1. Bioinformatics Jan Taylor

2. A bit about me Biochemistry and Molecular Biology Computer Science, Computational Biology Multivariate statistics Machine learning Experiment design Microbiology Plant Science Environment Animal Health Ontology/database development Programming Human Cancer Inherited Disease

3. What is bioinformatics anyway? Definition: Application of computational and analysis tools to the capture and interpretation of biological data

4. What does that mean? Mathematics IT/Engineering Statistics Processor development Network traffic improvement Storage solutions Artificial Intelligence Pattern recognition Text mining Image processing Simulation 3D structure visualisation Surface modelling ontologies Databases Sequence alignment Comparative genomics Drug design Protein:protein interactions Gene finding Protein folding Homology searching Evolutionary modelling Gene expression analysis Non-coding RNA GWAS Annotation Epidemiology Personalised medicine Biological networks

5. Challenges Databases and data resources – Because we need to store and retrieve lots of data Search and analysis tools – Because we need to infer function by comparison Interfaces and visualisation tools – Because we need to look at lots of data

6. Large scale biology NameStudy of Genomicsentire genome of an organism Transcriptomicsexpressed genes Exomicscoding sequences Proteomicsproteins within an organism Metabolomicsmetabolites within an organism Interactomicsinteractions between nucleotides, proteins and metabolites Connectomicsneural pathways in the brain Pharmocogeno mics application of genomics to pharmacology Phenomicsobservable phenotypes Physiomicsfunctional behaviour of an organism Exposomicsorganism’s environment Bibliomicsliterature concerning a topic

7. Genetics and genomics Genetics – Study of single genes, sequences, variation, inheritance and roles in health and disease Genomics – Study of all the genes in an individual, their interactions with each other, the environment and roles in complex disease

8. Genomic data NGS technologies leading to massive growth of sequence data NHS and research labs moving to using NGS for testing

9. Analysis stages Primary – Obtaining raw data Secondary – Turning the raw data into genome sequence Tertiary – Biological interpretation

10. To ask biologically meaningful questions What genes are in chromosomal region X and are linked to disease? What genes cause the condition? What is the normal function of gene Y? What mutations have been linked to diseases A and B? How does the mutation M alter gene function F? What is the 3D structure of gene Y’s product? Is gene Y expressed in condition C? Are there any known variants of gene G?

11. Clinical bioinformatics CLINICAL BIOINFORMATICS Personalised healthcare, Understanding of genetic, molecular and cellular basis of disease Clinical data basic biology bioinformatics

12. Application of bioinformatics To clinical problems – Understanding disease – Treatment and management – Development of medicines – Tailoring treatment

13. Growth Area NGS becoming a diagnostic tool in genetics/genomics labs Emergence for the need for ‘data scientists’ – beyond genomics UK 100K Genome project – a driver for the NHS

14. Career Prospects Fantastic! Clinical route: – MSC STP training program in Clinical Bioinformatics Keen to recruit from mathematics and computer science backgrounds Research route: -Many departments now have interdisciplinary research programs

15. Top Tips Teach yourself some biology – an understanding of the concepts and main principles of the application area Communication skills are vital