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.

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The availability of an intellectual property core for recursive least squares (RLS) filtering could enable the RLS algorithm to replace the least mean squares algorithm in a wide range of applications. The goal of this study is to develop a parameterizable generic architecture for RLS filtering in the form of a hardware description language (HDL) description, which can be used to generate highly efficient silicon layout. The key issue is to develop a family of circuit architectures that are 100% efficient and locally connected. This paper presents a generic mapping for RLS filtering and circuit architectures that can be mapped to a range of application requirements. It outlines the transition from array to architecture covering detailed design issues such as timing and control generation. The result is a family of QR designs, which are parameterized in terms of architecture size, wordlength, performance, and arithmetic processor timing.