Subject » BMEVISZA077
Algorithms in Bioinformatics
Bioinformatikai algoritmusok
A tantárgyleírás hatályossága
Hatályosság kezdete:
2026. March 21.
Hatályosság vége:
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| Subject name (Hungarian, English) |
Bioinformatikai algoritmusok
Algorithms in Bioinformatics
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| Subject code | BMEVISZA077 | ||||||||||||
| Subject type | — | ||||||||||||
| Training Level | — | ||||||||||||
| Course types and hours (weekly/semester) |
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| Assessment type | félévközi érdemjegy | ||||||||||||
| Credits | 2 | ||||||||||||
| Subject coordinator |
DR. Csima Judit
position: egyetemi docens
contact:
csima.judit@vik.bme.hu
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| Responsible department |
Számítástudományi és Információelméleti Tanszék
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| Faculty | Villamosmérnöki és Informatikai Kar | ||||||||||||
| Subject website | www.cs.bme.hu/.... | ||||||||||||
| Primary curriculum type | — | ||||||||||||
| Direct prerequisites – Strong prerequisite | none | ||||||||||||
| Direct prerequisites – Weak prerequisite | none | ||||||||||||
| Direct prerequisites – Parallel prerequisite | none | ||||||||||||
| Direct prerequisites – Milestone prerequisite | none | ||||||||||||
| Direct prerequisites – Exclusion | none |
Objectives
Programme
- Introduction
The concept of the graph of desire and reality
Safe cycle-increasing reversals
- Hurdles, superhurdles, fortresses
- Sorting by block interchanges
- 2-approximation and 1.5 approximation for sorting by transpositions
- Multiple Genome Rearrangement problems
- Student presentations based on selected scientific papers
- Introduction to dynamic programming: longest common subsequence, pairwise sequence alignment, examples
- Sophisticated sequence alignment algorithms: aligning with affine gap penalty, local alignment, Hirschberg’s algorithm for aligning sequences in linear space
- Dynamic programming on trees: The small parsimony problem, Felsenstein’s algorithms, the Noah’s ark problem
- RNA structure prediction: Nussinov algorithm. Introduction of the Zuker-Tinoco energy model and the Zuker-Sankoff algorithm
- RNA secondary structures as context-free grammars. Parsing algorithms for context-free grammars.
- Student presentations based on selected scientific papers
The objective of this course is to give an introduction to discrete mathematics and algorithms related to bioinformatics. The most important discrete structures: trees, sequences, graphs describing the biological entries will be introduced. The students will learn dynamic programming algorithms that are used to sequence comparison, likelihood calculations, RNA structure prediction (finding the most stable RNA structure), and other optimization problems. The course also covers genome rearrangement algorithms, which are mathematically beautiful and also very important in the age of genomics when millions of genomes are going to be sequenced. The student will learn skills necessary to read and understand scientific papers in the introduced topics.
Learning outcomes
Ez a tantárgy a KKK rendeletben meghatározott, következő kompetenciák fejlesztését szolgálja:
Knowledge
No learning outcomes recorded.
Skills
No learning outcomes recorded.
Attitudes
No learning outcomes recorded.
Autonomy and responsibility
No learning outcomes recorded.
Oktatási módszertan
Lectures, recitations, project-based computer assignments
Tanulástámogató anyagok
Online források
Pevzner, Pavel: Computational Molecular Biology: An Algorithmic Approach (Computational Molecular Biology), http://www.amazon.com/Computational-Molecular-Biology-Algorithmic-Approach/dp/0262161974/ref=cm_cr_pr_product_top ; ; Miklós, István: Algorithms of bioinformatics. http://ramet.elte.hu/~miklosi/Bioinf-23May2007.pdf
Recommended preliminary knowledge for completing the subject
Knowledge type competencies
(azon előzetes ismeretek összessége, amelyek megléte nem kötelező, de a tantárgy eredményes teljesítését nagyban elősegíti)
nincs
Skill type competencies
(azon előzetes képességek és készségek összessége, amelyek megléte nem kötelező, de a tantárgy eredményes teljesítését nagyban elősegíti)
nincs
Recommended (non-compulsory) preliminary competencies
(azon ajánlott (nem kötelező) előzetesen megszerzendő kompetenciák összessége, amelyek jelentősen hozzájárulnak a tantárgy eredményes teljesítéséhez)
nincs
General rules
Requirements:
The students are required to present read scientific papers in live PowerPoint presentations and/or chalk-talk, to be challenged by the instructor in front of the class.
The presentations will illustrate students’ ability to explain an algorithm/idea/other scientific work to the audience.
The student will also get exercises for homework. They have to handle in the solutions, and have to present the solutions at blackboard.
Critical element of the grading will be the homework, presentations and two tests, one in the middle of the course, and one at the end of the course
Grading will be based on the following criteria:
- Homework 25 points
- First student presentation 15 points
- Second student presentation 15 points
- Mid-term test 15 points
- Final test 30 points
Assessment methods
In-term assessments
No detailed assessments provided.
Weight of in-term assessments
No weights provided.
Exam-period assessments
No detailed assessments provided.
Weight of exam elements
No weights provided.
Grade calculation
No grade thresholds provided.
Attendance requirements
No attendance requirements provided.
Rules for retake and resubmission
Not provided.
Short description
Not provided.
Detailed description
Not provided.
Recommended courses
Not provided.
Workload to complete the subject
No workload breakdown provided.
Validity of subject requirements
Requirements valid from:
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Requirements valid until:
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Curriculum placement
No curriculum placements recorded for this subject version.