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 sequence data gives us new insights into the mechanisms of life. This course revolves around the commonly asked question "what can we learn from this sequence data?" The course focuses on the basic computational sequence analysis methods required to answer biological questions. Background information on the computational tools used for DNA, RNA, and protein sequence analysis is mixed with practical, hands-on elements with many exercises.
The course is divided in 8 modules:
1. an introduction in DNA sequence analysis. In this module it is explained what we can and what we cannot do with 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;
3. sequence alignments and (sequence) database queries. An introduction to text-mining tools, matrices and the blast sequence comparison methods. Topics include sequence search strategies, the dynamic algorithms involved, matrix derived raw-scores, bit-scores and e-values;
4. an introduction in transcriptome analysis. Topics are RNA-seq methods and micro arrays, their usage and limitations. Real array data will be used to demonstrate how these data can be applied in solving biological questions;
5. extraction of topological signals from primary protein sequences;
6. multiple sequence alignments as a tool to clarify the function(s) of an unknown protein. Usage of protein family and domain databases. Work flows for protein domain analysis;
7. annotation of DNA and protein sequences and ontologies;
8. protein models and structures. Comparison of three-dimensional protein structures and usage of structural databases, like CATH, SCOP, FSSP and MMDB. Homology based three-dimensional modelling of proteins.

Learning outcomes:

After successful completion of this course students are expected to be able to:
- remember advantages and shortcomings of databases that store text, nucleotide and protein sequences;
- understand the concepts behind widely used computational tools for DNA assembly, sequence alignments, translation into protein sequences, identification of protein motifs and protein structure prediction;
- understand advantages and shortcomings of DNA and protein sequence analysis software using FastA or BLAST algorithms and in use for multiple sequence alignment, SRS, topological signal prediction, protein 3D prediction;
- be able to select the most appropriate computational biology tools for a given analysis;
- be able to evaluate genomic 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:
- twee day assignment (50%);
- written examination with open questions (50%).
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 electronic form Blackboard and dedicated intraweb server during the course.