Course details
Advanced Bioinformatics
PBI Acad. year 2012/2013 Winter semester 4 credits
During the lectures, the students will get aquainted 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).
Guarantor
Language of instruction
Completion
Time span
- 20 hrs lectures
- 13 hrs pc labs
- 6 hrs projects
Department
Subject specific learning outcomes and competences
Knowledge of less-common algorithm and analysis methods, better ability to design and implement algorithms for bioinformatics.
Deeper understanding the role of computers in the analysis and presentation of biological data.
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.
Prerequisite knowledge and skills
There are no prerequisites
Study literature
- Jones N.C., Pevzner P.: An introduction to algorithms in bioinformatics. MIT Press, 2004, ISBN 978-0262101066
Syllabus of lectures
- Introduction
- Primary and derived bioinformatic data
- Genomes and genome analysis methods
- Uniprot and sequence analysis methods
- Statistical, information-theory and linguistic aspect of data
- Coding algorithms for biological sequence analysis
- PDB and structural data analysis
- Gene Ontology and functional data analysis
- Integration of data from multiple sources for genomics and proteomics
- Tools and libraries for software development (Biopython)
- Visualization tools (PyMol)
- Bioinformatics and nanotechnology: DNA computing, sequencing by hybridization
- Recent trends
Syllabus of computer exercises
- Biological sequence analysis
- Genome Browser, Biomart
- Biopython a PyMol
- R/Bioconductor
- Integration of bioinformatic data
Progress assessment
Study evaluation is based on marks obtained for specified items. Minimimum number of marks to pass is 50.
None.
Controlled instruction
Project, computer labs assignments.
Course inclusion in study plans