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Documents authored by Zentgraf, Jens

Document
DOI: 10.4230/LIPIcs.WABI.2024.15
Swiftly Identifying Strongly Unique k-Mers

Authors: Jens Zentgraf and Sven Rahmann

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

Abstract
Motivation. Short DNA sequences of length k that appear in a single location (e.g., at a single genomic position, in a single species from a larger set of species, etc.) are called unique k-mers. They are useful for placing sequenced DNA fragments at the correct location without computing alignments and without ambiguity. However, they are not necessarily robust: A single basepair change may turn a unique k-mer into a different one that may in fact be present at one or more different locations, which may give confusing or contradictory information when attempting to place a read by its k-mer content. A more robust concept are strongly unique k-mers, i.e., unique k-mers for which no Hamming-distance-1 neighbor with conflicting information exists in all of the considered sequences. Given a set of k-mers, it is therefore of interest to have an efficient method that can distinguish k-mers with a Hamming-distance-1 neighbor in the collection from those that do not. Results. We present engineered algorithms to identify and mark within a set K of (canonical) k-mers all elements that have a Hamming-distance-1 neighbor in the same set. One algorithm is based on recursively running a 4-way comparison on sub-intervals of the sorted set. The other algorithm is based on bucketing and running a pairwise bit-parallel Hamming distance test on small buckets of the sorted set. Both methods consider canonical k-mers (i.e., taking reverse complements into account) and allow for efficient parallelization. The methods have been implemented and applied in practice to sets consisting of several billions of k-mers. An optimized combined approach running with 16 threads on a 16-core workstation, yields wall-clock running times below 20 seconds on the 2.5 billion distinct 31-mers of the human telomere-to-telomere reference genome. Availability. An implementation can be found at https://gitlab.com/rahmannlab/strong-k-mers.
Cite as

Jens Zentgraf and Sven Rahmann. Swiftly Identifying Strongly Unique k-Mers. In 24th International Workshop on Algorithms in Bioinformatics (WABI 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 312, pp. 15:1-15:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{zentgraf_et_al:LIPIcs.WABI.2024.15,
  author =	{Zentgraf, Jens and Rahmann, Sven},
  title =	{{Swiftly Identifying Strongly Unique k-Mers}},
  booktitle =	{24th International Workshop on Algorithms in Bioinformatics (WABI 2024)},
  pages =	{15:1--15:15},
  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.2024.15},
  URN =		{urn:nbn:de:0030-drops-206593},
  doi =		{10.4230/LIPIcs.WABI.2024.15},
  annote =	{Keywords: k-mer, Hamming distance, strong uniqueness, parallelization, algorithm engineering}
}
Document
DOI: 10.4230/LIPIcs.WABI.2022.12
Fast Gapped k-mer Counting with Subdivided Multi-Way Bucketed Cuckoo Hash Tables

Authors: Jens Zentgraf and Sven Rahmann

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

Abstract
Motivation. In biological sequence analysis, alignment-free (also known as k-mer-based) methods are increasingly replacing mapping- and alignment-based methods for various applications. A basic step of such methods consists of building a table of all k-mers of a given set of sequences (a reference genome or a dataset of sequenced reads) and their counts. Over the past years, efficient methods and tools for k-mer counting have been developed. In a different line of work, the use of gapped k-mers has been shown to offer advantages over the use of the standard contiguous k-mers. However, no tool seems to be available that is able to count gapped k-mers with the same efficiency as contiguous k-mers. One reason is that the most efficient k-mer counters use minimizers (of a length m < k) to group k-mers into buckets, such that many consecutive k-mers are classified into the same bucket. This approach leads to cache-friendly (and hence extremely fast) algorithms, but the approach does not transfer easily to gapped k-mers. Consequently, the existing efficient k-mer counters cannot be trivially modified to count gapped k-mers with the same efficiency. Results. We present a different approach that is equally applicable to contiguous k-mers and gapped k-mers. We use multi-way bucketed Cuckoo hash tables to efficiently store (gapped) k-mers and their counts. We also describe a method to parallelize counting over multiple threads without using locks: We subdivide the hash table into independent subtables, and use a producer-consumer model, such that each thread serves one subtable. This requires designing Cuckoo hash functions with the property that all alternative locations for each k-mer are located in the same subtable. Compared to some of the fastest contiguous k-mer counters, our approach is of comparable speed, or even faster, on large datasets, and it is the only one that supports gapped k-mers.
Cite as

Jens Zentgraf and Sven Rahmann. Fast Gapped k-mer Counting with Subdivided Multi-Way Bucketed Cuckoo Hash Tables. In 22nd International Workshop on Algorithms in Bioinformatics (WABI 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 242, pp. 12:1-12:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{zentgraf_et_al:LIPIcs.WABI.2022.12,
  author =	{Zentgraf, Jens and Rahmann, Sven},
  title =	{{Fast Gapped k-mer Counting with Subdivided Multi-Way Bucketed Cuckoo Hash Tables}},
  booktitle =	{22nd International Workshop on Algorithms in Bioinformatics (WABI 2022)},
  pages =	{12:1--12:20},
  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.2022.12},
  URN =		{urn:nbn:de:0030-drops-170467},
  doi =		{10.4230/LIPIcs.WABI.2022.12},
  annote =	{Keywords: gapped k-mer, k-mer, counting, Cuckoo hashing, parallelization}
}
Document
DOI: 10.4230/LIPIcs.WABI.2020.4
Fast Lightweight Accurate Xenograft Sorting

Authors: Jens Zentgraf and Sven Rahmann

Published in: LIPIcs, Volume 172, 20th International Workshop on Algorithms in Bioinformatics (WABI 2020)

Abstract
Motivation: With an increasing number of patient-derived xenograft (PDX) models being created and subsequently sequenced to study tumor heterogeneity and to guide therapy decisions, there is a similarly increasing need for methods to separate reads originating from the graft (human) tumor and reads originating from the host species' (mouse) surrounding tissue. Two kinds of methods are in use: On the one hand, alignment-based tools require that reads are mapped and aligned (by an external mapper/aligner) to the host and graft genomes separately first; the tool itself then processes the resulting alignments and quality metrics (typically BAM files) to assign each read or read pair. On the other hand, alignment-free tools work directly on the raw read data (typically FASTQ files). Recent studies compare different approaches and tools, with varying results. Results: We show that alignment-free methods for xenograft sorting are superior concerning CPU time usage and equivalent in accuracy. We improve upon the state of the art by presenting a fast lightweight approach based on three-way bucketed quotiented Cuckoo hashing. Our hash table requires memory comparable to an FM index typically used for read alignment and less than other alignment-free approaches. It allows extremely fast lookups and uses less CPU time than other alignment-free methods and alignment-based methods at similar accuracy.
Cite as

Jens Zentgraf and Sven Rahmann. Fast Lightweight Accurate Xenograft Sorting. In 20th International Workshop on Algorithms in Bioinformatics (WABI 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 172, pp. 4:1-4:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@InProceedings{zentgraf_et_al:LIPIcs.WABI.2020.4,
  author =	{Zentgraf, Jens and Rahmann, Sven},
  title =	{{Fast Lightweight Accurate Xenograft Sorting}},
  booktitle =	{20th International Workshop on Algorithms in Bioinformatics (WABI 2020)},
  pages =	{4:1--4:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-161-0},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{172},
  editor =	{Kingsford, Carl and Pisanti, Nadia},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.WABI.2020.4},
  URN =		{urn:nbn:de:0030-drops-127933},
  doi =		{10.4230/LIPIcs.WABI.2020.4},
  annote =	{Keywords: xenograft sorting, alignment-free method, Cuckoo hashing, k-mer}
}
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