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13M111GI - Computational Genomics

Course specification
Course title Computational Genomics
Acronym 13M111GI
Study programme Electrical Engineering and Computing
Module Applied Mathematics, Audio and Video Communications, Audio and Video Technologies, Biomedical and Environmental Engineering, Biomedical and Nuclear Engineering, Computer Engineering and Informatics, Electronics, Electronics and Digital Systems, Energy Efficiency, Information and Communication Technologies, Microwave Engineering, Nanoelectronics and Photonics, Power Systems - Networks and Systems, Power Systems - Renewable Energy Sources, Power Systems - Substations and Power Equipment, Signals and Systems, Software Engineering, System Engineering and Radio Communications
Type of study master academic studies
Lecturer (for classes)
Lecturer/Associate (for practice)
Lecturer/Associate (for OTC)
ESPB 6.0 Status elective
Condition
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 Definitions of bioinformatics and genomics. Fundamentals of molecular biology and genome sequencing technologies. Techniques for matching text parts: Boyer-Moore, suffix tree. Burrows-Wheeler transformation and FM index. Techniques for approximate matching of text parts. RNA sequencing: single cell and spatial transcriptomics analyses. Data preprocessing and normalization. Bioinformatics tools.
Contents of exercises Same as for the theoretical lessons with an emphasis on data processing using tools and libraries based on Python programming language.
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)
  5. Heumos, L., Schaar, A.C., Lance, C. et al. Best practices for single-cell analysis across modalities. Nat Rev Genet (2023) (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 40
Practical lessons Oral examination
Projects 60
Colloquia
Seminars