Advanced Bioinformatics

• 20 hrs lectures
• 13 hrs pc labs
• 6 hrs projects

• 51 pts final exam
• 29 pts labs
• 20 pts projects

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.

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.

Real word bioinformatics problems often require advanced knowledge. Even though each problem is usually specific, some of the tasks are common, for instance, advanced sequence alignment methods based on suffix trees, next-generation sequencing data processing, differential expression and enrichment analysis, etc. The goal of this course is to familiarize a student with these techniques and prepare him/her for a future profession in the area of bioinformatics.

• Jones N.C., Pevzner P.: An introduction to algorithms in bioinformatics. MIT Press, 2004, ISBN 978-0262101066
• Zvelebil M., Baum J.: Understanding bioinformatics. Garland Science, London, 2007 ISBN 978-0815340249.
• Xinkun Wang, Next-Generation Sequencing Data Analysis, ISBN: 978-1482217889, CRC Press, 2016.
• UniProt URL: http://www.expasy.uniprot.org/
• Gene Ontology URL: http://www.geneontology.org
• Protein Data Bank URL: http://www.pdb.org/

• Zvelebil M., Baum J.: Understanding bioinformatics. Garland Science, London, 2007 ISBN 978-0815340249 UniProt URL: 

• UniProt URL: http://www.expasy.uniprot.org/

• Protein Data Bank URL: http://www.pdb.org/

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

1. Biological sequence analysis
2. Genome Browser, Biomart
3. Biopython a PyMol
4. R/Bioconductor
5. Integration of bioinformatic data

Design and implementation of an integrated computational tool for bioinformatics and its presentation on a mini-conference.

Project, computer labs assignments.

None.