Informatics is increasingly useful in the analysis of large-scale data in forensics. This module will provide an opportunity to explore the use of informatics in a biological and/or chemical setting. This will enable students to understand how in silico approaches allows for new knowledge to be created.

The module will look at the applications of chemical graph theory for understanding the synthesis of novel drugs of abuse and their precursors, as well as how to interrogate a range of chemical databases and link this to experimentally-derived observations.
Furthermore, there will be lectures on the application of bioinformatics that will advance what has been seen in prior modules on DNA sequencing, as well as exploring how computational methods and open-source software can generate new insights applied to forensic science.

This module is assessed with a single 4000-word essay on informatics techniques, as outlined in the module handbook. This should effectively summarise both chemical and biological informatics approaches, their development through time and the trajectory of understanding in the field. The essay will encapsulate how data can be extracted from databases and analysed effectively – it should show how mathematical approaches underpin each type of analysis and how it can have an impact at the forefront of forensic science.

This module will be taught through a mix of computational workshops and lectures (up to 40 hours)

1. Utilise open-source software for the exploration of large datasets in forensics.

Enquiry
Knowledge & Understanding
Learning

2. Interrogate databases and repositories to generate new understanding of forensic data.

Analysis
Enquiry
Knowledge & Understanding
Problem Solving

3. Apply complex mathematical theories to chemical and biological problems.

Analysis
Application
Knowledge & Understanding
Problem Solving

Access to the University VLE
Open-source graphical software (or R packages)
R

Lander, J. P. (2014) R for Everyone: Advanced Analytics and Graphics. Boston (MA): Addison-Wesley.

Stinerock, R. (2018) Statistics with R. A beginners guide. London: Sage.

Further resources as provided by course team.