Abstract
There has been an exponential growth in the performance and output of sequencing technologies (omics data) with full genome sequencing now producing gigabases of reads on a daily basis. These data may hold the promise of personalized medicine, leading to routinely available sequencing tests that can guide patient treatment decisions. In the era of high-throughput sequencing (HTS), computational considerations, data governance and clinical translation are the greatest rate-limiting steps. To ensure that the analysis, management and interpretation of such extensive omics data is exploited to its full potential, key factors, including sample sourcing, technology selection and computational expertise and resources, need to be considered, leading to an integrated set of high-performance tools and systems. This article provides an up-to-date overview of the evolution of HTS and the accompanying tools, infrastructure and data management approaches that are emerging in this space, which, if used within in a multidisciplinary context, may ultimately facilitate the development of personalized medicine.
Highlights
IntroductionThere have been exponential advances in our capacity to sequence a human genome
Over the past decade, there have been exponential advances in our capacity to sequence a human genome
While advances have been made across all aspects of the sequencing workflow, the focus on platform development has made a significant contribution to driving down machine size and highthroughput sequencing (HTS) costs while facilitating performance gains
Summary
There have been exponential advances in our capacity to sequence a human genome. A large number of CUDA-compatible HTS data processing and analysis tools have been developed in the past for use with RNA-seq [163] and DNA-seq, e.g. Cushaw [186], BarraCUDA [187], SOAP3 [188], CUDASWþþ [189] and SeqNFind [183], with a focus on sequence alignment using GPUs [186, 187] or CPUs and GPUs combined [189] (Table 1). These solutions differ in terms of scalability, flexibility, cost and computational expertise for implementation These solutions do not necessarily need to be taken individually, and the combination of clusters, GPUs, FPGAs and cloud-based workflows offers great promise to provide tailored genomic analysis solutions. If employers are empowered to this extent, there is a risk that the public will lose confidence in, and acceptance of, genetic testing, impacting negatively on the uptake in preventative screening
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