Algorithms for Biological Sequencing Data
The team led by University Lecturer Leena Salmela focuses on algorithms for genome assembly.


Genome assembly. Determining the genomic sequence of an organism is a fundamental task in molecular biology. Current sequencing technologies are not able to read the whole genome at once but instead produce sets of short reads, i.e. fragments of the genome, which must then be assembled. We have previously worked on several phases of fragment assembly including sequencing error correction, scaffolding, and gap filling. Together with our biological collaborators we have sequenced and assembled the genome of the Glanville fritillary butterfly which is the first large genome sequenced in Finland. Currently we work on integrating long range data such as genetic linkage maps and optical mapping data to read based genome assembly.

Optical mapping data. Optical maps are produced by immobilising ensembles of DNA molecules on a plate and applying a restriction enzyme to cut the DNA molecules at a specific DNA motif. The molecules are then imaged and the cutting sites can be read from the image thus capturing the relative order and size of fragments between the cut sites. Optical mapping data spans longer genomic regions than sequencing reads and can thus complement read based analysis of genomic data. We have developed algorithms for correcting errors in optical mapping data and to integrate optical mapping data to genome assembly.

De Bruijn graphs. The de Bruijn graph is an important data structure for processing data produced by second generation sequencing machines which produce short but accurate sequencing reads. We have used de Bruijn graphs to develop methods for e.g. sequencing error correction and gap filling. Our current projects include development of de Bruijn graphs suitable for third generation sequencing data.


  • University Lecturer Leena Salmela
  • Postdoctoral Researcher Diego Díaz-Domínguez
  • PhD Student Riku Walve
  • PhD Student Miika Leinonen
  • Research Assistant Essi Tepponen


  • Research Assistant Silvia Nepšinská
  • Taku Onodera (now assistant professor at University of Tokyo)

Recent Publications

  • B. Freire, S. Ladra, J.R. Paramá, and L. Salmela: ViQUF: De novo viral quasispecies reconstruction using unitig-based flow networks. To appear in IEEE/ACM Transactions on Computational Biology and Bioinformatics.
    [Article online] [Implementation]
  • D. Díaz-Domínguez, S.J. Puglisi, and L. Salmela: Computing all-vs-all MEMs in run-length encoded collections of HiFi reads. In Proc. SPIRE 2022, International symposium on String Processing and Information Retrieval (ed. D. Arroyuelo and B. Poblete), Lecture Notes in Computer Science 13617, Springer, 2022, 198-213.
    [Article online]
  • M. Leinonen and L. Salmela: Extraction of long k-mers using spaced seeds. IEEE/ACM Transactions on Computational Biology and Bioinformatics, Volume 19, 2022, 3444–3455.
    [Article online] [Implementation]
  • R. Walve, S.J. Puglisi, and L. Salmela: Space-efficient indexing of spaced seeds for accurate overlap computation of raw optical mapping data. IEEE/ACM Transactions on Computational Biology and Bioinformatics, Volume 19, 2022, 2454-2462.
    [Article online] [Implementation]
  • D. Diaz and G. Navarro: Efficient Construction of the BWT for Repetitive Text Using String Compression. In Proc. CPM 2022, Annual Symposium on Combinatorial Pattern Matching (ed. H. Bannai and J. Holub), Leibniz International Proceedings in Informatics (LIPIcs) 223, Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik, 2022, 29:1-29:18.
    [Article online]
  • R. Walve and L. Salmela: HGGA: hierarchical guided genome assembler. BMC Bioinformatics, Volume 23, Article number 167, 2022.
    [Article online] [Implementation]
  • K. Mukherjee, M. Rossi, L. Salmela, and C. Boucher: Fast and efficient Rmap assembly using the bi-labelled de Bruijn graph. Algorithms for Molecular Biology, Volume 16, Article number 6, 2021.
    [Article online] [Implementation]
  • B. Alipanahi, A. Kuhnle, S.J. Puglisi, L. Salmela, and C. Boucher: Succinct dynamic de Bruijn graphs. Bioinformatics, Volume 37, Issue 14, 15, 2021, 1946–1952.
    [Article online] [Implementation]
  • B. Freire, S. Ladra, J. Paramá, and L. Salmela: Inference of viral quasispecies with a paired de Bruijn graph. Bioinformatics, Volume 37, Issue 4, 2021, 473-481.
    [Article online] [Implementation]