Title:

Advanced Bioinformatics

Code:PBI
Ac.Year:2017/2018
Term:Winter
Curriculums:
ProgrammeBranchYearDuty
IT-MSC-2MBI2ndCompulsory
Language:Czech
Credits:4
Completion:examination (written&verbal)
Type of
instruction:
Hour/semLecturesSem. ExercisesLab. exercisesComp. exercisesOther
Hours:2000136
 ExaminationTestsExercisesLaboratoriesOther
Points:51002920
Guarantee:Lexa Matej, Ing., Ph.D., FI
Lecturer:Lexa Matej, Ing., Ph.D., FI
Instructor:Lexa Matej, Ing., Ph.D., FI
Puterová Janka, Ing., DIFS
Faculty:Faculty of Information Technology BUT
Department:Department of Computer Systems FIT BUT
Schedule:
DayLessonWeekRoomStartEndLect.Gr.St.G.EndG.
Wedexam - 1. oprava2018-01-17G20215:0016:501MIT
Wedexam - 1. oprava2018-01-17G20215:0016:502MIT
ThulecturelecturesA11214:0015:502MIT10 MBI10 MBI
Thuexam - řádná2018-01-04A11214:0015:501MIT
Thuexam - řádná2018-01-04A11214:0015:502MIT
Thuexam - 2. oprava2018-02-01E10516:0017:501MIT
Thuexam - 2. oprava2018-02-01E10516:0017:502MIT
 
Learning objectives:
  To build on the introductory bioinformatics course. Introduce the students to selected, fast-evolving, or otherwise noteworthy areas of bioinformatics. To allow space for creative activities resulting in the creation of a computational tool based on studied principles.
Description:
  During the lectures, the students will get acquainted with areas integrating different bioinformatic data-types. They will study possibilities of data integration to solve specific problems or create specific computational tools. Textbook material will be supplemented by recently published scientific papers. Students will work on individual computational modules in the exercises/projects leading to the creation of an integrated whole-class tool suitable for general bioinformatic analysis (functional annotation, structural prediction, molecule visualization).
Subject specific learning outcomes and competences:
  Knowledge of less-common algorithm and analysis methods, better ability to design and implement algorithms for bioinformatics.
Generic learning outcomes and competences:
  Deeper understanding the role of computers in the analysis and presentation of biological data.
Syllabus of lectures:
 
  1. Introduction
  2. Primary and derived bioinformatic data
  3. Genomes and genome analysis methods
  4. Uniprot and sequence analysis methods
  5. Statistical, information-theory and linguistic aspect of data
  6. Coding algorithms for biological sequence analysis
  7. PDB and structural data analysis
  8. Gene Ontology and functional data analysis
  9. Integration of data from multiple sources for genomics and proteomics
  10. Tools and libraries for software development (Biopython)
  11. Visualization tools (PyMol)
  12. Bioinformatics and nanotechnology: DNA computing, sequencing by hybridization
  13. Recent trends
Syllabus of computer exercises:
 
  1. Biological sequence analysis
  2. Genome Browser, Biomart
  3. Biopython a PyMol
  4. R/Bioconductor
  5. Integration of bioinformatic data
Syllabus - others, projects and individual work of students:
 Design and implementation of an integrated computational tool for bioinformatics and its presentation on a mini-conference.
Fundamental literature:
 
Study literature:
 
  • Jones N.C., Pevzner P.: An introduction to algorithms in bioinformatics. MIT Press, 2004, ISBN 978-0262101066
Progress assessment:
  Project, computer labs assignments.
Exam prerequisites:
  None.