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arXiv · 1501.05353

Rapid Sequence Identification of Potential Pathogens Using Techniques from Sparse Linear Algebra

Stephanie Dodson·Darrell O. Ricke·Jeremy Kepner·Nelson Chiu·Anna Shcherbina
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Abstract

The decreasing costs and increasing speed and accuracy of DNA sample collection, preparation, and sequencing has rapidly produced an enormous volume of genetic data. However, fast and accurate analysis of the samples remains a bottleneck. Here we present D$^{4}$RAGenS, a genetic sequence identification algorithm that exhibits the Big Data handling and computational power of the Dynamic Distributed Dimensional Data Model (D4M). The method leverages linear algebra and statistical properties to increase computational performance while retaining accuracy by subsampling the data. Two run modes, Fast and Wise, yield speed and precision tradeoffs, with applications in biodefense and medical diagnostics. The D$^{4}$RAGenS analysis algorithm is tested over several datasets, including three utilized for the Defense Threat Reduction Agency (DTRA) metagenomic algorithm contest.

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Stephanie Dodson, Darrell O. Ricke, Jeremy Kepner, Nelson Chiu, Anna Shcherbina. 2015-01-21. Rapid Sequence Identification of Potential Pathogens Using Techniques from Sparse Linear Algebra. https://doi.org/10.1109/ths.2015.7225316

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