Title:

Bioinformatics

Code:BIF
Ac.Year:2017/2018
Term:Summer
Curriculums:
ProgrammeFieldYearDuty
IT-MSC-2MBI1stCompulsory
IT-MSC-2MBS-Elective
IT-MSC-2MGM-Elective
IT-MSC-2MIN-Elective
IT-MSC-2MIS-Elective
IT-MSC-2MMI-Elective
IT-MSC-2MMM-Elective
IT-MSC-2MPV-Elective
IT-MSC-2MSK-Elective
Language of Instruction:Czech
Credits:5
Completion:examination (written)
Type of
instruction:
Hour/semLecturesSeminar
Exercises		Laboratory
Exercises		Computer
Exercises		Other
Hours:26001214
 ExamsTestsExercisesLaboratoriesOther
Points:581601214
Guarantor:Martínek Tomáš, Ing., Ph.D. (DCSY)
Lecturer:Burgetová Ivana, Ing., Ph.D. (DIFS)
Martínek Tomáš, Ing., Ph.D. (DCSY)
Instructor:Hon Jiří, Ing. (DIFS)
Musil Miloš, Ing. (DIFS)
Puterová Janka, Ing. (DIFS)
Smatana Stanislav, Ing. (DCSY)
Faculty:Faculty of Information Technology BUT
Department:Department of Computer Systems FIT BUT
 
Learning objectives:
  To understand the principles of molecular biology. To perceive the basic used algorithms and to well informed about relevant biological databases. To be able to design new effective methods for biological data analysis.
Description:
  This course introduces students to basic principles of molecular biology, present algorithms pro biological data analysis, describes their time complexity and shows direction how to design the new methods very effectively. Particularly, the following algorithms will be discussed: methods for sequence alignment, evolutionary models, construction of phylogenetic trees, algorithms for gene identification using machine learning and approaches for prediction of 2D and 3D protein structure. Lectures will be supplement with practical examples using available biological databases.
Subject specific learning outcomes and competences:
  Students will be able to take advantages of large biological database and design new efficient algorithms for their analysis.
Generic learning outcomes and competences:
  Understanding the relations between computers (computing) and selected molecular processes.
Syllabus of lectures:
 
  1. Introduction to bioinformatics
  2. Basis of molecular biology
  3. Tools of molecular biology
  4. Biological databases
  5. Sequence alignment, dynamic programing, BLAST, FASTA
  6. Evolutionary models
  7. Construction of phylogenetic trees
  8. DNA assembling
  9. Genomics and gene searching
  10. Proteins and their prediction
  11. Computation of RNA secondary structure
  12. Proteomics, regulatory networks
  13. Polymorphism of genes
Syllabus of computer exercises:
 
  1. Biological databases
  2. Analysis of genome sequences
  3. Sequence alignment
  4. Phylogenetic trees
  5. Gene prediction
  6. Protein structure analysis
Syllabus - others, projects and individual work of students:
 A project will be assigned to each student. Implementation, presentation and documentation of the project will be evaluated.
Fundamental literature:
 
  • Dan K. Krane, Michael L. Raymer: Fundamental Concepts of Bioinformatics, ISBN: 0-8053-4633-3, Benjamin Cummings 2003.
  • Neil C. Jones, Pavel A. Pevzner: An Introduction to Bioinformatics Algorithms, ISBN: 0262101068, MIT Press, 2004.
  • Andreas D. Baxevanis, B. F. Francis Ouellette: Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins, ISBN: 0-471-47878-4, Wiley-Interscience, 2005.
Study literature:
 
  • Jacques Cohen: Bioinformatics - An introduction for Computer Scientists, ACM Computing Surveys, 2004, Vol. 36, No. 2, p. 122-158.
  • Jean-Michel Claverie, Cedric Notredame: Bioinformatics for Dummies, ISBN: 0-7645-1696-5, Wiley Publishing, Inc., 2003.
  • Yi-Ping Phoebe Chen: Bioinformatics Technologies, ISBN: 3540208739, Springer, 2005.
  • Alberts, Bray, Johnson, Lewis, Raff, Roberts, Walter: Základy buněčné biologie, ISBN: 80-902906-0-4, Espero Publishing, 1998.
Controlled instruction:
  Presence in any form of instruction is not compulsory. An absence (and hence loss of points) can be compensated in the following ways: 
  1. presence in another laboratory group dealing with the same task. 
  2. showing a summary of results to the tutor at the next lab. 
  3. sending a short report (summarizing the results of the missed lab and answering the questions from the assignment) to the tutor, in 14 days after the missed lab.
Progress assessment:
  Mid-term exam, project, computer lab assignments.
Exam prerequisites:
  None.
 

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