Course detail
Analysis of Biological Sequences
FEKT-MPC-ABSAcad. year: 2021/2022
The subject provides statistical foundations and an overview of the core algorithms of sequence analysis. Topics covered will include background on probability, Hidden Markov Models, and multiple hypothesis testing. Sequence analysis algorithms will include alignment, optimal pairwise local alignment, pairwise global alignment and multiple alignment, gene finding and phylogenetic trees.
Language of instruction
Number of ECTS credits
Mode of study
Learning outcomes of the course unit
- describe basic methods of computer processing of symbolic sequences,
- explain characteristics of DNA and protein evolution,
- describe principle of methods for construction and analysis of fylogenetic trees,
- discus advantages and disadvantages of the methods,
- explain principle of numeric conversion of symbolic biological sequences.
Prerequisites
Co-requisites
Planned learning activities and teaching methods
Assesment methods and criteria linked to learning outcomes
up to 60 points from finel written exam
The exam is oriented to verification of orientation in terms of advanced processing of biological sequences, ability to design methods for sequence analysis, apply operations on sequences.
Course curriculum
2. Models of sequence evolution.
3. Models of protein evolution.
4. Fylogenetic trees.
5. Construction of fylogenetic trees.
6. Evaluation of fylogenetic analysis.
7. Numerical representation of genomic data.
8. Numerical conversion.
9. Description of protein structure.
10. NGS data processing.
11. Metagenomics.
Work placements
Aims
Specification of controlled education, way of implementation and compensation for absences
Recommended optional programme components
Prerequisites and corequisites
Basic literature
Durbin, R. Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids. Cambridge University Press, 2002. ISBN: 978-0521629713 (EN)
Rosypal, S. Nový přehled biologie. Scientia, Praha 2003. ISBN 80-7183-268-5 (CS)
Srinivasa, K. G. Statistical Modelling and Machine Learning Principles for Bioinformatics Techniques, Tools, and Applications. Springer, 2020. ISBN 978-9811524448 (EN)
Recommended reading
Pevzner, P. A. An Introduction to Bioinformatics Algorithms (Computational Molecular Biology. The MIT Press, 2004. ISBN: 978-0262101066 (EN)
Elearning
Classification of course in study plans
- Programme MPC-BTB Master's 1 year of study, summer semester, compulsory
Type of course unit
Lecture
Teacher / Lecturer
Syllabus
2. Classic and modern pairwise alignment algorithms.
3. Statistical significance of alignment scores and the interpretation of alignment algorithm's output.
4. Mechanism and the use of dynamic programming.
5. Implementation of Needleman-Wunch and Smith-Waterman algorithms.
6. Multiple alignment and phylogenetic reconstruction.
7. Evolution assumed by different models and algorithms.
8. Likelihood approach to phylogenetic reconstruction.
9. Markov models and hidden Markov models (HMM) in the genomic context.
10. Essential algorithms for making inference on HMM.
11. HMMs to gene finding.
12. Other algorithms in gene-finding.
13. Identify important algorithmic/statistical advances in bioinformatics that address biologically important questions.
Exercise in computer lab
Teacher / Lecturer
Syllabus
2. Pairwise alignment algorithms.
3. Computing alignment scores and the interpretation of alignment algorithm's output.
4. Algorithms for dynamic programming.
5. Implementation of Needleman-Wunch and Smith-Waterman algorithms.
6. Multiple alignment.
7. Tracking sequence evolution.
8. Phylogenetic reconstruction.
9. Markov models in the genomic context.
10. Hidden Markov models in the genomic context.
11. HMMs to gene finding I.
12. HMMs to gene finding II.
13. Other algorithms in gene-finding.
Project
Teacher / Lecturer
Syllabus
Elearning