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Current metagenomic analysis algorithms require significant computingresources, can report excessive false positives (type I errors), may missorganisms (type II errors / false negatives), or scale poorly on largedatasets. This paper explores using big data database technologies tocharacterize very large metagenomic DNA sequences in protein space, with theultimate goal of rapid pathogen identification in patient samples. Our approachuses the abilities of a big data databases to hold large sparse associativearray representations of genetic data to extract statistical patterns about thedata that can be used in a variety of ways to improve identificationalgorithms.

Using a Big Data Database to Identify Pathogens in Protein Data Space