Selected topics in functional genomics

General data

Course ID:	1000-2M22TFG
Erasmus code / ISCED:	11.3 Kod klasyfikacyjny przedmiotu składa się z trzech do pięciu cyfr, przy czym trzy pierwsze oznaczają klasyfikację dziedziny wg. Listy kodów dziedzin obowiązującej w programie Socrates/Erasmus, czwarta (dotąd na ogół 0) – ewentualne uszczegółowienie informacji o dyscyplinie, piąta – stopień zaawansowania przedmiotu ustalony na podstawie roku studiów, dla którego przedmiot jest przeznaczony. / (0612) Database and network design and administration The ISCED (International Standard Classification of Education) code has been designed by UNESCO.
Course title:	Selected topics in functional genomics
Name in Polish:	Wybrane zagadnienia genomiki funkcjonalnej
Organizational unit:	Faculty of Mathematics, Informatics, and Mechanics
Course groups:	(in Polish) Przedmioty obieralne na studiach drugiego stopnia na kierunku bioinformatyka Elective courses for 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 course will cover bioinformatics methods that are crucial components in all interdisciplinary projects that seek to describe and understand complex molecular biology systems. We will focus on analyzing data from functional genomics including transcriptomics, proteomics, metabolomics and epigenomics.
Full description:	The methods covered in this course are relevant for several of the UN sustainability goals where modern molecular biology is part of the solution, including goals pertaining to food- and bioenergy-production. There will be lectures/group discussions and supervised computer exercises. The lectures will primarily introduce the students to the theory behind the bioinformatics methods while the labs will show the students how the methods can be used in practice. Topics of consecutive lectures: - Introduction to functional genomics - Transcriptomics - Differential expression - Clustering - Machine learning - Networks - Metabolomics and proteomics - Data integration
Bibliography:	ENCODE project publications: https://www.encodeproject.org/publications/
Learning outcomes:	KNOWLEDGE: On completion of this course, the students will have general knowledge of the different data types generated within functional genomics ("omics"-data: transcriptomics, proteomics, metabolomics and epigenomics) and will be able to explain the theory behind the most common bioinformatics methods for analyzing such data. These methods include finding differentially expressed genes and gene sets, machine learning, clustering and network analysis, and methods for integrating "omics" data and biological knowledge in e.g. ontologies. SKILLS: On completion of this course, the students will be able to analyze "omics" data using different methods and will also be able to understand and interpret the results produced by these methods. Given a data set and a biological question, the students should be able to asses which methods and tools to use in order to answer the question. GENERAL COMPETENCE: The students will be able to perform reproducible analysis of data generated within functional genomics and be equipped to modify relevant methods when new datatypes emerge in the future.
Assessment methods and assessment criteria:	To pass the course, you need to get the two reports from week 1 and week 2 approved.

This course is not currently offered.

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