17338

A Linear Algebra Approach to Fast DNA Mixture Analysis Using GPUs

Siddharth Samsi, Brian Helfer, Jeremy Kepner, Albert Reuther, Darrell O. Ricke
MIT Lincoln Laboratory, Lexington, MA
arXiv:1707.00516 [cs.PF], (3 Jul 2017)

@article{samsi2017linear,

   title={A Linear Algebra Approach to Fast DNA Mixture Analysis Using GPUs},

   author={Samsi, Siddharth and Helfer, Brian and Kepner, Jeremy and Reuther, Albert and Ricke, Darrell O.},

   year={2017},

   month={jul},

   archivePrefix={"arXiv"},

   primaryClass={cs.PF}

}

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Analysis of DNA samples is an important step in forensics, and the speed of analysis can impact investigations. Comparison of DNA sequences is based on the analysis of short tandem repeats (STRs), which are short DNA sequences of 2-5 base pairs. Current forensics approaches use 20 STR loci for analysis. The use of single nucleotide polymorphisms (SNPs) has utility for analysis of complex DNA mixtures. The use of tens of thousands of SNPs loci for analysis poses significant computational challenges because the forensic analysis scales by the product of the loci count and number of DNA samples to be analyzed. In this paper, we discuss the implementation of a DNA sequence comparison algorithm by re-casting the algorithm in terms of linear algebra primitives. By developing an overloaded matrix multiplication approach to DNA comparisons, we can leverage advances in GPU hardware and algoithms for Dense Generalized Matrix-Multiply (DGEMM) to speed up DNA sample comparisons. We show that it is possible to compare 2048 unknown DNA samples with 20 million known samples in under 6 seconds using a NVIDIA K80 GPU.
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