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A High Performance Storage Appliance for Genomic Data

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Bioinformatics and Biomedical Engineering (IWBBIO 2017)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 10209))

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Abstract

Rapid advancements in the area of next generation sequencing is revolutionizing the way in which biologists and now increasingly, clinicians analyze genomic data. These advances have substantially decreased the time and the cost it takes to sequence the genomes of new patients, thereby making genomic techniques more mainstream and giving rise to the new era of precision medicine. National scale genome programs have been launched in various parts of the world such as USA, the United Kingdom, and Saudi Arabia to name a few. One of the key insights out of this mainstream adoption is that even though the time and cost of generating sequence data has decreased dramatically, the cost of analyzing the data to yield clinically relevant information has not proportionally decreased. On the contrary, downstream analysis of the genomic data now dominates the cost in terms of time, effort and monetary value. This could be attributed to a number of factors: the sheer volume of data, limited knowledge of phenotypic, regulatory and epigenetic artifacts within the genome, and limited computational capabilities of existing data analysis tools and infrastructure. Overcoming these challenges is central to realize a more accurate, sophisticated and cost-effective genomic medicine. Another challenge, related to the limited analytic capabilities of existing computational and storage infrastructure is what we address in this paper. We discuss how novel trends in hardware, including the emergence of cheap, high performance and endurance solid-state storage associated with low latency interconnect and software defined orchestration, can help creating a high performance storage tier which improves data acquisition, storage, transmission and analysis over the current commercial alternatives.

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Acknowledgments

This publication was supported by the Saudi Human Genome Project, King Abdulaziz City for Science and Technology (KACST). Our thanks to Majed Alelaiwi, Gabriele Paciucci, Adam Roe and Ahmad Al-jeshi of Intel for their collaboration throughout the project. Our thanks to Majed Alelaiwi, Gabriele Paciucci, Craig Rhodes, Adam Roe and Ahmad Al-jeshi of Intel for their collaboration throughout the project. We would also like to thank Faheem Karim and Martin Galle from Supermicro on their advice on chassis and configuration. We would like to thank Vaughn Wittorff and Dan Greenfield of PetaGene (Cambridge, UK) for allowing us to use their test compression runs.

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Correspondence to Mohamed Abouelhoda .

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Kaul, G., Shah, Z.A., Abouelhoda, M. (2017). A High Performance Storage Appliance for Genomic Data. In: Rojas, I., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2017. Lecture Notes in Computer Science(), vol 10209. Springer, Cham. https://doi.org/10.1007/978-3-319-56154-7_43

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  • DOI: https://doi.org/10.1007/978-3-319-56154-7_43

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-56153-0

  • Online ISBN: 978-3-319-56154-7

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