License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.WABI.2022.14
URN: urn:nbn:de:0030-drops-170481
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2022/17048/
Go to the corresponding LIPIcs Volume Portal

Shibuya, Yoshihiro ; Belazzougui, Djamal ; Kucherov, Gregory

Efficient Reconciliation of Genomic Datasets of High Similarity

pdf-format:
LIPIcs-WABI-2022-14.pdf (0.8 MB)

Abstract

We apply Invertible Bloom Lookup Tables (IBLTs) to the comparison of k-mer sets originated from large DNA sequence datasets. We show that for similar datasets, IBLTs provide a more space-efficient and, at the same time, more accurate method for estimating Jaccard similarity of underlying k-mer sets, compared to MinHash which is a go-to sketching technique for efficient pairwise similarity estimation. This is achieved by combining IBLTs with k-mer sampling based on syncmers, which constitute a context-independent alternative to minimizers and provide an unbiased estimator of Jaccard similarity. A key property of our method is that involved data structures require space proportional to the difference of k-mer sets and are independent of the size of sets themselves. As another application, we show how our ideas can be applied in order to efficiently compute (an approximation of) k-mers that differ between two datasets, still using space only proportional to their number. We experimentally illustrate our results on both simulated and real data (SARS-CoV-2 and Streptococcus Pneumoniae genomes).

BibTeX - Entry

@InProceedings{shibuya_et_al:LIPIcs.WABI.2022.14,
  author =	{Shibuya, Yoshihiro and Belazzougui, Djamal and Kucherov, Gregory},
  title =	{{Efficient Reconciliation of Genomic Datasets of High Similarity}},
  booktitle =	{22nd International Workshop on Algorithms in Bioinformatics (WABI 2022)},
  pages =	{14:1--14:14},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-243-3},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{242},
  editor =	{Boucher, Christina and Rahmann, Sven},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2022/17048},
  URN =		{urn:nbn:de:0030-drops-170481},
  doi =		{10.4230/LIPIcs.WABI.2022.14},
  annote =	{Keywords: k-mers, sketching, Invertible Bloom Lookup Tables, IBLT, MinHash, syncmers, minimizers}
}

Keywords: k-mers, sketching, Invertible Bloom Lookup Tables, IBLT, MinHash, syncmers, minimizers
Collection: 22nd International Workshop on Algorithms in Bioinformatics (WABI 2022)
Issue Date: 2022
Date of publication: 26.08.2022
Supplementary Material: Software (Source Code): https://github.com/yhhshb/km-peeler.git

DROPS-Home | Fulltext Search | Imprint | Privacy Published by LZI