Entropy-scaling search of massive biological data

TL;DR

This paper introduces a framework for similarity search in large biological datasets based on entropy and fractal dimension, enabling faster search tools with minimal loss of accuracy.

Contribution

It develops a theoretical framework linking dataset structure to search efficiency and demonstrates accelerated tools across multiple biological domains.

Findings

150x speedup in drug screening

3.5x speedup in metagenomics search

10x speedup in protein structure search

Abstract

Many datasets exhibit a well-defined structure that can be exploited to design faster search tools, but it is not always clear when such acceleration is possible. Here, we introduce a framework for similarity search based on characterizing a dataset's entropy and fractal dimension. We prove that searching scales in time with metric entropy (number of covering hyperspheres), if the fractal dimension of the dataset is low, and scales in space with the sum of metric entropy and information-theoretic entropy (randomness of the data). Using these ideas, we present accelerated versions of standard tools, with no loss in specificity and little loss in sensitivity, for use in three domains---high-throughput drug screening (Ammolite, 150x speedup), metagenomics (MICA, 3.5x speedup of DIAMOND [3,700x BLASTX]), and protein structure search (esFragBag, 10x speedup of FragBag). Our framework can be used to achieve "compressive omics," and the general theory can be readily applied to data science problems outside of biology.