2 Search Results for "Shibuya, Yoshihiro"

Document
DOI: 10.4230/LIPIcs.WABI.2022.14
Efficient Reconciliation of Genomic Datasets of High Similarity

Authors: Yoshihiro Shibuya, Djamal Belazzougui, and Gregory Kucherov

Published in: LIPIcs, Volume 242, 22nd International Workshop on Algorithms in Bioinformatics (WABI 2022)

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).
Cite as

Yoshihiro Shibuya, Djamal Belazzougui, and Gregory Kucherov. Efficient Reconciliation of Genomic Datasets of High Similarity. In 22nd International Workshop on Algorithms in Bioinformatics (WABI 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 242, pp. 14:1-14:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@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-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.WABI.2022.14},
  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}
}
Document
DOI: 10.4230/LIPIcs.WABI.2021.8
Space-Efficient Representation of Genomic k-Mer Count Tables

Authors: Yoshihiro Shibuya, Djamal Belazzougui, and Gregory Kucherov

Published in: LIPIcs, Volume 201, 21st International Workshop on Algorithms in Bioinformatics (WABI 2021)

Abstract
Motivation. k-mer counting is a common task in bioinformatic pipelines, with many dedicated tools available. Output formats could rely on quotienting to reduce the space of k-mers in hash tables, however counts are not usually stored in space-efficient formats. Overall, k-mer count tables for genomic data take a considerable space, easily reaching tens of GB. Furthermore, such tables do not support efficient random-access queries in general. Results. In this work, we design an efficient representation of k-mer count tables supporting fast random-access queries. We propose to apply Compressed Static Functions (CSFs), with space proportional to the empirical zero-order entropy of the counts. For very skewed distributions, like those of k-mer counts in whole genomes, the only currently available implementation of CSFs does not provide a compact enough representation. By adding a Bloom Filter to a CSF we obtain a Bloom-enhanced CSF (BCSF) effectively overcoming this limitation. Furthermore, by combining BCSFs with minimizer-based bucketing of k-mers, we build even smaller representations breaking the empirical entropy lower bound, for large enough k. We also extend these representations to the approximate case, gaining additional space. We experimentally validate these techniques on k-mer count tables of whole genomes (E.Coli and C.Elegans) as well as on k-mer document frequency tables for 29 E.Coli genomes. In the case of exact counts, our representation takes about a half of the space of the empirical entropy, for large enough k’s.
Cite as

Yoshihiro Shibuya, Djamal Belazzougui, and Gregory Kucherov. Space-Efficient Representation of Genomic k-Mer Count Tables. In 21st International Workshop on Algorithms in Bioinformatics (WABI 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 201, pp. 8:1-8:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{shibuya_et_al:LIPIcs.WABI.2021.8,
  author =	{Shibuya, Yoshihiro and Belazzougui, Djamal and Kucherov, Gregory},
  title =	{{Space-Efficient Representation of Genomic k-Mer Count Tables}},
  booktitle =	{21st International Workshop on Algorithms in Bioinformatics (WABI 2021)},
  pages =	{8:1--8:19},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-200-6},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{201},
  editor =	{Carbone, Alessandra and El-Kebir, Mohammed},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.WABI.2021.8},
  URN =		{urn:nbn:de:0030-drops-143619},
  doi =		{10.4230/LIPIcs.WABI.2021.8},
  annote =	{Keywords: k-mer counting, data structures, compression, minimizers, compressed static function, Bloom filter, empirical entropy}
}
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