Ella Cullen
University of Stirling, United Kingdom
Title: Ultra-fast drug resistance predictions from NGS sequence data using Cloud Computing
Biography
Biography: Ella Cullen
Abstract
Rapid and accurate prediction of drug resistance in pathogens is a growing need, affecting patient care and emerging personal
medicine. Bacterial genome sequencing has been introduced in many hospitals as a cheaper alternative to gene targeted
sequencing and PCR, but many handling issue remain to be overcome. Here, we address some of the challenges, by offering a cloudbased
solution that while keeping security and privacy at the heart of the development allows remote management of large datasets,
and ultra-fast drug resistance predictions without the need for local installation, maintenance or bioinformatics knowledge. Validated
using literature references, we have implemented a profiler that reconstitutes (from raw sequences) the genes associated with resistance
and produces an ultra-fast and accurate prediction of drug resistance. If raw sequences are available, regardless of the platform
used, the profiler will generate a prediction within minutes, in contrast to other solutions which typically require hours of analysis
time and interpretation. The profiler currently focuses on Mycobacterium tuberculosis and 9 key drugs, including Aminoglycosides
(Kanamycin, Capreomycin, Amikacin, Viomycin), Ethambutol, Ethionamide, Fluoroquinolones, Isoniazid, Para-Aminosalisylic
Acid, Pyrazinamide, Rifampicin, and Streptomycin.