Subject description - BE4M36BIN

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BE4M36BIN Bioinformatics
Roles:PO Extent of teaching:2P+2C
Department:13136 Language of teaching:EN
Guarantors:  Completion:Z,ZK
Lecturers:  Credits:5
Tutors:  Semester:L

Web page:

https://cw.fel.cvut.cz/wiki/courses/bin/start

Anotation:

The goal of the course is to explain the principles used in algorithms for processing molecular data. The course contains algorithms for sequence assembly, sequence alignment, sequence probabilistic and grammatical modelling, algorithms used for finding connections between primary and secondary/tertial structure of proteins and their functions and interactions, algorithms for analysis of data from highly parallel measurements (especially gene expression), and algorithms for modelling processes as metabolism and regulation of gene expression.

Course outlines:

1. Intro, sample bioinformatics tasks.
2. Sequencing algorithms, fragment assembly.
3. Sequence alignment.
4. Multiple sequence alignment.
5. Phylogenetic trees, distance methods.
6. Phylogenetic trees, parsimony and probabilistic methods.
7. Markov chains in computational biology.
8. Hidden Markov models of genomic sequences, gene finidng, profile HMMs.
9. Gene expression profiling.
10. RNA secondary structure prediction.
11. Modeling of higher protein structures, protein databases.
12. Gene ontology, gene/protein function prediction.
13. Network inference and modeling.
14. Spare lecture.

Exercises outline:

1. Intro, sample bioinformatics tasks.
2. Sequencing algorithms, fragment assembly.
3. Sequence alignment.
4. Multiple sequence alignment.
5. Phylogenetic trees, distance methods.
6. Phylogenetic trees, parsimony and probabilistic methods.
7. Markov chains in computational biology.
8. Hidden Markov models of genomic sequences, gene finidng, profile HMMs.
9. Gene expression profiling.
10. RNA secondary structure prediction.
11. Modeling of higher protein structures, protein databases.
12. Gene ontology, gene/protein function prediction.
13. Network inference and modeling.
14. Spare lab.

Literature:

1. Durbin et al.: Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids. Cambridge University Press, 1998.
2. Jones, Pevzner: An Introduction to Bioinformatics Algorithms, The MIT Press, 2004.
3. Lesk, A.M.: Introduction to Bioinformatics. Oxford University Press, 4th Edition, 2014.

Requirements:

Subject is included into these academic programs:

Program Branch Role Recommended semester
MEOI8_2018 Bioinformatics PO 2


Page updated 24.7.2024 12:51:44, semester: L/2024-5, L/2023-4, Z/2024-5, Z/2023-4, Send comments about the content to the Administrators of the Academic Programs Proposal and Realization: I. Halaška (K336), J. Novák (K336)