Course: Courses of work with molecular data in R

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Course title Courses of work with molecular data in R
Course code KBO/148
Organizational form of instruction Lesson
Level of course Doctoral
Year of study not specified
Frequency of the course In academic years starting with an odd year (e.g. 2019/2020), in the winter semester.
Semester -
Number of ECTS credits 4
Language of instruction English
Status of course Compulsory-optional
Form of instruction Face-to-face
Work placements This is not an internship
Recommended optional programme components None
Course availability The course is available to visiting students
Lecturer(s)
  • Štech Milan, doc. Ing. Ph.D.
  • Zeisek Vojtěch, Mgr. Ph.D.
Course content
Content of practicals: Basic methods of work R programming language, theory of methods used (there are short lectures before every practical session) Basic work in R - how to enter commands, install packages, read help, types of variables, indexes, etc.; Bioconductor; Load and export molecular data of various types and formats; Download molecular data from on-line databases; Extractions of SNP from sequencing data; Extraction of polymorphism from sequences; Mikrosatellites, AFLP, SNP, sequences, ?; Manipulations with data, conversions among formats; Distance matrices, import of custom matrices; Export of data; Basic statistics; PCoA; Phylogenetic trees (NJ, UPGMA, ML) and display and test; MSN; Basic statistics, genetic indices heterozygosity, HWE, F-statistics; DAPC; Whole genome SNP data; Spatial analysis - Mantel test, Moran's I, Monmonier, sPCA, ?; Basic map creation; Structure; Alignments; Manipulations with trees, work with big sets of trees; Phylogenetic independent contrast; Phylogenetic autocorrelation; Phylogenetic PCA; Ancestral state reconstruction; Additional extending topics?

Learning activities and teaching methods
Activating (simulations, games, drama)
  • Preparation for classes - 25 hours per semester
  • Preparation for credit - 25 hours per semester
  • Class attendance - 40 hours per semester
Learning outcomes
To teach students analysis of molecular data in R programming language, introduce packages available for analysis of molecular data. Practical work, analyzing of own or provided data.

Prerequisites
unspecified

Assessment methods and criteria
Interim evaluation

Recommended literature
  • http://ape-package.ird.fr/APER.html.
  • https://a-little-book-of-r-for-bioinformatics.readthedocs.io/en/latest/.
  • https://cran.r-project.org/manuals.html.
  • https://cran.r-project.org/web/packages/pegas/vignettes/ReadingFiles.pdf.
  • https://github.com/thibautjombart/adegenet/wiki/Tutorials.
  • https://www.r-phylo.org/wiki/Main_Page.
  • http://www.cookbook-r.com/.
  • http://zoonek2.free.fr/UNIX/48_R/all.html.


Study plans that include the course
Faculty Study plan (Version) Category of Branch/Specialization Recommended year of study Recommended semester
Faculty: Faculty of Science Study plan (Version): Botany (1) Category: Biology courses - Recommended year of study:-, Recommended semester: -
Faculty: Faculty of Science Study plan (Version): Botany (1) Category: Biology courses - Recommended year of study:-, Recommended semester: -
Faculty: Faculty of Science Study plan (Version): Botany (1) Category: Biology courses - Recommended year of study:-, Recommended semester: -
Faculty: Faculty of Science Study plan (Version): Botany (1) Category: Biology courses - Recommended year of study:-, Recommended semester: -