3 Search Results for "Korobeynikov, Anton"

Document
DOI: 10.4230/LIPIcs.ESA.2025.18
Efficiency of Learned Indexes on Genome Spectra

Authors: Md. Hasin Abrar, Paul Medvedev, and Giorgio Vinciguerra

Published in: LIPIcs, Volume 351, 33rd Annual European Symposium on Algorithms (ESA 2025)

Abstract
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.
Cite as

Md. Hasin Abrar, Paul Medvedev, and Giorgio Vinciguerra. Efficiency of Learned Indexes on Genome Spectra. In 33rd Annual European Symposium on Algorithms (ESA 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 351, pp. 18:1-18:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{abrar_et_al:LIPIcs.ESA.2025.18,
  author =	{Abrar, Md. Hasin and Medvedev, Paul and Vinciguerra, Giorgio},
  title =	{{Efficiency of Learned Indexes on Genome Spectra}},
  booktitle =	{33rd Annual European Symposium on Algorithms (ESA 2025)},
  pages =	{18:1--18:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-395-9},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{351},
  editor =	{Benoit, Anne and Kaplan, Haim and Wild, Sebastian and Herman, Grzegorz},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ESA.2025.18},
  URN =		{urn:nbn:de:0030-drops-244865},
  doi =		{10.4230/LIPIcs.ESA.2025.18},
  annote =	{Keywords: Genome spectra, piecewise linear approximation, learned index, k-mers}
}
Document
DOI: 10.4230/LIPIcs.WABI.2025.6
Fast Pseudoalignment Queries on Compressed Colored de Bruijn Graphs

Authors: Alessio Campanelli, Giulio Ermanno Pibiri, and Rob Patro

Published in: LIPIcs, Volume 344, 25th International Conference on Algorithms for Bioinformatics (WABI 2025)

Abstract
Motivation. Indexes for the colored de Bruijn graph (c-dBG) play a crucial role in computational biology by facilitating complex tasks such as read mapping and assembly. These indexes map k-mers (substrings of length k) appearing in a large collection of reference strings to the set of identifiers of the strings where they appear. These sets, colloquially referred to as color sets, tend to occupy large quantities of memory, especially for large pangenomes. Our previous work thus focused on leveraging the repetitiveness of the color sets to improve the space effectiveness of the resulting index. As a matter of fact, repetition-aware indexes can be up to one order of magnitude smaller on large pangenomes compared to indexes that do not exploit such repetitiveness. Such improved space effectiveness, on the other hand, imposes an overhead at query time when performing tasks such as pseudoalignment that require the collection and processing of multiple related color sets. Methods. In this paper, we show how to avoid this overhead. We devise novel query algorithms tailored for the specific repetition-aware representations adopted by the Fulgor index, a state-of-the-art c-dBG index, to significantly improve its pseudoalignment efficiency and without consuming additional space. Results. Our results indicate that with increasing redundancy in the pangenomes, the compression factor provided by the Fulgor index increases, while the relative query time actually reduces. For example, while the space of the Fulgor index improves by 2.5× with repetition-aware compression and its query time improves by 1.6× on a collection of 5,000 Salmonella Enterica genomes, these factors become (6.1×,2.8×) and (11.2×,3.2×) for 50,000 and 150,000 genomes respectively. For an even larger collection of 300,000 genomes, we obtained an index that is 22.3× smaller and 2.2× faster.
Cite as

Alessio Campanelli, Giulio Ermanno Pibiri, and Rob Patro. Fast Pseudoalignment Queries on Compressed Colored de Bruijn Graphs. In 25th International Conference on Algorithms for Bioinformatics (WABI 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 344, pp. 6:1-6:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{campanelli_et_al:LIPIcs.WABI.2025.6,
  author =	{Campanelli, Alessio and Pibiri, Giulio Ermanno and Patro, Rob},
  title =	{{Fast Pseudoalignment Queries on Compressed Colored de Bruijn Graphs}},
  booktitle =	{25th International Conference on Algorithms for Bioinformatics (WABI 2025)},
  pages =	{6:1--6:21},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-386-7},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{344},
  editor =	{Brejov\'{a}, Bro\v{n}a and Patro, Rob},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.WABI.2025.6},
  URN =		{urn:nbn:de:0030-drops-239327},
  doi =		{10.4230/LIPIcs.WABI.2025.6},
  annote =	{Keywords: Colored de Bruijn graphs, Pseudoalignment, Repetition-aware compression}
}
Document
DOI: 10.4230/LIPIcs.WABI.2017.14
Assessing the Significance of Peptide Spectrum Match Scores

Authors: Anastasiia Abramova and Anton Korobeynikov

Published in: LIPIcs, Volume 88, 17th International Workshop on Algorithms in Bioinformatics (WABI 2017)

Abstract
Peptidic Natural Products (PNPs) are highly sought after bioactive compounds that include many antibiotic, antiviral and antitumor agents, immunosuppressors and toxins. Even though recent advancements in mass-spectrometry have led to the development of accurate sequencing methods for nonlinear (cyclic and branch-cyclic) peptides, requiring only picograms of input material, the identification of PNPs via a database search of mass spectra remains problematic. This holds particularly true when trying to evaluate the statistical significance of Peptide Spectrum Matches (PSM) especially when working with non-linear peptides that often contain non-standard amino acids, modifications and have an overall complex structure. In this paper we describe a new way of estimating the statistical significance of a PSM, defined by any peptide (including linear and non-linear), by using state-of-the-art Markov Chain Monte Carlo methods. In addition to the estimate itself our method also provides an uncertainty estimate in the form of confidence bounds, as well as an automatic simulation stopping rule that ensures that the sample size is sufficient to achieve the desired level of result accuracy.
Cite as

Anastasiia Abramova and Anton Korobeynikov. Assessing the Significance of Peptide Spectrum Match Scores. In 17th International Workshop on Algorithms in Bioinformatics (WABI 2017). Leibniz International Proceedings in Informatics (LIPIcs), Volume 88, pp. 14:1-14:11, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2017)

Copy BibTex To Clipboard

@InProceedings{abramova_et_al:LIPIcs.WABI.2017.14,
  author =	{Abramova, Anastasiia and Korobeynikov, Anton},
  title =	{{Assessing the Significance of Peptide Spectrum Match Scores}},
  booktitle =	{17th International Workshop on Algorithms in Bioinformatics (WABI 2017)},
  pages =	{14:1--14:11},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-050-7},
  ISSN =	{1868-8969},
  year =	{2017},
  volume =	{88},
  editor =	{Schwartz, Russell and Reinert, Knut},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.WABI.2017.14},
  URN =		{urn:nbn:de:0030-drops-76411},
  doi =		{10.4230/LIPIcs.WABI.2017.14},
  annote =	{Keywords: mass spectrometry, natural products, peptide spectrum matches, statistical significance}
}
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