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Title: Efficiency of Learned Indexes on Genome Spectra
Data structures on a multiset of genomic k-mers are at the heart of many bioinformatic tools. As genomic datasets grow in scale, the efficiency of these data structures increasingly depends on how well they leverage the inherent patterns in the data. One recent and effective approach is the use of learned indexes that approximate the rank function of a multiset using a piecewise linear function with very few segments. However, theoretical worst-case analysis struggles to predict the practical performance of these indexes. We address this limitation by developing a novel measure of piecewise-linear approximability of the data, called CaPLa (Canonical Piecewise Linear approximability). CaPLa builds on the empirical observation that a power-law model often serves as a reasonable proxy for piecewise linear-approximability, while explicitly accounting for deviations from a true power-law fit. We prove basic properties of CaPLa and present an efficient algorithm to compute it. We then demonstrate that CaPLa can accurately predict space bounds for data structures on real data. Empirically, we analyze over 500 genomes through the lens of CaPLa, revealing that it varies widely across the tree of life and even within individual genomes. Finally, we study the robustness of CaPLa as a measure and the factors that make genomic k-mer multisets different from random ones.  more » « less
Award ID(s):
2138585 1931531
PAR ID:
10664668
Author(s) / Creator(s):
; ;
Editor(s):
Benoit, Anne; Kaplan, Haim; Wild, Sebastian; Herman, Grzegorz
Publisher / Repository:
Schloss Dagstuhl – Leibniz-Zentrum für Informatik
Date Published:
Volume:
351
ISSN:
1868-8969
Page Range / eLocation ID:
18:1-18:18
Subject(s) / Keyword(s):
Genome spectra piecewise linear approximation learned index k-mers Applied computing → Bioinformatics Applied computing → Computational biology Theory of computation → Data structures design and analysis
Format(s):
Medium: X Size: 18 pages; 1706856 bytes Other: application/pdf
Size(s):
18 pages 1706856 bytes
Sponsoring Org:
National Science Foundation
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