When the first human genome was sequenced, the whole project was estimated to have cost some 3 billion dollars and took over 13 years. Now, with massively parallel Next Generation Sequencing it is possible to sequence the human genome in less than 24 hours at a cost of under $1,000. With a rapid decline in sequencing costs, both the number of applications and the amount of data produced have boomed, driving a revolution in medicine. However, with the advent of new enabling technologies, what is next? What are the new and exciting technologies in the sequencing space which will be truly disruptive in the future? And what technical innovation is required to bring these technologies to the mainstream user or even the clinic?
In this episode of Invent: Life Sciences, we explore the race to innovate in the sequencing arena, what’s happened so far, and what’s going to happen next.
Find out more on this week’s episode of Invent: Life Sciences from TTP.
This Week’s Guests
Dr. Geoff Smith
Geoff is a next generation sequencing pioneer who has built and led many of the teams that invented and developed the entire NGS workflow – from sample prep, to the core sequencing technology, to new instrument systems. Previously Ilumina’s global head of technology development, he is now an independent board member of various exciting start ups.
Dr. Lauren Laing
Lauren leads the ‘Omics Team at TTP – a team addressing current needs in DNA and RNA sequencing and also looking to develop tools for future multi- proteo- and other ‘omic workflows. Prior to this Lauren has worked in developing new sequencing technologies, novel chemistries, approaches to automating sample preparation, and research applying sequencing in single cell and epigenetic applications.
Dr. James Hadfield
James is the Senior Director of Oncology Translational Medicine at AstraZeneca and author of the CoreGenomics blog, enseqlopedia. A life-sciences researcher and senior operational manager with over 20 years’ experience in Genomics technologies, James is a thought leader in genomics with a broad network across academia and industry.
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