Course detail

Analysis of Biological Sequences

FEKT-FABSAcad. year: 2010/2011

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

Czech

Number of ECTS credits

6

Mode of study

Not applicable.

Guarantor

prof. Ing. Valentine Provazník, Ph.D.

Department

Department of Biomedical Engineering (UBMI)

Learning outcomes of the course unit

Practical knowledge of design of methods for analysis of biological sequences.

Prerequisites

The subject knowledge on the Bachelor´s degree level is requested.

Co-requisites

Not applicable.

Planned learning activities and teaching methods

Teaching methods depend on the type of course unit as specified in the article 7 of BUT Rules for Studies and Examinations.

Assesment methods and criteria linked to learning outcomes

Requirements for completion of a course are specified by a regulation issued by the lecturer responsible for the course and updated for every year.

Course curriculum

Probability concepts in basic molecular biology. Pairwise alignment algorithms. Dynamic programming. Markov models and hidden Markov models (HMM). HMMs to gene finding. Other algorithms in gene-finding.

Work placements

Not applicable.

Aims

The aim of the course is to provide knowledge about advanced methods for analysis of biological sequences based on probability approach including hidden Markov models. Applications cover pairwise alignment, gene finding and phylogenetic trees.

Specification of controlled education, way of implementation and compensation for absences

Limitations of controlled teaching and its procedures are specified by a regulation issued by the lecturer responsible for the course and updated for every year.

Recommended optional programme components

Not applicable.

Prerequisites and corequisites

Not applicable.

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)

Recommended reading

Pevzner, P. A. An Introduction to Bioinformatics Algorithms (Computational Molecular Biology. The MIT Press, 2004. ISBN: 978-0262101066 (EN)

Classification of course in study plans

  • Programme BTBIO-F Master's

    branch F-BTB , 1 year of study, summer semester, compulsory

Type of course unit

 

Lecture

26 hod., optionally

Teacher / Lecturer

prof. Ing. Valentine Provazník, Ph.D.

Syllabus

1. Probability concepts in basic molecular biology.
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

26 hod., compulsory

Teacher / Lecturer

Ing. Helena Vítková, Ph.D.

Syllabus

1. Classical and Bayes probability.
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.