Advanced analysis of large genomic data
FEKT-DBT1Acad. year: 2019/2020
Representation of genomic and proteomic data. Deterministic and probabilistic models of sequence evolution. Methods of construction of large phylogenetic trees. Motifs searching in genomic and proteomic sequences.
Learning outcomes of the course unit
Recommended optional programme components
Recommended or required reading
Snustad, D. P., Simmons, M. J. Genetika. Nakladatelství Masarykovy univerzity, Brno, 2009.
Higgs, P. G., Attwood, T. K. Bioinformatics and Molecular Evolution. Blackwell Publishing, 2005.
Yang, Z. Computational Molecular Evolution. Oxford University Press, 2006.
Mandoiu, I. I., Zelikovski, A. Bioinformatics Algorithms. A John Wiley Publishing, 2008.
Planned learning activities and teaching methods
Assesment methods and criteria linked to learning outcomes
Language of instruction
1. Principles of representation of genomic data.
2. Deterministic descriptionof sequences in space.
3. The theory of fractals in the representation of sequences.
4. Deterministic models of sequence evolution.
5. Probabilistic models of sequence evolution.
6. Advanced principles of phylogenetics.
7. Methods of construction of large phylogenetic trees.
8. Quality in phylogenetics.
9. Principles of representation of proteomic data.
10. Advanced encoding of proteomic data.
11. Motif search in genomic and proteomic sequences.
The aim is to provide an overview of current topics in the field of biological sequence processing with a focus on deterministic and probabilistic methods.