Subject

Introduction to Bioinformatics

Course Title

Introduction to Bioinformatics
Introduction to bioinformatics

Code

F23L3W085

Study Programme

Bioinformatics

Organizer of the study programme (unit, institute, department or division)

Faculty of Computer Science and Engineering

Degree level (first, second, third cycle)

First Cycle

Academic year / semester

7 / Winter

Number of ECTS credits

Teacher

Monika Simjanoska Miseva, Nevena Atckovska

Prerequisites for enrolling in the course

Artificial Intelligence or Machine Learning or Introduction to Data Science

10.

Objectives of the course programme (competences)

For students to become familiar with the areas and problems covered by bioinformatics, to
be able to perform gene and protein sequence analysis, to use biological databases
data, to familiarize them with computational methods for solving problems in molecular
biology.

11.

Course content

Lectures:
1. Fundamentals of Molecular Biology
2. DNA Replication and the Central Dogma
3. Key Actors and Processes
4. Genetic files
5. Control of gene expression
6. Metaphors for Understanding Genetic Processes
7. Viruses
8. Evolution of the genome
9. Artificial chromosomes
10. Microbiome
11. Sequence Alignment
12. Motives
13. Visit to the Faculty of Science's Molecular Laboratory

Practical Classes:
1. Biological databases and centers
2. Formats of Representation and Application
3. Amino acids
4. Predicting secondary structure
5. Regulation of gene expression
6. DNA Microarrays and Gene Ontology
7. SARS-CoV-2
8. Gene recombination
9. Molecular docking
10. Analysis of microbiome data
11. Sequence Alignment Methods
12. Methods for Finding Motifs
13. Visit to the Faculty of Science's Molecular Laboratory

12.

Learning methods

lectures, projects, discussions, workshops

13.

Total available time

6 ECTS x 30 hours = 180 hours

14.

Distribution of available time

30 + 45 + 15 + 15 + 75 = 180 hours

15.

Forms of teaching activities

15.1.	Lectures - theoretical instruction	30 hours
15.2.	Exercises (laboratory, auditory), seminars, teamwork	45 hours

16.

Other forms of activities

16.1.	Project assignments	15 hours
16.2.	Independent assignments	15 hours
16.3.	Home study	75 hours

17.

Assessment method

17.1.	Tests	0 points
17.2.	Seminar paper / project (presentation: written and oral)	15 points
17.3.	Activities and learning	10 points
17.4.	Final exam	60 points

18.

Grading criteria (points / grade)

up to 50 points	5 (five) (F)
from 51 to 60 points	6 (six) (E)
from 61 to 70 points	7 (seven) (D)
from 71 to 80 points	8 (eight) (C)
from 81 to 90 points	9 (nine) (B)
from 91 to 100 points	10 (ten) (A)

19.

Requirement for obtaining a signature and taking the final exam

Completed activities 15, 16

20.

Language of instruction

Macedonian and English

21.

Method for monitoring the quality of teaching

22.

Literature

22.1.

Required literature

1. Neil C. Jones and Pavel A. Pevzner | An Introduction to Bioinformatics Algorithms | MIT Press | 2004
2. Harvey Lodish, Arnold Berk, Chris A. Kaiser, Monty Krieger, Anthony Bretscher, Hidde Ploegh, Angelika Amon, Matthew P. Scott | Molecular Cell Biology - 8th edition | W. H. Freeman | 2016
3. Andreas D. Baxevanis, Gary D. Bader, David S. Wishart | Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins 4th Edition | Wiley | 2020
4. Dev Bukhsh Singh, Rajesh Kumar Pathak | Bioinformatics: Methods and Applications | Elsevier Science | 2021
5. Ken Youens-Clark | Mastering Python for Bioinformatics: How to Write Flexible, Documented, Tested Python Code for Research Computing 1st Edition | O'REILLY | 2021
6. Miguel Rocha, Pedro G. Ferreira | Bioinformatics Algorithms: Design and Implementation in Python 1st Edition | Academic Press | 2018

22.2.

Additional literature

No. Author Title Publisher Year