Algorithms in computational genomics

General data

Course ID:	1000-2M12AGO
Erasmus code / ISCED:	11.3 The subject classification code consists of three to five digits, where the first three represent the classification of the discipline according to the Discipline code list applicable to the Socrates/Erasmus program, the fourth (usually 0) - possible further specification of discipline information, the fifth - the degree of subject determined based on the year of study for which the subject is intended. / (0612) Database and network design and administration The ISCED (International Standard Classification of Education) code has been designed by UNESCO.
Course title:	Algorithms in computational genomics
Name in Polish:	Algorytmy w genomice obliczeniowej (wspólne z 1000-719GP2)
Organizational unit:	Faculty of Mathematics, Informatics, and Mechanics
Course groups:	Elective courses for Computer Science and Machine Learning
ECTS credit allocation (and other scores):	(not available) Basic information on ECTS credits allocation principles: the annual hourly workload of the student’s work required to achieve the expected learning outcomes for a given stage is 1500-1800h, corresponding to 60 ECTS; the student’s weekly hourly workload is 45 h; 1 ECTS point corresponds to 25-30 hours of student work needed to achieve the assumed learning outcomes; weekly student workload necessary to achieve the assumed learning outcomes allows to obtain 1.5 ECTS; work required to pass the course, which has been assigned 3 ECTS, constitutes 10% of the semester student load. view allocation of credits
Language:	English
Type of course:	elective monographs
Short description:	The main aim of this lecture is to introduce students with selected models, algorithms and tools used in comparative genomics. In particular we will be focused on algorithms and tools related to trees (in many contexts). Classes will be partially converted to labs.
Full description:	1. Introduction. Elementary defintions, genes, species, genomes, ewolution, alignments, sequence comparison (2 lectures). 2. Models of sequence evolution (1). 3. Maximum likelihood estimation, maximum parsimony, nearest neighbor joining (2) 4. Bayes methods (1-2) 5. Consesus trees and supertrees (2) 6. Hierarchical clustering (1) 7. Reconciled trees, networks, horizontal transfer (2-3). 8. Suffix trees, suffix arrays (1-2). Założenia Basics of algorithmics, programming skills (e.g. python, c/c++ or java)
Bibliography:	1. Inferring Phylogenies Joseph Felsenstein 2. Paul G. Higgs, Teresa K. Attwood, Bioinformatyka i ewolucja molekularna, 3. R. Durbin, S. Eddy, A. Krogh, G. Mitchson, Biological Sequence Analysis, .
Learning outcomes:	Has knowledge on advanced methods from comparative genomics (K_W04) can perform computations related to genome comparison and can interpret their results (K_U07)
Assessment methods and assessment criteria:	Exam 60% + compulsory lab project 35% + project presentation 5%.

This course is not currently offered.

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