Bioinformatics

Course Description

In the last two decades, bioinformatics has expanded rapidly as the discipline that narrowly connects biology with computer science. The accelerating growth of biological data collection has inspired development of new computational methods for storage, retrieval, analysis and visualization of such data. This course will focus on the computational aspects of biological sequence analysis. Lectures will cover algorithms and tools developed for pairwise and multiple sequence alignment, genome and transcriptome assembly, gene finding and phylogenetic tree reconstruction.

General Competencies

By the end of the course students will be able to describe representation of biological sequences, query and retrieve data from biological databases, apply and classify algorithms and tools for nucleotide and protein sequence alignment, genome and transcriptome assembly, and gene finding. Students will also learn how to apply methods for estimating molecular sequence evolution and how to build a phylogenetic tree.

Learning Outcomes

  1. Explain some of the issues and challenges in contemporary Bioinformatics
  2. Evaluate bioinformatics algortihms
  3. Design algorithms solving sequence assembly problems
  4. compare and evalute methods for sequence alignment
  5. Design algorithms for building phylogenetics trees
  6. Analyze data from biological databases

Forms of Teaching

Lectures

Lectures will involve discussion and practical work.

Exams

The mid-term examination will be held in Week 7, and the final exam in Week 15.

Other Forms of Group and Self Study

Project or seminar work in groups up to 6 students

Grading Method

Continuous Assessment Exam
Type Threshold Percent of Grade Threshold Percent of Grade
Seminar/Project 0 % 40 % 0 % 40 %
Mid Term Exam: Written 0 % 25 % 0 %
Final Exam: Written 0 % 35 %
Exam: Written 0 % 60 %

Week by Week Schedule

  1. What is bioinformatics? Introduction to molecular biology: DNA, RNA, genome, protein sequences. Biological databases. Data formats of biological sequences.
  2. Introduction to genome sequencing. Human genome and other genome projects. High through-put sequencing and genome and transcriptome assembly. Assembly evaluation.
  3. Sequence homology and sequence similarity. Sequence alignment: local, global. Dynamic programming algorithms for alignment.
  4. Indexed based alignment. BLAST and its variants.
  5. Suffix and prefix tries. Suffix tree. Suffix array.
  6. High through-put sequencing hashing methods. Pair-wise and multiple sequence alignment.
  7. The Burrows-Wheeler transform. Self-indexes. Hash-based methods vs. BWT-based methods.
  8. Miterm exam
  9. Phylogeny. Evolutionary models. Phylogenetic tree construction.
  10. Gene prediction.
  11. Data storage structures. Data compression for high through-put sequencing.
  12. Sequence assembly. Mapping and de novo assembling. Mapping algorithms. Repeats and masking.
  13. De novo assembly. Hamiltonian and Eulerian paths in graph.
  14. Overlap-layout-consensus approach. De Bruijn graph for assembly. Complexities in graphs.
  15. Final exam

Study Programmes

University graduate
Computer Engineering (profile)
Specialization Course (1. semester) (3. semester)
Computer Science (profile)
Recommended elective courses (3. semester)
Control Engineering and Automation (profile)
Recommended elective courses (3. semester)
Electronic and Computer Engineering (profile)
Recommended elective courses (3. semester)
Information Processing (profile)
Specialization Course (1. semester) (3. semester)
Software Engineering and Information Systems (profile)
Specialization Course (1. semester) (3. semester)
Telecommunication and Informatics (profile)
Recommended elective courses (3. semester)

Literature

D. Gusfield (1997.), Algorithms on Strings, Trees and Sequences: Computer Science and Computational Biology, Cambridge University Press
N.C. Jones, P. J. Pevzner (2004.), An Introduction to Bioinformatics Algorithms, MIT Press
R. C. Deonier, S. Tavaré, M. S. Waterman (2010.), Computational Genome Analysis: An Introduction, Springer
M. Zvelebil, J. O. Baum (2007.), Understanding Bioinformatics, Garland Science
J. Pevsner (2009.), Bioinformatics and Functional Genomics, 2nd Edition, Wiley-Blackwell

Grading System

ID 103777
  Winter semester
4 ECTS
L1 English Level
L1 e-Learning
30 Lecturers
0 Exercises
0 Laboratory exercises

General

90 Excellent
75 Very Good
60 Good
50 Acceptable