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13M111GI - Computational Genomics

Course specification
Course title Computational Genomics
Acronym 13M111GI
Study programme Electrical Engineering and Computing
Module Computer Engineering and Informatics
Type of study master academic studies
Lecturer (for classes)
Lecturer/Associate (for practice)
Lecturer/Associate (for OTC)
    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
    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
    1. R. Durbin, S. Eddy, A. Krogh, G. Mitchison, "Biological Sequence Analysis", Cambridge University (Original title)
    2. N. Jones, P. Pevzner, "An Introduction to Bioinformatics Algorithms", MIT Press (Original title)
    3. D. Gusfield, "Algorithms on Strings, Trees and Sequences", Cambridge University Press (Original title)
    4. 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