460-6025/02 – Algorithms for Bioinformatics (ABI)

Gurantor departmentDepartment of Computer ScienceCredits10
Subject guarantorprof. RNDr. Václav Snášel, CSc.Subject version guarantorprof. RNDr. Václav Snášel, CSc.
Study levelpostgraduateRequirementChoice-compulsory type B
YearSemesterwinter + summer
Study languageEnglish
Year of introduction2019/2020Year of cancellation
Intended for the facultiesFEIIntended for study typesDoctoral
Instruction secured by
LoginNameTuitorTeacher giving lectures
SNA57 prof. RNDr. Václav Snášel, CSc.
Extent of instruction for forms of study
Form of studyWay of compl.Extent
Full-time Examination 28+0
Part-time Examination 28+0

Subject aims expressed by acquired skills and competences

The aim of the course is to provide students deeper overview of design and implementation of algorithms and data structures. In addition, this knowledge and skills will be further enhanced in a direction that is in line with the specific focus of its Ph.D. studies and dissertation work.

Teaching methods

Seminars
Individual consultations
Project work
Other activities

Summary

The course is focused on biological applications, computational problems, and their various advanced algorithmic solutions. Students will be introduced, in-depth, to the algorithmic techniques applied in bioinformatics. Each topic provides the biological motivation and precisely defines the corresponding computational problems; different methods and the corresponding algorithms are also presented including detailed examples to illustrate each algorithm.

Compulsory literature:

• Sung, W. K. (2009). Algorithms in bioinformatics: A practical introduction. CRC Press. • Compeau, Phillip, and Pavel Pevzner. Bioinformatics algorithms: an active learning approach. Vol. 1. La Jolla: Active Learning Publishers, 2015. • Zvelebil, Marketa J., and Jeremy O. Baum. Understanding bioinformatics. Garland Science, 2007.

Recommended literature:

• M. Dorigo, T. Stützle, Ant Colony Optimization, MIT Press, Cambridge, MA, 2004. • A. Engelbrecht, Computational Intelligence: An Introduction, 2nd Edition, Wiley, New York, NY, USA, 2007.

Way of continuous check of knowledge in the course of semester

Continuous monitoring of study activities and assigned tasks during regular consultations. If some publication activity will be a part of the student's tasks, the relevant article would be presented in the course.

E-learning

Other requirements

The student prepares and presents the work on a given topic.

Prerequisities

Subject has no prerequisities.

Co-requisities

Subject has no co-requisities.

Subject syllabus:

• Sequence similarity • Suffix trees • Genome alignment and multiple sequence alignment • Database search • Phylogeny Reconstruction and Comparison • Genome Rearrangement • Motif Finding • RNA secondary structure prediction • Peptide Sequencing • Population genetics • BAM algorithm for resequencing, BLAST and BLAT algorithms description • Assembly algoritms, paired-end scaffolding algorithms

Conditions for subject completion

Full-time form (validity from: 2019/2020 Winter semester)
Task nameType of taskMax. number of points
(act. for subtasks)
Min. number of points
Examination Examination  
Mandatory attendence parzicipation:

Show history

Occurrence in study plans

Academic yearProgrammeField of studySpec.ZaměřeníFormStudy language Tut. centreYearWSType of duty
2021/2022 (P0588D140004) Bioinformatics and Computational Biology P English Ostrava Choice-compulsory type B study plan
2021/2022 (P0588D140004) Bioinformatics and Computational Biology K English Ostrava Choice-compulsory type B study plan
2020/2021 (P0588D140004) Bioinformatics and Computational Biology P English Ostrava Choice-compulsory type B study plan
2020/2021 (P0588D140004) Bioinformatics and Computational Biology K English Ostrava Choice-compulsory type B study plan

Occurrence in special blocks

Block nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner