Bioinformatics 1

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.

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
  7. Design index structures based on suffix trees and suffix arrays

Forms of Teaching


Lectures in the classroom

Independent assignments

Team work on the implementation of a method.

Grading Method

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

Week by Week Schedule

  1. Protein, RNA and DNA, Bioinformatics databases, Data formats
  2. Biological sequences and structures, Dynamic programming algorithms, Project
  3. Dynamic programming algorithms, Project
  4. Suffix trees, Project
  5. Suffix trees, Project
  6. Suffix arrays, Project
  7. Suffix arrays, FM index, Project
  8. Midterm exam
  9. Multiple sequence alignment, Database search, Basic Local Alignment Search Tool (BLAST) algorithm
  10. Alignment, Substitution model, Project
  11. Tree building, Tree evaluation, Project
  12. Sequencing methods, Read mapping, Project
  13. De novo assembly, Overlap-Layout-Consensus, Project
  14. String graph, De Bruijn graph, Project
  15. Final exam

Study Programmes

University undergraduate
Computing (study)
Elective Courses (6. semester)
Electrical Engineering and Information Technology (study)
Elective Courses (6. semester)
University graduate
Audio Technologies and Electroacoustics (profile)
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Communication and Space Technologies (profile)
Free Elective Courses (2. semester)
Computational Modelling in Engineering (profile)
Free Elective Courses (2. semester)
Computer Engineering (profile)
Elective Course of the profile (2. semester) Specialization Course (2. semester)
Control Systems and Robotics (profile)
Elective Courses of the Profile (2. semester)
Data Science (profile)
Elective Coursesof the Profile (2. semester)
Electrical Power Engineering (profile)
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Electric Machines, Drives and Automation (profile)
Elective Courses of the Profile (2. semester)
Electronic and Computer Engineering (profile)
Elective Courses of the Profile (2. semester)
Electronics (profile)
Elective Courses of the Profile (2. semester)
Information and Communication Engineering (profile)
Elective Courses of the Profile (2. semester)
Information Processing (profile)
Specialization Course (2. semester)
Network Science (profile)
Free Elective Courses (2. semester)
Software Engineering and Information Systems (profile)
Elective Course of the profile (2. semester) Specialization Course (2. semester)


(.), Mile Sikic, Mirjana Domazet-Loso, Skripta iz bioinformatike,
(.), N.C. Jones, P. J. Pevzner, An Introduction to Bioinformatics Algorithms,
(.), D. Gusfield, Algorithms on Strings, Trees and Sequences: Computer Science and Computational Biology,

Associate Lecturers

For students


ID 183492
  Summer semester
L1 English Level
L1 e-Learning
30 Lectures
5 Laboratory exercises

Grading System

90 Excellent
75 Very Good
60 Good
50 Acceptable