Algorithmic and statistical aspects of DNA sequencing

General data

Course ID:	1000-2M12DNA
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:	Algorithmic and statistical aspects of DNA sequencing
Name in Polish:	Algorytmiczne i statystyczne aspekty sekwencjonowania DNA
Organizational unit:	Faculty of Mathematics, Informatics, and Mechanics
Course groups:	Elective courses for Computer Science
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 lecture is devoted to DNA sequencing technology and its output data analysis. It will cover both algorithmic problems (e.g. applications of de Bruijn graphs and Burrows-Wheeler transform) and statistical ones (differential analysis, hypothesis testing).
Full description:	1. Next generation sequencing: - various sequencing technologies and their output, - applications: genome resequencing, targeted sequencing (transcriptome, exons, immunoprecipitation), 'de novo' sequencing, 'metagenome' sequencing, - experiment design and quality control. 2. Algorithmic problems and their typical solutions: - short read assembly (e.g. de Bruijn graphs), - read mapping (e.g. Burrows-Wheeler transform, Ferragina-Manzini index), - genome variation discovery (copy number variation, single nucleotide polymorphism and structural variation discovery).. 3. Differential analysis problems: - differential gene expression analysis with RNA-seq, - microRNA and long-non-coding-RNA identification in sequencing data.
Bibliography:	"Large-scale genome sequence processing", M. Kasahara, S. Morishita, Imperial College Press, 2006 "Bioinformatics for High Throughput Sequencing", N. Rodríguez-Ezpeleta, M. Hackenberg, A.M. Aransay, Springer, 2012
Learning outcomes:	Knowledge: - typical modern genome-scale technologies - basic distributions describing genome-scale measures and algorithms to their analysis Abilities: - application of proper technology to a given biological problem - design of genome-scale experiments and its results' analyzis - application of proper statistical model to experiment results - implementing selected algorithms to experiment result analysis Competences: - awareness of own limitations and the need for further education (K_K01) - awareness of the need for systematic work on software projects (K_K02)
Assessment methods and assessment criteria:	Final assesment is based on lab projects and (optionally) oral exam. PhD students should carry out projects individually.

This course is not currently offered.

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