim P. Krijnen - http://cran.r-project.org/doc/contrib/Krijnen-IntroBioInfStatistics.pdf Last update: Svozil Daniel (21.03.2019)
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Students will know: Basics principles of the sequence similarity and homology. Sequence alignment and sequence database searching. Molecular phylogenetic analysis. Prediction of genes and their structure. Analysis of DNA microarrays data. Nucleic acids and proteins structure and its prediction. Last update: Hladíková Jana (04.01.2018)
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Molecular Biology/Genetics, Biochemistry Last update: Hladíková Jana (04.01.2018)
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Teaching methods | ||||
Activity | Credits | Hours | ||
Účast na přednáškách | 1 | 28 | ||
Příprava na přednášky, semináře, laboratoře, exkurzi nebo praxi | 1 | 28 | ||
Práce na individuálním projektu | 1 | 28 | ||
Příprava na zkoušku a její absolvování | 1 | 28 | ||
Účast na seminářích | 1 | 28 | ||
5 / 5 | 140 / 140 |
Coursework assessment | |
Form | Significance |
Defense of an individual project | 30 |
Examination test | 70 |