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Lecturer(s)
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Šebela Marek, prof. Mgr. Dr.
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Raus Martin, Mgr. Ph.D.
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Škrabišová Mária, Mgr. Ph.D.
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Course content
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Definition of bioinformatics, its historical relation to genomics. Goals of bioinformatics and its methods. Sequences of proteins and nucleic acids. Sequence analysis methods for nucleic acids and proteins with an emphasis on modern methods used after 2000. Formats for writing nucleotide and amino acid sequences. Publicly available sequence databases, sources of bioinformatics data. Options for getting information from a sequence. Database of sequence motifs. Prediction of protein cellular localization, post-translational modifications and secondary structures. Gene Ontology. Relationships between sequences, sequence homology. Global and local assignment of sequences. Needleman-Wunsch and Smith-Waterman algorithms. Pairwise and multiple assignments, substitution matrices. Sequential logo. Searching databases by similarity to a known sequence, programs FASTA, BLAST and its variants. Searching sequence databases by structure query. Phylogenetic trees (dendrograms) and their representation. Construction of dendrograms: character-based approaches and distance matrix-based approaches. Evaluation of dendrogram quality. Spatial structures of proteins. Protein Data Bank database, PDB format, SCOP database. Software for molecular graphics of proteins. Protein structure prediction. Automatic modeling of protein structures, AlphaFold Database. Ligand docking, AutoDock Vina. Gene prediction, eukaryotic gene and its structure (protein annotation), origin of genes and their evolution, prediction methods. RNA structure: non-coding RNA types, their significance, RNA secondary structure, RNA prediction, RNA structure database. Importance of miRNA and siRNA for regulation of gene expression. Genetic diversity: genetic diversity of populations, types of polymorphisms and their origin, consequences of polymorphisms, database of polymorphisms, database of human diseases inherited according to Mendelian genetics, sequencing, genotyping, file formats, haplotype, haplotype analysis, association studies (GWAS). Glycoproteins. Types of protein glycosylation and partial classification. Information obtained during the analysis of glycoproteins. Comparison of different ways of storing data - types of data files/storage, method of accessing and processing data, advantages and disadvantages of individual types. Limits of current computing technology. Complexity and solvability of algorithms, relationship between complexity and volume of data. Methods of processing, advantages and limitations in the parallelization of computing processes.
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Learning activities and teaching methods
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Monologic Lecture(Interpretation, Training), Dialogic Lecture (Discussion, Dialog, Brainstorming)
- Preparation for the Exam
- 55 hours per semester
- Attendace
- 26 hours per semester
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Learning outcomes
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The subject explains theoretical and practical aspects of bioinformatics. There are biological databases included as well as sequence alignment, gene and protein structures, protein structure prediction, molecular phylogenetics, genomics, proteomics and glycobiology. Students will get acquainted themselves with bioinformatic tools and they will develop skills in retrieval, processing and presentation of bioinformatic data. In addition, they become experienced in fundamental programming and script languages.
Students become competent in bioinformatics and will get acquainted with bioinformatic tools and their applications.
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Prerequisites
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successful passing of the subjects from the first three semesters of the study plan Bioinformatics (bachelor level), namely the subjects KMI/UDI and KBC/UBCH-UBC.
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Assessment methods and criteria
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Written exam, Seminar Work
The lecture is supplementted with a seminar for solving tasks under supervision of the teacher, doing homeworks each week and a requirement for completing a final bioinformatic project. Students are obliged to pass 2 written examination tests in the seminar (60% of points for a successful passing).
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Recommended literature
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Baxevanis, A.D.; Bader, G.D.; Wishart, D.S. (Eds.). (2020). Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins. New York, USA.
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Bourne, P.E.; Weissig, H. (2003). Structural Bioinformatics. Hoboken, NJ, USA.
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Claverie, J.-M.; Notredame, C. (2007). Bioinformatics for Dummies. Hoboken.
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Dandekar, T.; Kunz, M. (2023). Bioinformatics: An Introductory Textbook.
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Gibas, C.; Jambeck, P. (2001). Developing Bioinformatics Computer Skills.
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St. Clair, C.; Visick, J.E. (2015). Exploring Bioinformatics: A Project-Based Approach. Burlington, MA, USA.
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von der Lieth, C.-W.; Lütteke, T.; Frank, M. (Eds.). Bioinformatics for Glycobiology and Glycomics: an Introduction. Chichester, UK. 2009.
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Xiong, J. (2006). Essential Bioinformatics. Cambridge.
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Zvelebil, M.; Baum, J.O. (2008). Understanding Bioinformatics. New York.
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