Review of applications of high-throughput sequencing in personalized medicine: barriers and facilitators of future progress in research and clinical application

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.

Keywords: clinical translation; cloud computing; grid computing; high-performance computing; high-throughput sequencing; personalized medicine; translational research.

Figure 1.

Summary schema of omics levels with associated technologies, data types, outputs, analytical considerations and research and clinical applications. Five levels or components, genome, epigenome, transcriptome, proteome and metabolome are presented, all of which can be considered with respect to the phenome (common patient characteristics). Key associations between omics levels are also represented, including transcription (between the genome and transcriptome), histone modification and TF-binding (connecting the epigenome with the proteome) and translation (from the transcriptome to the proteome). Source: Adapted from: [19–21] and [22].

Figure 2.

Overview of stages, barriers, facilitators and stakeholders in HTS pipelines from hypothesis setting to clinical interpretation. Eight common stages involved within a generic HTS pipeline/workflow are presented, set against factors acting as barriers to, or facilitators of, progress towards commercial/clinical translation and key stakeholders.

Figure 3.

Overview of the difference options for high HPC. This is an illustration of commodity clusters, GPUs, FPGAs and cloud solutions. It highlights differences in performance, flexibility and level of custom design. Note: HDL, hardware description language; RTL, register-transfer level.