Practics of Bioinformatics
FEKT-APBIAcad. year: 2017/2018
The course is focused on practical application of basic bioinformatical analyses of DNA and amino acids sequences. Primarily, it is oriented on global, local and multi alignment algorithms and algorithms for RNA and protein sequence secondary structure prediction. The signal processing methods for genomic and proteomic data analyses are studied. Further, practical application of phylogenetic tree construction is applied on suitable dataset of DNA sequences. Students will learn how to analyse sequences in R programming language.
Learning outcomes of the course unit
The student is able to:
- find protein-coding DNA sequences in GenBank database and load the data in desired format
- find protein sequence, which is coded by the DNA sequence, in Uniprot database
- find coding regions in DNA sequences
- analyse sequences in R
- use alignment online tools and suitably choose scoring parameters according data type
- program algorithms for alignment with afinne penalty
- predict secondary structure of protein sequences with online tools
- predict positive selection in genes
- program calculation of DNA spectrograms
- construct phylogenetic tree from DNA sequences by online tools
Student should have knowledge equivalent to completion of the course ABIN. Student should be able to work with Matlab or other programming language. Student have to know the basics of molecular biology of nucleotide and protein sequences and understand to principles of global and local sequence alignment and principle of protein secondary structure prediction.
Recommended optional programme components
Recommended or required reading
Cvrčková F: Úvod do praktické bioinformatiky, Academia, 2006 (CS)
Koonin E. V., Galperin M. Y. (2003) Sequence - Evolution - Function: Comparative approaches in comparative genomics. Kluwer Acad. Press. (EN)
Gusfield D. (1997) Algorithms on strings, trees, and sequences. Cambridge Univ. Press. (EN)
Planned learning activities and teaching methods
Teaching methods depend on the type of the course unit as specified in the article 7 of BUT Rules for Studies and Examinations.
Assesment methods and criteria linked to learning outcomes
Student can get maximally 30 points for programming, maximally 30 points for practical work and maximally 40 points for theoretical test. It is necessary to get minimally 15 points for the programming, minimally 20 points for the theoretical test and minimally 50 points in sum to successfully pass the course.
Language of instruction
1. Basic programming in R.
2. Biostrings library.
3. Regular expressions and data formats.
4. Exon searching.
5. Sequence alignment with affine penalty.
7. Phylogenetic tree construction.
8. Prediction of positive selection.
9. RNA structure prediction.
10. Protein structure prediction.
The goal of this course is to teach the students how to search in the basic genomic and proteomic databases like GenBank and Uniprot, analyse data from the databases with commonly used bioinformatic online tools, and the student are also teached how to program some basic bioinformatics algorithms in R programming language.
Specification of controlled education, way of implementation and compensation for absences
Computer exercises are mandatory, properly excused missed lectures can be compensated individually after discussion with teacher.