Bioinformatics 2
Notes
Where possible I shall post lecture notes the evening after the lecture, so that (inevitable) mistakes spotted in the class can be corrected.
Please do not email me immediately after the class asking for the lecture slides. Additional reading will be posted before or after the lecture as appropriate.
2011 notes are available for reference
Lectures are held in George Sq 07 S37 at 11.10am until 1pm. Tutorials on Feb 01 and Feb 29 will be held in Appleton Tower AT 5.07 at 9am.
Lecture Notes
This lectures gives a quick introduction to the field of bioinformatics, and a review of the basic rules of probability. These are foundational basic blocks which are needed to understand the more advanced material discussed later, which in turn underpins many current bioinformatics algorithms.
For further material on basic probability and distributions, you may find
the following texts useful: Grimmet and Stirzaker, Probability and random processes (OUP), C.M. Bishop, Pattern Recognition and Machine Learning, Springer, particularly section 1.2. You should be able to find these books in the library and you may be able to find pdfs for them on the web. A great book which is available on the web is
Information Theory, inference and learning algorithms by David Mackay. Chapter 23 contains a review of useful distributions, including the ones mentioned in the lecture.
In this lecture we introduce some fundamental data analysis tools and concepts: foundations of statistical testing and principal component analysis.
Tutorial 1 (01 Feb 2012)
will be held in Appleton Tower AT5.07 at 9am. We will be covering the material of lectures 1 and 2. Questions are available
here.
This lecture introduces networks in biology and how they can be modelled statistically. A nice introductory review article (going in more depth than we do) is
this. Also of interest could be Terry Speed's
lectures at the Edinburgh summer school on statistical inference in computational biology in 2010 (in fact, these were my main inspiration).
Lecture 4 (8 Feb 2012) Transcriptomics - Guest lecture from Donald Dunbar
The lecture introduces the technologies for measuring and analysing mRNA expression data on a genome wide scale. Further reading to complement the lecture is recommended: a good starting point could be wikipedia's detailed page on
DNA microarrays. For a (much) more detailed treatment, you may wish to consuld Baldi and Wesley Hatfield, DNA Microarrays and Gene Expression, Cambridge University Press 2002.
The lab will introduce some of the features of the cytoscape visualisation tool for analysis of high throughput data in a network context. The slides contain a brief presentation about cytoscape functionalities and the task for the class.
Innovative learning week 20-27 Feb
. As part of the innovations, you get no teaching this week...
Tutorial 2 (29 Feb 2012)
will be held in Appleton Tower AT5.07 at 9am. We will be covering the material of lecture 2 and 3. This is now rescheduled to 07/03 9am in AT4.14A. Questions are available
here.
This lecture introduces dynamical systems and how they can be useful in modelling certain situations in biology. A good reference in general for dynamical systems and much of machine learning is David Barber's new book, available here (this is much more detailed than we need). A very good discussion of HMMs in sequence analysis problems is given in Durbin et al's book, Biological sequence analysis (on the reading list).
Lecture 6 (7 Mar 2012) ChIP-on-chip Technologies - Guest Lecture from Ian Simpson
Slides. This lecture introduced the fundamental technology behind ChIP-on-chip and ChIP-Seq, as well as the main data analysis tools. Further references and material can be found
here.
Lecture 8 (14 Mar 2012) Guest Lecture from Dr Chris Larminie, GSK
This lecture introduced the uses of bioinformatic tools in drug discovery within an industrial R\& D environment. The lecture slides are not posted here but have been emailed to the students.
Big Tutorial (21 Mar 2012) 11-1pm, solving together some simple questions in preparation for the exam
.
