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Introduction to computational biology

General data

Course ID: 1000-2N03BO
Erasmus code / ISCED: 11.303 The subject classification code consists of three to five digits, where the first three represent the classification of the discipline according to the Discipline code list applicable to the Socrates/Erasmus program, the fourth (usually 0) - possible further specification of discipline information, the fifth - the degree of subject determined based on the year of study for which the subject is intended. / (0612) Database and network design and administration The ISCED (International Standard Classification of Education) code has been designed by UNESCO.
Course title: Introduction to computational biology
Name in Polish: Wstęp do biologii obliczeniowej
Organizational unit: Faculty of Mathematics, Informatics, and Mechanics
Course groups: (in Polish) Przedmioty obieralne na studiach drugiego stopnia na kierunku bioinformatyka
Elective courses (facultative) for Computer Science
Elective courses for Computer Science and Machine Learning
Specific programme courses of 2nd stage Bioinformatics
ECTS credit allocation (and other scores): 6.00 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 courses

Prerequisites:

Algorithms and data structures 1000-213bASD
Probability theory 1000-213bRP

Short description:

The aim of this course is to introduce students with a computer science and mathematics background to the problems of modern computational biology. The topics are focused on analysis of protein and nucleic acid sequences. Fundamental mathematical models and computational methods used in the description of molecular sequences will be presented.

Full description:

1. Biological introduction: basic knowledge of molecular biology, structure of nucleic acids and proteins, transcription and translation.

2. Molecular sequence analysis: sequencing by hybridization, algorithms for global and local alignment of two sequences.

3. Mathematical models of molecular evolution: Jukes-Cantor and Kimura models for DNA sequences, PAM and BLOSUM substitution matrices for proteins, statistical significance of alignment scores.

4. Multiple sequence alignment: dynamic programming, greedy algorithms, efficient heuristics (CLUSTALW, T-Coffee, MUSCLE).

5. Hidden Markov Models and their applications to molecular sequences: Viterbi and Baum-Welch algorithms.

6. Searching sequence databases: BLAST algorithm.

7. Finding motifs in DNA sequences, functional enrichment analysis of gene sets.

8. Introduction to phylogenetics: reconstructing phylogenetic trees of single genes and reconciling them.

9. Introduction to genomic data analysis: mapping reads to reference genome, genome assembly, metagenomics.

The course will be given in Polish, if no non-Polish-speaking students register for it.

Bibliography:

1. Richard Durbin, Sean R. Eddy, Anders Krogh, Graeme Mitchison, Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids, Cambridge University Press, 1998.

2. Pavel A. Pevzner, Computational Molecular Biology: An Algorithmic Approach, MIT Press, 2000.

3. Warren J. Ewens, Gregory R. Grant, Statistical Methods in Bioinformatics: An Introduction, Springer 2001.

4. A. Malcolm Campbell, Laurie J. Heyer, Discovering Genomics, Proteomics, and Bioinformatics, CSHL Press, 2007.

Learning outcomes:

Knowledge:

1. Has a general knowledge of the problems of contemporary computational biology.

2. Has basic knowledge of mathematical models and computational methods used in the description of molecular sequences.

Skills:

1. Can implement fundamental bioinformatics analyses of molecular sequences.

2. Can use advanced bioinformatics tools to analyze experimental data.

Competences:

1. Knows the limitations of their own knowledge and understands the need for further education (K_K01).

2. Is able to manage their time and make commitments and meet deadlines (K_K05)

3. Is able to use interdisciplinary literature.

Assessment methods and assessment criteria:

Theory test, programming assignments, programming homework. Oral exam.

In the case of completing the course by a doctoral student, an additional element will be to read an original research article that is close to the current research front and discuss it with the lecturer.

Classes in period "Summer semester 2024/25" (past)

Time span: 2025-02-17 - 2025-06-08
Selected timetable range:
Go to timetable
Type of class:
Lab, 30 hours more information
Lecture, 30 hours more information
Coordinators: Aleksander Jankowski
Group instructors: Adam Cicherski, Aleksander Jankowski, Łukasz Kozłowski
Students list: (inaccessible to you)
Credit: Examination

Classes in period "Summer semester 2025/26" (future)

Time span: 2026-02-16 - 2026-06-07

Selected timetable range:
Go to timetable
Type of class:
Lab, 30 hours more information
Lecture, 30 hours more information
Coordinators: Aleksander Jankowski
Group instructors: Adam Cicherski, Aleksander Jankowski
Students list: (inaccessible to you)
Credit: Course - Examination
Lecture - Examination
Course descriptions are protected by copyright.
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