Efficient Solutions to Biological Problems Using de Bruijn Graphs (Invited Talk)

Author Leena Salmela

Thumbnail PDF


  • Filesize: 360 kB
  • 2 pages

Document Identifiers

Author Details

Leena Salmela
  • University of Helsinki, Finland

Cite AsGet BibTex

Leena Salmela. Efficient Solutions to Biological Problems Using de Bruijn Graphs (Invited Talk). In 22nd International Workshop on Algorithms in Bioinformatics (WABI 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 242, pp. 1:1-1:2, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)


The de Bruijn graph has become a standard method in the analysis of sequencing reads in computational biology due to its ability to represent the information contained in large read sets in small space. A de Bruijn graph represents a set of sequencing reads by its k-mers, i.e. the set of substrings of length k that occur in the reads. In the classical definition, the k-mers are the edges of the graph and the nodes are the k-1 bases long prefixes and suffixes of the k-mers. Usually only k-mers occurring several times in the read set are kept to filter out noise in the data. De Bruijn graphs have been used to solve many problems in computational biology including genome assembly [Ramana M. Idury and Michael S. Waterman, 1995; Pavel A. Pevzner et al., 2001; Anton Bankevich et al., 2012; Yu Peng et al., 2010], sequencing error correction [Leena Salmela and Eric Rivals, 2014; Giles Miclotte et al., 2016; Leena Salmela et al., 2017; Limasset et al., 2019], reference free variant calling [Raluca Uricaru et al., 2015], indexing read sets [Camille Marchet et al., 2021], and so on. Next I will discuss two of these problems in more depth. The de Bruijn graph first emerged in computation biology in the context of genome assembly [Ramana M. Idury and Michael S. Waterman, 1995; Pavel A. Pevzner et al., 2001] where the task is to reconstruct a genome based on sequencing reads. As the de Bruijn graph can represent large read sets compactly, it became the standard approach to assemble short reads [Anton Bankevich et al., 2012; Yu Peng et al., 2010]. In the theoretical framework of de Bruijn graph based genome assembly, a genome is thought to be the Eulerian path in the de Bruijn graph built on the sequencing reads. In practise, the Eulerian path is not unique and thus not useful in the biological context. Therefore, practical implementations report subpaths that are guaranteed to be part of any Eulerian path and thus part of the actual genome. Such models include unitigs, which are nonbranching paths of the de Bruijn graph, and more involved definitions such as omnitigs [Alexandru I. Tomescu and Paul Medvedev, 2017]. In genome assembly the choice of k is a crucial matter. A small k can result in a tangled graph, whereas a too large k will fragment the graph. Furthermore, a different value of k may be optimal for different parts of the genome. Variable order de Bruijn graphs [Christina Boucher et al., 2015; Djamal Belazzougui et al., 2016], which represent de Bruijn graphs of all orders k in a single data structure, have been proposed as a solution but no rigorous definition corresponding to unitigs has been presented. We give the first definition of assembled sequences, i.e. contigs, on such graphs and an algorithm for enumerating them. Another problem that can be solved with de Bruijn graphs is the correction of sequencing errors [Leena Salmela and Eric Rivals, 2014; Giles Miclotte et al., 2016; Leena Salmela et al., 2017; Limasset et al., 2019]. Because each position of a genome is sequenced several times, it is possible to correct sequencing errors in reads if we can identify data originating from the same genomic region. A de Bruijn graph can be used to represent compactly the reliable information and the individual reads can be corrected by aligning them to the graph.

Subject Classification

ACM Subject Classification
  • Theory of computation → Design and analysis of algorithms
  • Applied computing → Sequencing and genotyping technologies
  • de Bruijn graph
  • variable order de Bruijn graph
  • genome assembly
  • sequencing error correction
  • k-mers


  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    PDF Downloads


  1. Anton Bankevich, Sergey Nurk, Dmitry Antipov, Alexey A. Gurevich, Mikhail Dvorkin, Alexander S. Kulikov, Valery M. Lesin, Sergey I. Nikolenko, Son Pham, Andrey D. Prjibelski, Alexey V. Pyshkin, Alexander V. Sirotkin, Nikolay Vyahhi, Glenn Tesler, Max A. Alekseyev, and Pavel A. Pevzner. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. Journal of Computational Biology, 19(5):455-477, 2012. Google Scholar
  2. Djamal Belazzougui, Travis Gagie, Veli Mäkinen, Marco Previtali, and Simon J. Puglisi. Bidirectional variable-order de Bruijn graphs. In Proceedings of LATIN 2016, pages 164-178. Springer, 2016. Google Scholar
  3. Christina Boucher, Alex Bowe, Travis Gagie, Simon J. Puglisi, and Kunihiko Sadakane. Variable-order de Bruijn graphs. In Proceedgins of Data Compression Conference 2015, pages 383-392, 2015. Google Scholar
  4. Ramana M. Idury and Michael S. Waterman. A new algorithm for DNA sequence assembly. Journal of Computational Biology, 2(2):291-306, 1995. Google Scholar
  5. Antoine Limasset, Jean-François Flot, and Pierre Peterlongo. Toward perfect reads: self-correction of short reads via mapping on de Bruijn graphs. Bioinformatics, 36(5):1374-1381, 2019. Google Scholar
  6. Camille Marchet, Christina Boucher, Simon J. Puglisi, Paul Medvedev, Mikaël Salson, and Rayan Chikhi. Data structures based on k-mers for querying large collections of sequencing data sets. Genome Research, 31:1-12, 2021. Google Scholar
  7. Giles Miclotte, Mahdi Heydari, Piet Demeester, Stephane Rombauts, Yves Van de Peer, Pieter Audenaert, and Jan Fostier. Jabba: hybrid error correction for long sequencing reads. Algorithms for Molecular Biology, 11(10), 2016. Google Scholar
  8. Yu Peng, Henry C. M. Leung, S. M. Yiu, and Francis Y. L. Chin. IDBA - A practical iterative de Bruijn graph de novo assembler. In Proceedings of RECOMB 2010, volume 6044 of LNBI, pages 426-440. Springer, 2010. Google Scholar
  9. Pavel A. Pevzner, Haixu Tang, and Michael S. Waterman. An Eulerian path approach to DNA fragment assembly. Proceedings of the National Acadademy of Sciences, 98(17):9748-9753, 2001. Google Scholar
  10. Leena Salmela and Eric Rivals. LoRDEC: accurate and efficient long read error correction. Bioinformatics, 30(24):3506-3514, 2014. Google Scholar
  11. Leena Salmela, Riku Walve, Eric Rivals, and Esko Ukkonen. Accurate selfcorrection of errors in long reads using de Bruijn graphs. Bioinformatics, 33(6):799-806, 2017. (Also in RECOMB-seq 2016). Google Scholar
  12. Alexandru I. Tomescu and Paul Medvedev. Safe and complete contig assembly through omnitigs. Journal of Computational Biology, 24(6):590-602, 2017. Google Scholar
  13. Raluca Uricaru, Guillaume Rizk, Vincent Lacroix, Elsa Quillery, Olivier Plantard, Rayan Chikhi, Claire Lemaitre, and Pierre Peterlongo. Reference-free detection of isolated SNPs. Nucleic Acids Research, 43(2):e11, 2015. Google Scholar