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Sapling: Accelerating Suffix Array Queries with Learned Data Models

View ORCID ProfileMelanie Kirsche, Arun Das, View ORCID ProfileMichael C. Schatz
doi: https://doi.org/10.1101/2020.01.29.925768
Melanie Kirsche
1Department of Computer Science, Johns Hopkins University, Baltimore, MD
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  • For correspondence: mkirsche@jhu.edu
Arun Das
1Department of Computer Science, Johns Hopkins University, Baltimore, MD
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Michael C. Schatz
1Department of Computer Science, Johns Hopkins University, Baltimore, MD
2Department of Biology, Johns Hopkins University, Baltimore, MD
3Cold Spring Harbor Laboratory, Cold Spring Harbor, NY
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Abstract

Motivation As genomic data becomes more abundant, efficient algorithms and data structures for sequence alignment become increasingly important. The suffix array is a widely used data structure to accelerate alignment, but the binary search algorithm used to query it requires widespread memory accesses, causing a large number of cache misses on large datasets.

Results Here we present Sapling, an algorithm for sequence alignment which uses a learned data model to augment the suffix array and enable faster queries. We investigate different types of data models, providing an analysis of different neural network models as well as providing an open-source aligner with a compact, practical piecewise linear model. We show that Sapling outperforms both an optimized binary search approach and multiple existing read aligners on a wide collection of genomes, including human, bacteria, and plants, speeding up the algorithm by more than a factor of two while adding less than 1% to the suffix array’s memory footprint.

Availability and implementation The source code and tutorial are available open-source at https://github.com/mkirsche/sapling.

Supplementary Information Supplementary notes and figures are available online.

Copyright 
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 January 30, 2020.
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Sapling: Accelerating Suffix Array Queries with Learned Data Models
Melanie Kirsche, Arun Das, Michael C. Schatz
bioRxiv 2020.01.29.925768; doi: https://doi.org/10.1101/2020.01.29.925768
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Sapling: Accelerating Suffix Array Queries with Learned Data Models
Melanie Kirsche, Arun Das, Michael C. Schatz
bioRxiv 2020.01.29.925768; doi: https://doi.org/10.1101/2020.01.29.925768

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