29 Search Results for "Gagie, Travis"

Artifact
Software
DOI: 10.4230/artifacts.22480
muPBWT k-SMEM

Authors: Davide Cozzi

Abstract
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Davide Cozzi. muPBWT k-SMEM (Software, Source code). Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@misc{dagstuhl-artifact-22480,
   title = {{muPBWT k-SMEM}}, 
   author = {Cozzi, Davide},
   note = {Software, European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement PANGAIA No. 87253, swhId: \href{https://archive.softwareheritage.org/swh:1:dir:d3467768a54423c8294abfc44f87f18705b3ed02;origin=https://github.com/dlcgold/muPBWT;visit=swh:1:snp:e69e661f21c323ba367bb685b5cd0da27a8b0462;anchor=swh:1:rev:9dc88c898146ae314ceb58aef9daab6ba27caa8c}{\texttt{swh:1:dir:d3467768a54423c8294abfc44f87f18705b3ed02}} (visited on 2024-11-28)},
   url = {https://github.com/dlcgold/muPBWT/tree/k-smem},
   doi = {10.4230/artifacts.22480},
}
Document
DOI: 10.4230/LIPIcs.WABI.2024.10
b-move: Faster Bidirectional Character Extensions in a Run-Length Compressed Index

Authors: Lore Depuydt, Luca Renders, Simon Van de Vyver, Lennart Veys, Travis Gagie, and Jan Fostier

Published in: LIPIcs, Volume 312, 24th International Workshop on Algorithms in Bioinformatics (WABI 2024)

Abstract
Due to the increasing availability of high-quality genome sequences, pan-genomes are gradually replacing single consensus reference genomes in many bioinformatics pipelines to better capture genetic diversity. Traditional bioinformatics tools using the FM-index face memory limitations with such large genome collections. Recent advancements in run-length compressed indices like Gagie et al.’s r-index and Nishimoto and Tabei’s move structure, alleviate memory constraints but focus primarily on backward search for MEM-finding. Arakawa et al.’s br-index initiates complete approximate pattern matching using bidirectional search in run-length compressed space, but with significant computational overhead due to complex memory access patterns. We introduce b-move, a novel bidirectional extension of the move structure, enabling fast, cache-efficient bidirectional character extensions in run-length compressed space. It achieves bidirectional character extensions up to 8 times faster than the br-index, closing the performance gap with FM-index-based alternatives, while maintaining the br-index’s favorable memory characteristics. For example, all available complete E. coli genomes on NCBI’s RefSeq collection can be compiled into a b-move index that fits into the RAM of a typical laptop. Thus, b-move proves practical and scalable for pan-genome indexing and querying. We provide a C++ implementation of b-move, supporting efficient lossless approximate pattern matching including locate functionality, available at https://github.com/biointec/b-move under the AGPL-3.0 license.
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Lore Depuydt, Luca Renders, Simon Van de Vyver, Lennart Veys, Travis Gagie, and Jan Fostier. b-move: Faster Bidirectional Character Extensions in a Run-Length Compressed Index. In 24th International Workshop on Algorithms in Bioinformatics (WABI 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 312, pp. 10:1-10:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{depuydt_et_al:LIPIcs.WABI.2024.10,
  author =	{Depuydt, Lore and Renders, Luca and Van de Vyver, Simon and Veys, Lennart and Gagie, Travis and Fostier, Jan},
  title =	{{b-move: Faster Bidirectional Character Extensions in a Run-Length Compressed Index}},
  booktitle =	{24th International Workshop on Algorithms in Bioinformatics (WABI 2024)},
  pages =	{10:1--10:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-340-9},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{312},
  editor =	{Pissis, Solon P. and Sung, Wing-Kin},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.WABI.2024.10},
  URN =		{urn:nbn:de:0030-drops-206546},
  doi =		{10.4230/LIPIcs.WABI.2024.10},
  annote =	{Keywords: Pan-genomics, FM-index, r-index, Move Structure, Bidirectional Search, Approximate Pattern Matching, Lossless Alignment, Cache Efficiency}
}
Document
DOI: 10.4230/LIPIcs.SEA.2024.1
Move-r: Optimizing the r-index

Authors: Nico Bertram, Johannes Fischer, and Lukas Nalbach

Published in: LIPIcs, Volume 301, 22nd International Symposium on Experimental Algorithms (SEA 2024)

Abstract
We present a static text index called Move-r, which is a highly optimized version of the r-index ([Travis Gagie et al., 2020] Gagie et al., 2020) that encorporates recent theoretical developments of the move data structure ([Takaaki Nishimoto and Yasuo Tabei, 2021] Nishimoto and Tabei, 2021). The r-index is the method of choice for indexing highly repetitive texts, such as different versions of a text document or DNA from the same species, as it exploits the compressibilty of the underlying data. With Move-r, we can answer count- and locate queries 2-35 (typically 15) times as fast as with any other r-index supporting locate queries while being 0.8-2.5 (typically 2) times as large. A Move-r index can be constructed 0.9-2 (typically 2) times as fast while using 1/3-1 (typically 1/2) times as much space.
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Nico Bertram, Johannes Fischer, and Lukas Nalbach. Move-r: Optimizing the r-index. In 22nd International Symposium on Experimental Algorithms (SEA 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 301, pp. 1:1-1:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{bertram_et_al:LIPIcs.SEA.2024.1,
  author =	{Bertram, Nico and Fischer, Johannes and Nalbach, Lukas},
  title =	{{Move-r: Optimizing the r-index}},
  booktitle =	{22nd International Symposium on Experimental Algorithms (SEA 2024)},
  pages =	{1:1--1:19},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-325-6},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{301},
  editor =	{Liberti, Leo},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SEA.2024.1},
  URN =		{urn:nbn:de:0030-drops-203662},
  doi =		{10.4230/LIPIcs.SEA.2024.1},
  annote =	{Keywords: Compressed Text Index, Burrows-Wheeler Transform}
}
Document
DOI: 10.4230/LIPIcs.SEA.2024.10
Taxonomic Classification with Maximal Exact Matches in KATKA Kernels and Minimizer Digests

Authors: Dominika Draesslerová, Omar Ahmed, Travis Gagie, Jan Holub, Ben Langmead, Giovanni Manzini, and Gonzalo Navarro

Published in: LIPIcs, Volume 301, 22nd International Symposium on Experimental Algorithms (SEA 2024)

Abstract
For taxonomic classification, we are asked to index the genomes in a phylogenetic tree such that later, given a DNA read, we can quickly choose a small subtree likely to contain the genome from which that read was drawn. Although popular classifiers such as Kraken use k-mers, recent research indicates that using maximal exact matches (MEMs) can lead to better classifications. For example, we can - build an augmented FM-index over the the genomes in the tree concatenated in left-to-right order; - for each MEM in a read, find the interval in the suffix array containing the starting positions of that MEM’s occurrences in those genomes; - find the minimum and maximum values stored in that interval; - take the lowest common ancestor (LCA) of the genomes containing the characters at those positions. This solution is practical, however, only when the total size of the genomes in the tree is fairly small. In this paper we consider applying the same solution to three lossily compressed representations of the genomes' concatenation: - a KATKA kernel, which discards characters that are not in the first or last occurrence of any k_max-tuple, for a parameter k_max; - a minimizer digest; - a KATKA kernel of a minimizer digest. With a test dataset and these three representations of it, simulated reads and various parameter settings, we checked how many reads' longest MEMs occurred only in the sequences from which those reads were generated ("true positive" reads). For some parameter settings we achieved significant compression while only slightly decreasing the true-positive rate.
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Dominika Draesslerová, Omar Ahmed, Travis Gagie, Jan Holub, Ben Langmead, Giovanni Manzini, and Gonzalo Navarro. Taxonomic Classification with Maximal Exact Matches in KATKA Kernels and Minimizer Digests. In 22nd International Symposium on Experimental Algorithms (SEA 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 301, pp. 10:1-10:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{draesslerova_et_al:LIPIcs.SEA.2024.10,
  author =	{Draesslerov\'{a}, Dominika and Ahmed, Omar and Gagie, Travis and Holub, Jan and Langmead, Ben and Manzini, Giovanni and Navarro, Gonzalo},
  title =	{{Taxonomic Classification with Maximal Exact Matches in KATKA Kernels and Minimizer Digests}},
  booktitle =	{22nd International Symposium on Experimental Algorithms (SEA 2024)},
  pages =	{10:1--10:13},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-325-6},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{301},
  editor =	{Liberti, Leo},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SEA.2024.10},
  URN =		{urn:nbn:de:0030-drops-203756},
  doi =		{10.4230/LIPIcs.SEA.2024.10},
  annote =	{Keywords: Taxonomic classification, metagenomics, KATKA, maximal exact matches, string kernels, minimizer digests}
}
Document
DOI: 10.4230/LIPIcs.CPM.2024.12
Solving the Minimal Positional Substring Cover Problem in Sublinear Space

Authors: Paola Bonizzoni, Christina Boucher, Davide Cozzi, Travis Gagie, and Yuri Pirola

Published in: LIPIcs, Volume 296, 35th Annual Symposium on Combinatorial Pattern Matching (CPM 2024)

Abstract
Within the field of haplotype analysis, the Positional Burrows-Wheeler Transform (PBWT) stands out as a key innovation, addressing numerous challenges in genomics. For example, Sanaullah et al. introduced a PBWT-based method that addresses the haplotype threading problem, which involves representing a query haplotype through a minimal set of substrings. To solve this problem using the PBWT data structure, they formulate the Minimal Positional Substring Cover (MPSC) problem, and then, subsequently present a solution for it. Additionally, they present and solve several variants of this problem: k-MPSC, leftmost MPSC, rightmost MPSC, and length-maximal MPSC. Yet, a full PBWT is required for each of their solutions, which yields a significant memory usage requirement. Here, we take advantage of the latest results on run-length encoding the PBWT, to solve the MPSC in a sublinear amount of space. Our methods involve demonstrating that k-Set Maximal Exact Matches (k-SMEMs) can be computed in a sublinear amount of space via efficient computation of k-Matching Statistics (k-MS). This leads to a solution that requires sublinear space for, not only the MPSC problem, but for all its variations proposed by Sanaullah et al. Most importantly, we present experimental results on haplotype panels from the 1000 Genomes Project data that show the utility of these theoretical results. We conclusively demonstrate that our approach markedly decreases the memory required to solve the MPSC problem, achieving a reduction of at least two orders of magnitude compared to the method proposed by Sanaullah et al. This efficiency allows us to solve the problem on large versions of the problem, where other methods are unable to scale to. In summary, the creation of {μ}-PBWT paves the way for new possibilities in conducting in-depth genetic research and analysis on a large scale. All source code is publicly available at https://github.com/dlcgold/muPBWT/tree/k-smem.
Cite as

Paola Bonizzoni, Christina Boucher, Davide Cozzi, Travis Gagie, and Yuri Pirola. Solving the Minimal Positional Substring Cover Problem in Sublinear Space. In 35th Annual Symposium on Combinatorial Pattern Matching (CPM 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 296, pp. 12:1-12:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{bonizzoni_et_al:LIPIcs.CPM.2024.12,
  author =	{Bonizzoni, Paola and Boucher, Christina and Cozzi, Davide and Gagie, Travis and Pirola, Yuri},
  title =	{{Solving the Minimal Positional Substring Cover Problem in Sublinear Space}},
  booktitle =	{35th Annual Symposium on Combinatorial Pattern Matching (CPM 2024)},
  pages =	{12:1--12:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-326-3},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{296},
  editor =	{Inenaga, Shunsuke and Puglisi, Simon J.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CPM.2024.12},
  URN =		{urn:nbn:de:0030-drops-201225},
  doi =		{10.4230/LIPIcs.CPM.2024.12},
  annote =	{Keywords: Positional Burrows-Wheeler Transform, r-index, minimal position substring cover, set-maximal exact matches}
}
Document
DOI: 10.4230/LIPIcs.WABI.2023.13
Acceleration of FM-Index Queries Through Prefix-Free Parsing

Authors: Aaron Hong, Marco Oliva, Dominik Köppl, Hideo Bannai, Christina Boucher, and Travis Gagie

Published in: LIPIcs, Volume 273, 23rd International Workshop on Algorithms in Bioinformatics (WABI 2023)

Abstract
FM-indexes are a crucial data structure in DNA alignment, but searching with them usually takes at least one random access per character in the query pattern. Ferragina and Fischer [Ferragina and Fischer, 2007] observed in 2007 that word-based indexes often use fewer random accesses than character-based indexes, and thus support faster searches. Since DNA lacks natural word-boundaries, however, it is necessary to parse it somehow before applying word-based FM-indexing. Last year, Deng et al. [Deng et al., 2022] proposed parsing genomic data by induced suffix sorting, and showed the resulting word-based FM-indexes support faster counting queries than standard FM-indexes when patterns are a few thousand characters or longer. In this paper we show that using prefix-free parsing - which takes parameters that let us tune the average length of the phrases - instead of induced suffix sorting, gives a significant speedup for patterns of only a few hundred characters. We implement our method and demonstrate it is between 3 and 18 times faster than competing methods on queries to GRCh38. And was consistently faster on queries made to 25,000, 50,000 and 100,000 SARS-CoV-2 genomes. Hence, it is very clear that our method accelerates the performance of count over all state-of-the-art methods with a minor increase in the memory.
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Aaron Hong, Marco Oliva, Dominik Köppl, Hideo Bannai, Christina Boucher, and Travis Gagie. Acceleration of FM-Index Queries Through Prefix-Free Parsing. In 23rd International Workshop on Algorithms in Bioinformatics (WABI 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 273, pp. 13:1-13:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{hong_et_al:LIPIcs.WABI.2023.13,
  author =	{Hong, Aaron and Oliva, Marco and K\"{o}ppl, Dominik and Bannai, Hideo and Boucher, Christina and Gagie, Travis},
  title =	{{Acceleration of FM-Index Queries Through Prefix-Free Parsing}},
  booktitle =	{23rd International Workshop on Algorithms in Bioinformatics (WABI 2023)},
  pages =	{13:1--13:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-294-5},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{273},
  editor =	{Belazzougui, Djamal and Ouangraoua, A\"{i}da},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.WABI.2023.13},
  URN =		{urn:nbn:de:0030-drops-186390},
  doi =		{10.4230/LIPIcs.WABI.2023.13},
  annote =	{Keywords: FM-index, pangenomics, scalability, word-based indexing, random access}
}
Document
DOI: 10.4230/LIPIcs.CPM.2023.22
Merging Sorted Lists of Similar Strings

Authors: Gene Myers

Published in: LIPIcs, Volume 259, 34th Annual Symposium on Combinatorial Pattern Matching (CPM 2023)

Abstract
Merging T sorted, non-redundant lists containing M elements into a single sorted, non-redundant result of size N ≥ M/T is a classic problem typically solved practically in O(M log T) time with a priority-queue data structure the most basic of which is the simple heap. We revisit this problem in the situation where the list elements are strings and the lists contain many identical or nearly identical elements. By keeping simple auxiliary information with each heap node, we devise an O(M log T+S) worst-case method that performs no more character comparisons than the sum of the lengths of all the strings S, and another O(M log (T/e¯)+S) method that becomes progressively more efficient as a function of the fraction of equal elements e¯ = M/N between input lists, reaching linear time when the lists are all identical. The methods perform favorably in practice versus an alternate formulation based on a trie.
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Gene Myers. Merging Sorted Lists of Similar Strings. In 34th Annual Symposium on Combinatorial Pattern Matching (CPM 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 259, pp. 22:1-22:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{myers:LIPIcs.CPM.2023.22,
  author =	{Myers, Gene},
  title =	{{Merging Sorted Lists of Similar Strings}},
  booktitle =	{34th Annual Symposium on Combinatorial Pattern Matching (CPM 2023)},
  pages =	{22:1--22:15},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-276-1},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{259},
  editor =	{Bulteau, Laurent and Lipt\'{a}k, Zsuzsanna},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CPM.2023.22},
  URN =		{urn:nbn:de:0030-drops-179763},
  doi =		{10.4230/LIPIcs.CPM.2023.22},
  annote =	{Keywords: heap, trie, longest common prefix}
}
Document
DOI: 10.4230/LIPIcs.CPM.2023.26
MONI Can Find k-MEMs

Authors: Igor Tatarnikov, Ardavan Shahrabi Farahani, Sana Kashgouli, and Travis Gagie

Published in: LIPIcs, Volume 259, 34th Annual Symposium on Combinatorial Pattern Matching (CPM 2023)

Abstract
Suppose we are asked to index a text T [0..n - 1] such that, given a pattern P [0..m - 1], we can quickly report the maximal substrings of P that each occur in T at least k times. We first show how we can add O (r log n) bits to Rossi et al.’s recent MONI index, where r is the number of runs in the Burrows-Wheeler Transform of T, such that it supports such queries in O (k m log n) time. We then show how, if we are given k at construction time, we can reduce the query time to O (m log n).
Cite as

Igor Tatarnikov, Ardavan Shahrabi Farahani, Sana Kashgouli, and Travis Gagie. MONI Can Find k-MEMs. In 34th Annual Symposium on Combinatorial Pattern Matching (CPM 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 259, pp. 26:1-26:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{tatarnikov_et_al:LIPIcs.CPM.2023.26,
  author =	{Tatarnikov, Igor and Shahrabi Farahani, Ardavan and Kashgouli, Sana and Gagie, Travis},
  title =	{{MONI Can Find k-MEMs}},
  booktitle =	{34th Annual Symposium on Combinatorial Pattern Matching (CPM 2023)},
  pages =	{26:1--26:14},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-276-1},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{259},
  editor =	{Bulteau, Laurent and Lipt\'{a}k, Zsuzsanna},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CPM.2023.26},
  URN =		{urn:nbn:de:0030-drops-179802},
  doi =		{10.4230/LIPIcs.CPM.2023.26},
  annote =	{Keywords: Compact data structures, Burrows-Wheeler Transform, run-length compression, maximal exact matches}
}
Document
DOI: 10.4230/LIPIcs.ESA.2022.57
Simple Worst-Case Optimal Adaptive Prefix-Free Coding

Authors: Travis Gagie

Published in: LIPIcs, Volume 244, 30th Annual European Symposium on Algorithms (ESA 2022)

Abstract
We give a new and simple worst-case optimal algorithm for adaptive prefix-free coding that matches Gagie and Nekrich’s (2009) bounds except for lower-order terms, and uses no data structures more complicated than a lookup table.
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Travis Gagie. Simple Worst-Case Optimal Adaptive Prefix-Free Coding. In 30th Annual European Symposium on Algorithms (ESA 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 244, pp. 57:1-57:5, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{gagie:LIPIcs.ESA.2022.57,
  author =	{Gagie, Travis},
  title =	{{Simple Worst-Case Optimal Adaptive Prefix-Free Coding}},
  booktitle =	{30th Annual European Symposium on Algorithms (ESA 2022)},
  pages =	{57:1--57:5},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-247-1},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{244},
  editor =	{Chechik, Shiri and Navarro, Gonzalo and Rotenberg, Eva 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.2022.57},
  URN =		{urn:nbn:de:0030-drops-169959},
  doi =		{10.4230/LIPIcs.ESA.2022.57},
  annote =	{Keywords: Adaptive prefix-free coding, Shannon coding, Lookup tables}
}
Document
DOI: 10.4230/LIPIcs.WABI.2022.18
Prefix-Free Parsing for Building Large Tunnelled Wheeler Graphs

Authors: Adrián Goga and Andrej Baláž

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

Abstract
We propose a new technique for creating a space-efficient index for large repetitive text collections, such as pangenomic databases containing sequences of many individuals from the same species. We combine two recent techniques from this area: Wheeler graphs (Gagie et al., 2017) and prefix-free parsing (PFP, Boucher et al., 2019). Wheeler graphs are a general framework encompassing several indexes based on the Burrows-Wheeler transform (BWT), such as the FM-index. Wheeler graphs admit a succinct representation which can be further compacted by employing the idea of tunnelling, which exploits redundancies in the form of parallel, equally-labelled paths called blocks that can be merged into a single path. The problem of finding the optimal set of blocks for tunnelling, i.e. the one that minimizes the size of the resulting Wheeler graph, is known to be NP-complete and remains the most computationally challenging part of the tunnelling process. To find an adequate set of blocks in less time, we propose a new method based on the prefix-free parsing (PFP). The idea of PFP is to divide the input text into phrases of roughly equal sizes that overlap by a fixed number of characters. The phrases are then sorted lexicographically. The original text is represented by a sequence of phrase ranks (the parse) and a list of all used phrases (the dictionary). In repetitive texts, the PFP representation of the text is generally much shorter than the original since individual phrases are used many times in the parse, thus reducing the size of the dictionary. To speed up the block selection for tunnelling, we apply the PFP to obtain the parse and the dictionary of the original text, tunnel the Wheeler graph of the parse using existing heuristics and subsequently use this tunnelled parse to construct a compact Wheeler graph of the original text. Compared with constructing a Wheeler graph from the original text without PFP, our method is much faster and uses less memory on collections of pangenomic sequences. Therefore, our method enables the use of Wheeler graphs as a pangenomic reference for real-world pangenomic datasets.
Cite as

Adrián Goga and Andrej Baláž. Prefix-Free Parsing for Building Large Tunnelled Wheeler Graphs. In 22nd International Workshop on Algorithms in Bioinformatics (WABI 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 242, pp. 18:1-18:12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{goga_et_al:LIPIcs.WABI.2022.18,
  author =	{Goga, Adri\'{a}n and Bal\'{a}\v{z}, Andrej},
  title =	{{Prefix-Free Parsing for Building Large Tunnelled Wheeler Graphs}},
  booktitle =	{22nd International Workshop on Algorithms in Bioinformatics (WABI 2022)},
  pages =	{18:1--18:12},
  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/entities/document/10.4230/LIPIcs.WABI.2022.18},
  URN =		{urn:nbn:de:0030-drops-170529},
  doi =		{10.4230/LIPIcs.WABI.2022.18},
  annote =	{Keywords: Wheeler graphs, BWT tunnelling, prefix-free parsing, pangenomic graphs}
}
Document
DOI: 10.4230/LIPIcs.WABI.2022.19
Pangenomic Genotyping with the Marker Array

Authors: Taher Mun, Naga Sai Kavya Vaddadi, and Ben Langmead

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

Abstract
We present a new method and software tool called rowbowt that applies a pangenome index to the problem of inferring genotypes from short-read sequencing data. The method uses a novel indexing structure called the marker array. Using the marker array, we can genotype variants with respect from large panels like the 1000 Genomes Project while avoiding the reference bias that results when aligning to a single linear reference. rowbowt can infer accurate genotypes in less time and memory compared to existing graph-based methods.
Cite as

Taher Mun, Naga Sai Kavya Vaddadi, and Ben Langmead. Pangenomic Genotyping with the Marker Array. In 22nd International Workshop on Algorithms in Bioinformatics (WABI 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 242, pp. 19:1-19:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{mun_et_al:LIPIcs.WABI.2022.19,
  author =	{Mun, Taher and Vaddadi, Naga Sai Kavya and Langmead, Ben},
  title =	{{Pangenomic Genotyping with the Marker Array}},
  booktitle =	{22nd International Workshop on Algorithms in Bioinformatics (WABI 2022)},
  pages =	{19:1--19:17},
  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/entities/document/10.4230/LIPIcs.WABI.2022.19},
  URN =		{urn:nbn:de:0030-drops-170530},
  doi =		{10.4230/LIPIcs.WABI.2022.19},
  annote =	{Keywords: Sequence alignment indexing genotyping}
}
Document
DOI: 10.4230/LIPIcs.SEA.2022.16
RLBWT Tricks

Authors: Nathaniel K. Brown, Travis Gagie, and Massimiliano Rossi

Published in: LIPIcs, Volume 233, 20th International Symposium on Experimental Algorithms (SEA 2022)

Abstract
Until recently, most experts would probably have agreed we cannot backwards-step in constant time with a run-length compressed Burrows-Wheeler Transform (RLBWT), since doing so relies on rank queries on sparse bitvectors and those inherit lower bounds from predecessor queries. At ICALP '21, however, Nishimoto and Tabei described a new, simple and constant-time implementation. For a permutation π, it stores an O (r)-space table - where r is the number of positions i where either i = 0 or π (i + 1) ≠ π (i) + 1 - that enables the computation of successive values of π(i) by table look-ups and linear scans. Nishimoto and Tabei showed how to increase the number of rows in the table to bound the length of the linear scans such that the query time for computing π(i) is constant while maintaining O (r)-space. In this paper we refine Nishimoto and Tabei’s approach, including a time-space tradeoff, and experimentally evaluate different implementations demonstrating the practicality of part of their result. We show that even without adding rows to the table, in practice we almost always scan only a few entries during queries. We propose a decomposition scheme of the permutation π corresponding to the LF-mapping that allows an improved compression of the data structure, while limiting the query time. We tested our implementation on real-world genomic datasets and found that without compression of the table, backward-stepping is drastically faster than with sparse bitvector implementations but, unfortunately, also uses drastically more space. After compression, backward-stepping is competitive both in time and space with the best existing implementations.
Cite as

Nathaniel K. Brown, Travis Gagie, and Massimiliano Rossi. RLBWT Tricks. In 20th International Symposium on Experimental Algorithms (SEA 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 233, pp. 16:1-16:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{brown_et_al:LIPIcs.SEA.2022.16,
  author =	{Brown, Nathaniel K. and Gagie, Travis and Rossi, Massimiliano},
  title =	{{RLBWT Tricks}},
  booktitle =	{20th International Symposium on Experimental Algorithms (SEA 2022)},
  pages =	{16:1--16:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-251-8},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{233},
  editor =	{Schulz, Christian and U\c{c}ar, Bora},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SEA.2022.16},
  URN =		{urn:nbn:de:0030-drops-165500},
  doi =		{10.4230/LIPIcs.SEA.2022.16},
  annote =	{Keywords: Compressed String Indexes, Repetitive Text Collections, Burrows-Wheeler Transform}
}
Document
DOI: 10.4230/LIPIcs.SEA.2022.22
Computing Maximal Unique Matches with the r-Index

Authors: Sara Giuliani, Giuseppe Romana, and Massimiliano Rossi

Published in: LIPIcs, Volume 233, 20th International Symposium on Experimental Algorithms (SEA 2022)

Abstract
In recent years, pangenomes received increasing attention from the scientific community for their ability to incorporate population variation information and alleviate reference genome bias. Maximal Exact Matches (MEMs) and Maximal Unique Matches (MUMs) have proven themselves to be useful in multiple bioinformatic contexts, for example short-read alignment and multiple-genome alignment. However, standard techniques using suffix trees and FM-indexes do not scale to a pangenomic level. Recently, Gagie et al. [JACM 20] introduced the r-index that is a Burrows-Wheeler Transform (BWT)-based index able to handle hundreds of human genomes. Later, Rossi et al. [JCB 22] enabled the computation of MEMs using the r-index, and Boucher et al. [DCC 21] showed how to compute them in a streaming fashion. In this paper, we show how to augment Boucher et al.’s approach to enable the computation of MUMs on the r-index, while preserving the space and time bounds. We add additional O(r) samples of the longest common prefix (LCP) array, where r is the number of equal-letter runs of the BWT, that permits the computation of the second longest match of the pattern suffix with respect to the input text, which in turn allows the computation of candidate MUMs. We implemented a proof-of-concept of our approach, that we call MUM-PHINDER, and tested on real-world datasets. We compared our approach with competing methods that are able to compute MUMs. We observe that our method is up to 8 times smaller, while up to 19 times slower when the dataset is not highly repetitive, while on highly repetitive data, our method is up to 6.5 times slower and uses up to 25 times less memory.
Cite as

Sara Giuliani, Giuseppe Romana, and Massimiliano Rossi. Computing Maximal Unique Matches with the r-Index. In 20th International Symposium on Experimental Algorithms (SEA 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 233, pp. 22:1-22:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{giuliani_et_al:LIPIcs.SEA.2022.22,
  author =	{Giuliani, Sara and Romana, Giuseppe and Rossi, Massimiliano},
  title =	{{Computing Maximal Unique Matches with the r-Index}},
  booktitle =	{20th International Symposium on Experimental Algorithms (SEA 2022)},
  pages =	{22:1--22:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-251-8},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{233},
  editor =	{Schulz, Christian and U\c{c}ar, Bora},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SEA.2022.22},
  URN =		{urn:nbn:de:0030-drops-165568},
  doi =		{10.4230/LIPIcs.SEA.2022.22},
  annote =	{Keywords: Burrows-Wheeler Transform, r-index, maximal unique matches, bioinformatics, pangenomics}
}
Document
Invited Talk
DOI: 10.4230/LIPIcs.CPM.2022.3
Compact Text Indexing for Advanced Pattern Matching Problems: Parameterized, Order-Isomorphic, 2D, etc. (Invited Talk)

Authors: Sharma V. Thankachan

Published in: LIPIcs, Volume 223, 33rd Annual Symposium on Combinatorial Pattern Matching (CPM 2022)

Abstract
In the past two decades, we have witnessed the design of various compact data structures for pattern matching over an indexed text [Navarro, 2016]. Popular indexes like the FM-index [Paolo Ferragina and Giovanni Manzini, 2005], compressed suffix arrays/trees [Roberto Grossi and Jeffrey Scott Vitter, 2005; Kunihiko Sadakane, 2007], the recent r-index [Travis Gagie et al., 2020; Takaaki Nishimoto and Yasuo Tabei, 2021], etc., capture the key functionalities of classic suffix arrays/trees [Udi Manber and Eugene W. Myers, 1993; Peter Weiner, 1973] in compact space. Mostly, they rely on the Burrows-Wheeler Transform (BWT) and its associated operations [Burrows and Wheeler, 1994]. However, compactly encoding some advanced suffix tree (ST) variants, like parameterized ST [Brenda S. Baker, 1993; S. Rao Kosaraju, 1995; Juan Mendivelso et al., 2020], order-isomorphic/preserving ST [Maxime Crochemore et al., 2016], two-dimensional ST [Raffaele Giancarlo, 1995; Dong Kyue Kim et al., 1998], etc. [Sung Gwan Park et al., 2019; Tetsuo Shibuya, 2000]- collectively known as suffix trees with missing suffix links [Richard Cole and Ramesh Hariharan, 2003], has been challenging. The previous techniques are not easily extendable because these variants do not hold some structural properties of the standard ST that enable compression. However, some limited progress has been made in these directions recently [Arnab Ganguly et al., 2017; Travis Gagie et al., 2017; Gianni Decaroli et al., 2017; Dhrumil Patel and Rahul Shah, 2021; Arnab Ganguly et al., 2021; Sung{-}Hwan Kim and Hwan{-}Gue Cho, 2021; Sung{-}Hwan Kim and Hwan{-}Gue Cho, 2021; Arnab Ganguly et al., 2017; Arnab Ganguly et al., 2022; Arnab Ganguly et al., 2021]. This talk will briefly survey them and highlight some interesting open problems.
Cite as

Sharma V. Thankachan. Compact Text Indexing for Advanced Pattern Matching Problems: Parameterized, Order-Isomorphic, 2D, etc. (Invited Talk). In 33rd Annual Symposium on Combinatorial Pattern Matching (CPM 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 223, pp. 3:1-3:3, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{thankachan:LIPIcs.CPM.2022.3,
  author =	{Thankachan, Sharma V.},
  title =	{{Compact Text Indexing for Advanced Pattern Matching Problems: Parameterized, Order-Isomorphic, 2D, etc.}},
  booktitle =	{33rd Annual Symposium on Combinatorial Pattern Matching (CPM 2022)},
  pages =	{3:1--3:3},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-234-1},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{223},
  editor =	{Bannai, Hideo and Holub, Jan},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CPM.2022.3},
  URN =		{urn:nbn:de:0030-drops-161300},
  doi =		{10.4230/LIPIcs.CPM.2022.3},
  annote =	{Keywords: Text Indexing, Suffix Trees, String Matching}
}
Document
DOI: 10.4230/LIPIcs.WABI.2021.13
Compressing and Indexing Aligned Readsets

Authors: Travis Gagie, Garance Gourdel, and Giovanni Manzini

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

Abstract
Compressed full-text indexes are one of the main success stories of bioinformatics data structures but even they struggle to handle some DNA readsets. This may seem surprising since, at least when dealing with short reads from the same individual, the readset will be highly repetitive and, thus, highly compressible. If we are not careful, however, this advantage can be more than offset by two disadvantages: first, since most base pairs are included in at least tens reads each, the uncompressed readset is likely to be at least an order of magnitude larger than the individual’s uncompressed genome; second, these indexes usually pay some space overhead for each string they store, and the total overhead can be substantial when dealing with millions of reads. The most successful compressed full-text indexes for readsets so far are based on the Extended Burrows-Wheeler Transform (EBWT) and use a sorting heuristic to try to reduce the space overhead per read, but they still treat the reads as separate strings and thus may not take full advantage of the readset’s structure. For example, if we have already assembled an individual’s genome from the readset, then we can usually use it to compress the readset well: e.g., we store the gap-coded list of reads' starting positions; we store the list of their lengths, which is often highly compressible; and we store information about the sequencing errors, which are rare with short reads. There is nowhere, however, where we can plug an assembled genome into the EBWT. In this paper we show how to use one or more assembled or partially assembled genome as the basis for a compressed full-text index of its readset. Specifically, we build a labelled tree by taking the assembled genome as a trunk and grafting onto it the reads that align to it, at the starting positions of their alignments. Next, we compute the eXtended Burrows-Wheeler Transform (XBWT) of the resulting labelled tree and build a compressed full-text index on that. Although this index can occasionally return false positives, it is usually much more compact than the alternatives. Following the established practice for datasets with many repetitions, we compare different full-text indices by looking at the number of runs in the transformed strings. For a human Chr19 readset our preliminary experiments show that eliminating separators characters from the EBWT reduces the number of runs by 19%, from 220 million to 178 million, and using the XBWT reduces it by a further 15%, to 150 million.
Cite as

Travis Gagie, Garance Gourdel, and Giovanni Manzini. Compressing and Indexing Aligned Readsets. In 21st International Workshop on Algorithms in Bioinformatics (WABI 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 201, pp. 13:1-13:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{gagie_et_al:LIPIcs.WABI.2021.13,
  author =	{Gagie, Travis and Gourdel, Garance and Manzini, Giovanni},
  title =	{{Compressing and Indexing Aligned Readsets}},
  booktitle =	{21st International Workshop on Algorithms in Bioinformatics (WABI 2021)},
  pages =	{13:1--13:21},
  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.dagstuhl.de/entities/document/10.4230/LIPIcs.WABI.2021.13},
  URN =		{urn:nbn:de:0030-drops-143660},
  doi =		{10.4230/LIPIcs.WABI.2021.13},
  annote =	{Keywords: data compression, compact data structures, FM-index, Burrows-Wheeler Transform, EBWT, XBWT, DNA reads}
}
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