Bioinformatics 2
Lecturer
Guido Sanguinetti, Rm 1.44 Informatics Forum. <gsanguin@inf.ed.ac.uk>
Course Tutor
H.M. Shahzad Asif shahzad.asif@ed.ac.uk
Last Update: 11 Jan 2011
NEWS
The Edinburgh SysBio Club Wiki contains information on systems biology events in Edinburgh.
Quick Links:
This year's lecture notes will appear here when they become available (typically the day of the lecture).
The course's formal descriptor in DRPS is here
Old [2011] Lecture notes, for reference only. Changes are likely to be minor in general but substantial in lectures 1-2
Aims and Objectives
Bioinformatics is at the interface between two of the most influential
scientific fields. An appreciation of computational and biological sciences,
in particular the terminology employed in both fields, is essential for
those working at such an interface. In this course, we aim to cover the
following:
1. The concepts of computer science that relate to problems in biological
sciences.
2. Commercial and academic perspectives on bioinformatics.
3. The impact of bioinformatics on the methodologies used in biological
science.
4. The influence biological science has on computing science.
Context
An undergraduate degree in computing or mathematical
sciences will be useful, particularly some exposure to machine learning.
However, the course would also be suitable for an individual
from biological
sciences with some programming experience and some background in basic
statistics/ machine learning.
Assessment
This course will be assessed on assigned coursework and an exam (30/70 split).
The coursework for this year is now out here
References
Recommended textbooks:
Jones N.C. and Pevzner P. (2004) An Introduction to Bioinformatics Algorithms, MIT Press
Durbin R., Eddy S., Krogh A. and Mitchinson G. (1998) Biological sequence
analysis: Probabilistic models of proteins and nucleic acids. Cambridge
University Press. ISBN 0-521-62971-3.
Baldi P. and Brunak S. (2001) Bioinformatics: The Machine Learning approach. MIT Press
For more details on pattern recognition and machine learning
Bishop C.M. (2006) Pattern Recognition and Machine Learning, Springer.
Duda R.O., Hart P.E. and Stork D.G. (2000) Pattern Classification, Wiley Interscience.
A good textbook for Molecular Biology is:
Alberts B. (2002).
Molecular Biology of the Cell
An intro level guide to programming PERL for Bioinformatics problems is:
Tisdal J. (2001)
Beginning Perl for Bioinformatics
Examples from the literature will be used throughout the lectures.
Resources
Biological Data Analysis Start Points:
Programming Tools and Libraries
DICE libraries
BioJava under DICE:
export
CLASSPATH=/usr/lib/BioJava/biojava.jar:/usr/lib/BioJava/xerces.jar:/usr/lib/BioJava/bytecode.jar:.
