13M111GI - Computational Genomics

Course specification
Course title		Computational Genomics
Acronym		13M111GI
Study programme		Electrical Engineering and Computing
Module
Type of study		master academic studies
Lecturer (for classes)		asst. professor PhD Marko Mišić
Lecturer/Associate (for practice)		asst. MA Predrag Obradović, electrical and computer engineer
Lecturer/Associate (for OTC)		asst. MA Predrag Obradović, electrical and computer engineer
ESPB		6.0	Status	elective
Condition		The course 13M111PSZ is not a prerequisite for this new course (13M111GI); however, it is recommended that the course 13M111GI, be taken after the course 13M111PSZ. Cosequently, on the level of master studies, 13M111PSZ is located in the Fall semesters and 13M111GI in the Spring semesters.
The goal		This course presents some of the basic computational methods that can infer biological information from genomic data, the strengths and weaknesses of related methods, and the important parameters embedded in these analyses. Theoretical, applied, and statistical issues will be addressed.
The outcome		Students should be able to understand the principles of algorithm design for biological datasets, to analyze problems, and use described methods in order to locate genes, repeat families, similarities between sequences of different organisms and several other applications.
Contents
URL to the subject page		http://rti.etf.bg.ac.rs/rti/ms1gi/
URL to lectures		https://teams.microsoft.com/l/team/19%3aaRohSGJOVIYB6mXTQ6Dea5Tgjbd-npz4xFGnzYVdGH81%40thread.tacv2/conversations?groupId=1cc74ef9-1a4c-4aa0-899d-380ccc3d2658&tenantId=1774ef2e-9c62-478a-8d3a-fd2a495547ba
Contents of lectures		Sequence Alignments. Hidden Markov Models. Multiple Alignment Algorithms. Gene Expression Analysis. Population Genomics. Molecular Evolution. Comparative Genomics.
Contents of exercises		Same as for the theoretical lessons.
Literature
R. Durbin, S. Eddy, A. Krogh, G. Mitchison, "Biological Sequence Analysis", Cambridge University (Original title) N. Jones, P. Pevzner, "An Introduction to Bioinformatics Algorithms", MIT Press (Original title) D. Gusfield, "Algorithms on Strings, Trees and Sequences", Cambridge University Press (Original title) Najnoviji radovi po izboru predavača (Original title)
Number of hours per week during the semester/trimester/year
Lectures	Exercises	OTC	Study and Research	Other classes
2	2	1
Methods of teaching		Lectures and auditory practices are supplied with electronic presentations. Students work on homework projects independently.
Knowledge score (maximum points 100)
Pre obligations		Points	Final exam	Points
Activites during lectures			Test paper	50
Practical lessons			Oral examination
Projects		50
Colloquia
Seminars