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MEM-Based Pangenome Indexing for k-mer Queries

Authors Stephen Hwang , Nathaniel K. Brown , Omar Y. Ahmed , Katharine M. Jenike , Sam Kovaka , Michael C. Schatz , Ben Langmead

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ACM Subject Classification
  • Applied computing → Computational genomics
Keywords
  • Pangenomics
  • Comparative genomics
  • Compressed indexing

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Abstract

Pangenomes are growing in number and size, thanks to the prevalence of high-quality long-read assemblies. However, current methods for studying sequence composition and conservation within pangenomes have limitations. Methods based on graph pangenomes require a computationally expensive multiple-alignment step, which can leave out some variation. Indexes based on k-mers and de Bruijn graphs are limited to answering questions at a specific substring length k. We present Maximal Exact Match Ordered (MEMO), a pangenome indexing method based on maximal exact matches (MEMs) between sequences. A single MEMO index can handle arbitrary-length queries over pangenomic windows. MEMO enables both queries that test k-mer presence/absence (membership queries) and that count the number of genomes containing k-mers in a window (conservation queries). MEMO’s index for a pangenome of 89 human autosomal haplotypes fits in 2.04 GB, 8.8× smaller than a comparable KMC3 index and 11.4× smaller than a PanKmer index. MEMO indexes can be made smaller by sacrificing some counting resolution, with our decile-resolution HPRC index reaching 0.67 GB. MEMO can conduct a conservation query for 31-mers over the human leukocyte antigen locus in 13.89 seconds, 2.5× faster than other approaches. MEMO’s small index size, lack of k-mer length dependence, and efficient queries make it a flexible tool for studying and visualizing substring conservation in pangenomes.

Cite As Get BibTex

Stephen Hwang, Nathaniel K. Brown, Omar Y. Ahmed, Katharine M. Jenike, Sam Kovaka, Michael C. Schatz, and Ben Langmead. MEM-Based Pangenome Indexing for k-mer Queries. In 24th International Workshop on Algorithms in Bioinformatics (WABI 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 312, pp. 4:1-4:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024) https://doi.org/10.4230/LIPIcs.WABI.2024.4

Author Details

Stephen Hwang
  • XDBio Program, Johns Hopkins University, Baltimore, MD, USA
Nathaniel K. Brown
  • Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
Omar Y. Ahmed
  • Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
Katharine M. Jenike
  • Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
Sam Kovaka
  • Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
Michael C. Schatz
  • Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
Ben Langmead
  • Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA

Funding

This work was carried out at the Advanced Research Computing at Hopkins (ARCH) core facility (rockfish.jhu.edu), supported by the National Science Foundation (NSF) grant OAC 1920103.
  • Hwang, Stephen: Johns Hopkins University, XDBio Program.
  • Brown, Nathaniel K.: Johns Hopkins University Computer Science PhD Fellowship.
  • Ahmed, Omar Y.: R01HG011392 and R35GM139602 to BL.
  • Jenike, Katharine M.: NSF grant 2216612, NIH grant U01CA253481, and HFSP award RGP0025/2021 to MCS.
  • Kovaka, Sam: NSF grant 2216612, NIH grant U01CA253481, and HFSP award RGP0025/2021 to MCS.
  • Schatz, Michael C.: NSF grant 2216612, NIH grant U01CA253481, and HFSP award RGP0025/2021 to MCS.
  • Langmead, Ben: R01HG011392 and R35GM139602 to BL.

Acknowledgements

We thank Christina Boucher for helpful conversations.

Supplementary Materials

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