Speakers at ICG-13

Speakers at ICG-13

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Biography

Dr Dan Frampton is the senior bioinformatician based in the Division of Infection and Immunity, UCL (University College London), UK.  He is currently working with Professor Rachel McKendry at the London Centre for Nanotechnology on the design of a rapid diagnostic test for drug resistance in tuberculosis.

 

His previous role was Senior Bioinformatician on the ICONIC and PANGEA projects (funded by the Wellcome Trust and the Bill & Melinda Gates Foundation, respectively), where he designed and implemented an automated pipeline to assemble viral genomes and identify drug resistance mutations from Next Generation Sequencing (NGS) data for ~20,000 clinical samples. He was responsible for investigating potential clinical linkages between patients in several influenza outbreaks, evaluating and advising on subsequent infection control measures.  Whilst developing analytical methods, Dr Frampton also established a local workflow for storing and updating NGS data for high-throughput genomic studies.

 

His current research interests centre around public health genomics, using NGS data to facilitate more accurate surveillance of infectious disease outbreaks and generate more patient-centric diagnostic and prognostic tools for clinicians. Dr Frampton is particularly interested in identifying and trialing broader areas that would benefit from his research methods. As such his previous projects have been quite diverse, including: host and viral transcriptomics of chickenpox, characterisation of llama B-cell repertoires and differential expression / methylation analysis of canine transmissible venereal tumours (CTVT).  These projects have shown that these innovative working methods can be applied widely and generate groundbreaking ways forward in scientific research. 


Abstract

Challenges for infectious disease biobanking in the age of NGS

 Dr Dan Frampton 1

1 Division of Infection and Immunity, UCL, Gower Street, London, UK

In terms of infectious disease, the vast wealth of data generated by new sequencing and diagnostic technologies provides researchers and public health bodies not only with the opportunity for vastly improved pathogen surveillance, but also the capability to better predict and prevent such pathogen outbreaks.  When these can be linked to electronic healthcare records and physical sample collections such as biobanks, the potential for personalised medicine is clear.  However, maximising the utility of this data is not trivial: there are several key challenges which must be met in order to make full use of this increase in diagnostic complexity. 

 

The first are practical: if not maintained locally, electronic biobanking requires rapidly moving large datasets between cloud and local storage and thus the development of novel storage and processing methods.  Indeed, in the case of genomics, sequencing capacity is growing at a faster rate than our ability to store data, making local data storage impractical.

 

A further challenge is in combining biobank data: sharing and collating data can greatly increase the power of downstream studies, but uniformity between biobanks is often low.  Thus improved compatibility across sampling, storage and data collection protocols is essential to facilitate efficient use of data between teams and study groups.  This requires biobanks to work more closely together, but also alongside molecular diagnostics laboratories to move towards more standardised outputs.  In addition, novel data processing algorithms and software are needed to link disparate biobank, molecular and clinical data, both to fully describe individual samples and to assist investigative research (e.g. identifying novel biomarkers, risk groups).

 

Ideally novel diagnostic technologies are integrated into existing surveillance and molecular pathology programmes, but this presents several additional problems: training current staff in new methodologies, the cost of adapting laboratories and storage facilities to meet new demands and the difficulty of introducing new diagnostic outputs into existing biobanking and data storage workflows, for example. 

 

Solutions to these issues need also to be flexible: meeting the needs of today’s researchers whilst being able to adjust to disruptive technologies of tomorrow.  This presentation will focus on these challenges and how we as a scientific community can best address them, with reference to examples from current projects.

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