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IMPROVING MIN HASH VIA THE CONTAINMENT INDEX WITH APPLICATIONS TO METAGENOMIC ANALYSIS

David Koslicki, Hooman Zabeti
doi: https://doi.org/10.1101/184150
David Koslicki
1Mathematics Department, Oregon State University, Corvallis, OR
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  • For correspondence: david.koslicki@math.oregonstate.edu
Hooman Zabeti
1Mathematics Department, Oregon State University, Corvallis, OR
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Abstract

Min hash is a probabilistic method for estimating the similarity of two sets in terms of their Jaccard index, defined as the ration of the size of their intersection to their union. We demonstrate that this method performs best when the sets under consideration are of similar size and the performance degrades considerably when the sets are of very different size. We introduce a new and efficient approach, called the containment min hash approach, that is more suitable for estimating the Jaccard index of sets of very different size. We accomplish this by leveraging another probabilistic method (in particular, Bloom filters) for fast membership queries. We derive bounds on the probability of estimate errors for the containment min hash approach and show it significantly improves upon the classical min hash approach. We also show significant improvements in terms of time and space complexity. As an application, we use this method to detect the presence/absence of organisms in a metagenomic data set, showing that it can detect the presence of very small, low abundance microorganisms.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted September 04, 2017.
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IMPROVING MIN HASH VIA THE CONTAINMENT INDEX WITH APPLICATIONS TO METAGENOMIC ANALYSIS
David Koslicki, Hooman Zabeti
bioRxiv 184150; doi: https://doi.org/10.1101/184150
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IMPROVING MIN HASH VIA THE CONTAINMENT INDEX WITH APPLICATIONS TO METAGENOMIC ANALYSIS
David Koslicki, Hooman Zabeti
bioRxiv 184150; doi: https://doi.org/10.1101/184150

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