What is Bioinformatics?
Modern genomic analysis creates large volumes of data which is impossible for humans to analyse manually. At AWMGS the bioinformatics team uses a range of open source and in house software to process raw genomic data into more human readable formats. Once the data has been processed by our software clinical scientists can then interpret the results and generate the patient reports. The type of software used to process and analyse the data depends on the sample type. For example different software is required for samples from patients with inherited disease and those from cancer patients.
Bioinformatics at AWMGL
Within AWMGS the bioinformatics team develops and maintains the high quality computer software which underpins the laboratories diagnostic and research roles. The bioinformatics team has a range of skills in software development, data analysis, database design, web development, data science, machine learning and statistics. The team applies these skills to develop new tests, automate laboratory processes and ensure high quality patient results. The role of the team is expanding and the team is increasingly involved in other areas such as IT Infrastructure, training and quality control.
Some examples of recent successes includes:
- The recently developed and validated PanCancer pipeline, which can detect multiple different types of mutations in cancer samples and which aids the diagnosis and treatment of patients.
- A new web application and database to aid the interpretation of genetic variants. The software system provides support to clinical scientists analysing variants using the widely used ACMG guidelines and stores the results in an easily accessible way.
The bioinformatics team develops software to process data from the laboratory’s Miseq, Hiseq, Nextseq and Novaseq instruments. Our bioinformatics software runs on a dedicated High Performance Computer (HPC) system to ensure that the data is processed rapidly. The team now supports the recently purchased DRAGEN platform which allows large amounts of genomic data to be processed much more rapidly than on a traditional computing system.