Bioinformatics 2

You will need a DICE account for this course, or a laptop with python/jupyter/biopython, to run software in class. Students from outside informatics should visit the ITO to obtain their account.

Course Details

Course Concept and Aims

A primary requisite for relevant, and efficient, research in Bioinformatics is that scientists from both fields (biology and informatics) are involved, or consulted. Team work can only be successful if all parties have a basic ground knowledge of the respective other field and, most importantly, that they can communicate with each other.

The aims of the course are to help you overcome both of these difficulties in your future careers. The course will cover topics that include core biology concepts that relate to Bioinformatics, biological data and their source and structure as well as common tools for their analysis.

This course builds on basic knowledge acquired in Bioinformatics 1, and will introduce advanced and modern concepts used in functional genomics and modelling.

As Bioinformatics 1, the course will involve group-based practical work on using and developing Bioinformatics solutions.

Course catalogue entry

Summary of Intended Learning Outcomes:

Course Discussion Forum

If the class is interested, we can use NB or a similar system as a discussion forum. Please feel free to self-organise, and let us know so we can participate.


All lectures are in Appleton Tower Room 5.08 (South Lab).

This is a computing lab since we aim to use examples and media based walkthroughs in the class. Sessions will typically have a mix of lecture style material with tutorial and lab type events used where appropriate. Please ask questions during the class.

For those without DICE login accounts, please go to the ITO in Appleton Tower to collect your passwords. Let them know you are from another school but registered for Bioinformatics 2. Any problems should be fixed there.

Schedule (tentative)

Future slides linked up below are still from the 2017 session and will be updated.
17 Jan: Sequencing and Genome Assembly
24 Jan: Probabilistic Modelling 1
31 Jan: Probabilistic Modelling 2
07 Feb: Gene Features
14 Feb: Networks: theory and applications
21 Feb: FLW - no lecture
28 Feb: Ontologies (Ian Simpson)
7 Mar: Guest lecture by Chris Larminie (GSK)
14 Feb: Guest lecture by Igor Goryanin
14 Mar: Gene set analysis (Grant Robertson)
21 Mar: tbd
tbd: Feedback and questions session


The course will have a mini project, which will be set following the first session. This will be worth 30%. The rest of the marks will come from an end of semester exam.

To access the assignment, go here (data will be provided after the first few sessions). Deadline is 19 March 2018, 4pm. Submit the assignment via the submit command on DICE or hand it in at ITO before the deadline.


We will give written feedback on the submitted coursework, and will schedule at least one Q&A and feedback sessions. Since most lectures will have a practical component, we will also provide direct feedback during these sessions. We encourage everyone to actively participate and to ask questions whenever they arise. Course staff can always be contacted by email to ask questions and/or arrange a meeting. Please let us know if you have any constructive criticism about the course, or if you are interested in learning more about particular areas.


Course Staff are:

We have a mix of wet-lab and computational activities so can be hard to find. Always best to drop an email with the question and we'll meet or mail you asap.


Basic concepts are well explained in the following book, which is available online on campus:
Julia E. Richards , R. Scott Hawley Chapter 3 - The Central Dogma of Molecular Biology : How Cells Orchestrate the Use of Genetic Information The Human Genome A User's Guide, 2011

Apart from this, we will not be using a specific text book. All material required for the course will be linked from the course web pages. For looking up specific concepts we recommend the use of Pubmed's bookshelf system. For a more interactive introduction to many concepts we recommend the excellent Dolan DNA Learning Centre websites.

A good textbook covering basic and advanced concepts is:
Jones N.C. and Pevzner P. (2004) An Introduction to Bioinformatics Algorithms, MIT Press

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