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SPRITE: A Fast Parallel SNP Detection Pipeline

  • Vasudevan Rengasamy
  • Kamesh Madduri
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9697)

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.

Keywords

Alignment Position Maximum Memory Alignment Output Parallel Scaling Reference Contig 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

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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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.The Pennsylvania State UniversityUniversity ParkUSA

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