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SPRITE: A Fast Parallel SNP Detection Pipeline

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Book cover High Performance Computing (ISC High Performance 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9697))

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Abstract

We present Sprite, a new high-performance data analysis pipeline for detecting single nucleotide polymorphisms (SNPs) in the human genome. A SNP detection pipeline for next-generation sequencing data uses several software tools, including tools for read alignment, processing alignment output, and SNP identification. We target end-to-end scalability and I/O efficiency in Sprite by merging tools in this pipeline and eliminating redundancies. For a benchmark human whole-genome sequencing data set, Sprite takes less than 50 min on 16 nodes of the TACC Stampede supercomputer. A key component of our optimized pipeline is parsnip, a new parallel method and software tool for SNP detection. We find that the quality of results obtained by parsnip (sensitivity and precision using high-confidence variant calls as ground truth) is comparable to state-of-the-art SNP-calling software. A prototype implementation of Sprite is available at sprite-psu.sourceforge.net.

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Acknowledgments

This research is supported by the National Science Foundation award # 1439057. We thank members of our project research team for helpful discussions.

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Authors and Affiliations

  1. The Pennsylvania State University, University Park, PA, USA

    Vasudevan Rengasamy & Kamesh Madduri

Authors
  1. Vasudevan Rengasamy
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  2. Kamesh Madduri
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Corresponding author

Correspondence to Vasudevan Rengasamy .

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Editors and Affiliations

  1. Deutsches Klimarechenzentrum, Hamburg, Germany

    Julian M. Kunkel

  2. Argonne National Laboratory, Lemont, Illinois, USA

    Pavan Balaji

  3. University of Tennessee, Knoxville, Tennessee, USA

    Jack Dongarra

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Rengasamy, V., Madduri, K. (2016). SPRITE: A Fast Parallel SNP Detection Pipeline. In: Kunkel, J., Balaji, P., Dongarra, J. (eds) High Performance Computing. ISC High Performance 2016. Lecture Notes in Computer Science(), vol 9697. Springer, Cham. https://doi.org/10.1007/978-3-319-41321-1_9

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  • DOI: https://doi.org/10.1007/978-3-319-41321-1_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-41320-4

  • Online ISBN: 978-3-319-41321-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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