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

TL;DR

The paper introduces D4RAGenS, a fast and accurate genetic sequence identification algorithm that uses linear algebra and subsampling to handle large genomic datasets efficiently, with applications in biodefense and diagnostics.

Contribution

It presents a novel sequence identification method leveraging D4M and linear algebra, offering two modes for speed and accuracy tradeoffs, suitable for large-scale genomic data analysis.

Findings

Demonstrates high accuracy in pathogen identification

Achieves significant speed improvements over existing methods

Validated on datasets from DTRA contest

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.