Genomics and Bioinformatics

Population genetics considers the medical and evolutionary significance of genetic variation in human populations. At Southampton we are concerned with understanding the mechanisms which generate and allow persistence of variation contributing to disease. The genome contains many thousands of DNA variants of which only a small number are likely to negatively impact health. Sequenced DNA is now available from a huge number of individuals providing unprecedented insights into the genetic structure of human populations. Analysis of these sequences allows prediction of the variants which likely to be damaging to an individual’s health. Sequenced genomes also provide important insights into the core evolutionary mechanisms (patterns of mutation, meiotic recombination, genetic drift, population migration, and natural selection) which determine the existence of this variation across the genome. Furthermore, analysis of DNA from ancient human populations provides exciting insights into the origins of disease variation and the greater susceptibility of some populations to certain diseases. Only by understanding the underlying population genetic mechanisms can robust interpretation of variation across the genome be achieved. Population genetic analysis is therefore the basis for many of the important analytical tools used in the clinical interpretation of patient DNA samples which enable a firm molecular diagnosis to be made. 

Data generation is no longer the limiting factor in genomics and other 'omic fields. New methods and approaches to the analysis and interpretation of increasingly complex and layered data represent the key challenge to enable us to fully exploit the rich array of information that is now routinely accessible.

In Southampton we work to develop and apply bioinformatic tools and machine learning methods for the integration and analysis of big-data, leveraging our local Iridis high-performance computing facility. We apply this expertise across all areas of research, including disease and cancer genomics, epigenomics and transcriptomics. As well as bioinformaticians having their own extensive research programmes, we actively collaborate with other researchers to help them carry out their research, to answer questions that they could not ask without bioinformatic expertise.

The Clinical Informatics Research Unit (CIRU) operates as an applied research and enterprise unit with the core aims to:

• Undertake applied research in software development, data modelling and definitions, and develop terminology and standards;
• Provide software and services to those engaged in clinical research to both improve quality and effectiveness, and drive new research questions within research data management;
• Deliver healthcare innovation through informatics solutions.

CIRU is made up of five key service groups known as EDGE, CORE, AXIS, RESIN and KITE covering data management, form design, data integration, global health research and new concepts and clinical innovation design. All groups strive to transform clinical research and practice through novel approaches to information and software management. The CIRU's mission is to deliver Healthcare Innovation through Informatics solutions, whilst advancing clinical research across the globe.

RNA transcriptomics is one of the key functional readouts of the genome. Studying RNA enables a myriad of analyses including gene expression, alternative splicing, identification of gene fusions and mutation discovery. Targeted RNA assays such as RT-PCR, qPCR and minigene analysis, and transcriptome-wide RNA-seq are increasingly being used to help interpret the effects of genomic variation. In many cases this can have important clinical implications for patients and their families, such as confirming that a novel gene variant alters splicing and is the cause of a disease or detecting fusion genes that cause cancer. RNA-seq can also generate gene expression profiles that can be used to aid diagnosis, predict patient outcomes or to help us understand the differences between experimental conditions such as treatment response or tumour biology. Through personalised medicine, RNA-seq can also be used to help guide development of novel therapies for many different types of condition. With the advent of single-cell RNA-seq (available in-house in Southampton via Drop-seq), cellular composition and behaviour can be studied with unparalleled detail in a tissue of interest.

Our genes are made of DNA, but their activity is regulated by more than their DNA sequence. Epigenetic marks are the chemical tags that mark DNA itself, as well as the many proteins and RNAs that organise DNA in the nucleus.  Changes in epigenetic marks orchestrate gene expression over an enormous dynamic range, across different cells, tissues, and developmental stages – they are essential for our bodies to grow, develop, and respond to the different environments we live in. 

But some epigenetic alterations – like genetic variations – are harmful to health, causing problems ranging from increased risk of heart disease, asthma or cancer, to severe inborn growth disorders. 

At Southampton we have a comprehensive, integrated portfolio of epigenetic research, from basic biology to clinical care.  We are investigating molecular mechanisms in development and regeneration.  We are harnessing genomewide and population approaches including epidemiology, genomics, epigenomics, transcriptomics, proteomics and systems biology.  We are identifying at-risk populations, functionally analysing biomarkers, and testing interventions in clinical trials. 

We are committed to translate our work into real benefit, not only through discovery, but through communicating, with established programmes of outreach and education tailored to schools, young mothers, health professionals and the public.

While high throughput sequencing of our DNA, epigenome and transcriptomes provides an unprecedented abundance of information on patients nucleic acids – sensitive interpretation of these data can be best served though combining with high throughput data of other types that help describe the impact of and interaction with nucleic acid change.