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Spectrum preserving tilings enable sparse and modular reference indexing

View ORCID ProfileJason Fan, View ORCID ProfileJamshed Khan, View ORCID ProfileGiulio Ermanno Pibiri, View ORCID ProfileRob Patro
doi: https://doi.org/10.1101/2022.10.27.513881
Jason Fan
1University of Maryland, College Park, MD 20440, USA
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Jamshed Khan
1University of Maryland, College Park, MD 20440, USA
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Giulio Ermanno Pibiri
2Ca’ Foscari University of Venice, Venice, Italy
3ISTI-CNR, Pisa, Italy
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Rob Patro
1University of Maryland, College Park, MD 20440, USA
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  • For correspondence: rob@cs.umd.edu
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Abstract

The reference indexing problem for k-mers is to pre-process a collection of reference genomic sequences ℛ so that the position of all occurrences of any queried k-mer can be rapidly identified. An efficient and scalable solution to this problem is fundamental for many tasks in bioinformatics.

In this work, we introduce the spectrum preserving tiling (SPT), a general representation of ℛ that specifies how a set of tiles repeatedly occur to spell out the constituent reference sequences in ℛ. By encoding the order and positions where tiles occur, SPTs enable the implementation and analysis of a general class of modular indexes. An index over an SPT decomposes the reference indexing problem for k-mers into: (1) a k-mer-to-tile mapping; and (2) a tile-to-occurrence mapping. Recently introduced work to construct and compactly index k-mer sets can be used to efficiently implement the k-mer-to-tile mapping. However, implementing the tile-to-occurrence mapping remains prohibitively costly in terms of space. As reference collections become large, the space requirements of the tile-to-occurrence mapping dominates that of the k-mer-to-tile mapping since the former depends on the amount of total sequence while the latter depends on the number of unique k-mers in ℛ.

To address this, we introduce a class of sampling schemes for SPTs that trade off speed to reduce the size of the tile-to-reference mapping. We implement a practical index with these sampling schemes in the tool pufferfish2. When indexing over 30,000 bacterial genomes, pufferfish2 reduces the size of the tile-to-occurrence mapping from 86.3GB to 34.6GB while incurring only a 3.6× slowdown when querying k-mers from a sequenced readset.

Availability pufferfish2 is implemented in Rust and available at https://github.com/COMBINE-lab/pufferfish2.

Competing Interest Statement

R.P. is a co-founder of Ocean Genomics Inc.

Footnotes

  • jasonfan{at}umd.edu, jamshed{at}cs.umd.ed, giulioermanno.pibiri{at}unive.it

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-NC-ND 4.0 International license.
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Posted October 28, 2022.
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Spectrum preserving tilings enable sparse and modular reference indexing
Jason Fan, Jamshed Khan, Giulio Ermanno Pibiri, Rob Patro
bioRxiv 2022.10.27.513881; doi: https://doi.org/10.1101/2022.10.27.513881
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Spectrum preserving tilings enable sparse and modular reference indexing
Jason Fan, Jamshed Khan, Giulio Ermanno Pibiri, Rob Patro
bioRxiv 2022.10.27.513881; doi: https://doi.org/10.1101/2022.10.27.513881

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