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Rapid sequence identification of potential pathogens using techniques from sparse linear algebra

April 14, 2015
  |
Conference Paper
Author:
Stephanie A. Dodson
…
Published in:
HST 2015, IEEE Int. Conf. on Technologies for Homeland Security, 14-16 April 2015.
Topic:
high performance computing
R&D area:
R&D group:

Summary

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 D4RAGenS, 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 D4RAGenS analysis algorithm is tested over several datasets, including three utilized for the Defense Threat Reduction Agency (DTRA) metagenomic algorithm contest.
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Summary

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 D4RAGenS, a genetic sequence identification algorithm that exhibits the Big Data handling...

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Rapid sequence identification of potential pathogens using techniques from sparse linear algebra

Using a big data database to identify pathogens in protein data space [e-print]

January 22, 2015
  |
Journal Article
Author:
Ashley M. Conard
…
Published in:
https://arxiv.org/abs/1501.05546
Topic:
big data
R&D area:
R&D group:

Summary

Current metagenomic analysis algorithms require significant computing resources, can report excessive false positives (type I errors), may miss organisms (type II errors/false negatives), or scale poorly on large datasets. This paper explores using big data database technologies to characterize very large metagenomic DNA sequences in protein space, with the ultimate goal of rapid pathogen identification in patient samples. Our approach uses the abilities of a big data databases to hold large sparse associative array representations of genetic data to extract statistical patterns about the data that can be used in a variety of ways to improve identification algorithms.
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Summary

Current metagenomic analysis algorithms require significant computing resources, can report excessive false positives (type I errors), may miss organisms (type II errors/false negatives), or scale poorly on large datasets. This paper explores using big data database technologies to characterize very large metagenomic DNA sequences in protein space, with the ultimate...

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Using a big data database to identify pathogens in protein data space [e-print]

Genetic sequence matching using D4M big data approaches

September 9, 2014
  |
Conference Paper
Author:
Darrell O. Ricke
…
Published in:
HPEC 2014: IEEE Conf. on High Performance Extreme Computing, 9-11 September 2014.
Topic:
high performance computing
R&D area:
R&D group:

Summary

Recent technological advances in Next Generation Sequencing tools have led to increasing speeds of DNA sample collection, preparation, and sequencing. One instrument can produce over 600 Gb of genetic sequence data in a single run. This creates new opportunities to efficiently handle the increasing workload. We propose a new method of fast genetic sequence analysis using the Dynamic Distributed Dimensional Data Model (D4M) - an associative array environment for MATLAB developed at MIT Lincoln Laboratory. Based on mathematical and statistical properties, the method leverages big data techniques and the implementation of an Apache Acculumo database to accelerate computations one-hundred fold over other methods. Comparisons of the D4M method with the current gold-standard for sequence analysis, BLAST, show the two are comparable in the alignments they find. This paper will present an overview of the D4M genetic sequence algorithm and statistical comparisons with BLAST.
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Summary

Recent technological advances in Next Generation Sequencing tools have led to increasing speeds of DNA sample collection, preparation, and sequencing. One instrument can produce over 600 Gb of genetic sequence data in a single run. This creates new opportunities to efficiently handle the increasing workload. We propose a new method...

READ MORE
Genetic sequence matching using D4M big data approaches
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