SSB-20306 Bioinformation Technology

Course

Credits 6.00

Language of instruction:

English

Assumed knowledge on:

Cell Biology I, Microbiology & Biochemistry or Gentechnology.

Continuation courses:

Genomics and Applied Bioinformatics.

Contents:

The availability of large amounts of high throughput omics data gives us new insights and a better understanding of the molecular mechanisms of life. This course revolves around two commonly asked questions: "i) how can we transform this data into useful information and ii) what can we learn from this kind of information?" This course will introduce the basic concepts and tools essential for this transformation process. Background information on frequently used computational tools for DNA, RNA, and protein sequence analysis is mixed with practical, hands-on elements consisting of exercises demonstrating important basic bioinformatics concepts. The course is divided in a number of modules:
1. An introduction in primary DNA sequence analysis: Topics include gene architecture, reading frames, intervening sequences and translation of a nucleotide sequence to protein. In this module it is explained what kind of information we can and cannot extract from a primary DNA sequence;
2. An introduction in proteomics: a computational primer on high-throughput tandem mass spectrometry of peptides and proteins, demonstrating the use of LC-MS-MS data in identifying proteins of interest. Topics include the role of a decoy database and calculation of false discovery rates;
3. Homology and similarity: Pairwise sequence alignment and basic sequence database search methods. Topics include the PAM and Blosum Matrices, the BLAST algorithm for comparing primary biological sequence information, matrix derived raw-scores, bit-scores and E-values;
4. An introduction to the NCBI an EMBL public sequence databases, searching PubMed, publication and sequence retrieval;
5. An introduction in transcriptome analysis: Real data will be used to demonstrate how RNA-seq data can be applied in solving biological questions;
6. Prediction of protein cellular localization. Introduction of standard tools for extraction of topological signals from primary protein sequences;
7. Multiple sequence alignments as a tool to elucidate the possible function(s) of novel proteins. Topics include protein domains and work-flows for protein domain analysis;
8. Annotation of DNA and protein sequences using ontologies;
9. Protein structures and 3D-protein models: three-dimensional protein structure alignments and usage of structural databases, like CATH, SCOP, FSSP and MMDB.

Learning outcomes:

After successful completion of this course students are expected to be able to:
- explain the concepts behind widely used computational tools for dna assembly;
- sequence alignment, translation into protein sequences, identification of protein motifs topological signals and protein structure prediction;
- recognize and distinguish, advantages and shortcomings of standardly used databases that store text, nucleotide and protein sequences;
- recognize and distinguish advantages and shortcomings of standardly used computational tools for dna and protein sequence analysis, for topological signal prediction and 3d-protein prediction;
- apply these methods to (simple) real life biological problems;
- assess and judge critically omics derived information with respect to the biological questions involved.

Activities:

- hands on course introductory lectures;
- training and study of relevant literature.

Examination:

The examination is based on:
- two day assignment (50%);
- written examination with open questions (50%).
Both the practical assignment and the exam need a 5.5 to pass.
The results of the 2-day assignment will be presented in the form of a written report combined with an oral presentation.

Literature:

Book: Michael Agostino. (2012). Practical Bioinformatics. Taylor & Francis Inc. 394p. ISBN: 9780815344568. Course lectures, additional reading and hands-on excercises are made available in an electronic form (Blackboard) During the course we will for the larger data sets additionally use dedicated intraweb servers.