Genetic Sequence Matching Using D4M Big Data Approaches

TL;DR

This paper introduces a fast genetic sequence analysis method using D4M big data techniques, achieving a hundredfold speed increase over traditional methods while maintaining comparable accuracy to BLAST.

Contribution

The paper presents a novel D4M-based algorithm for genetic sequence matching that significantly accelerates analysis using big data tools and database integration.

Findings

D4M method is approximately 100 times faster than traditional approaches.

D4M achieves comparable alignment accuracy to BLAST.

The approach leverages big data and associative arrays for efficient sequence analysis.

Abstract

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.