Economic Genome Assembly from Low Coverage Illumina and Nanopore Data

Authors Thomas Gatter , Sarah von Löhneysen, Polina Drozdova , Tom Hartmann , Peter F. Stadler

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Author Details

Thomas Gatter
  • Bioinformatics Group, Department of Computer Science, University of Leipzig, Germany
  • Interdisciplinary Center of Bioinformatics, University of Leipzig, Germany
Sarah von Löhneysen
  • Bioinformatics Group, Department of Computer Science, University of Leipzig, Germany
  • Interdisciplinary Center of Bioinformatics, University of Leipzig, Germany
Polina Drozdova
  • Institute of Biology, Irkutsk State University, Russia
Tom Hartmann
  • Bioinformatics Group, Department of Computer Science, University of Leipzig, Germany
  • Interdisciplinary Center of Bioinformatics, University of Leipzig, Germany
Peter F. Stadler
  • Bioinformatics Group, Department of Computer Science, University of Leipzig, Germany
  • Interdisciplinary Center of Bioinformatics, University of Leipzig, Germany
  • Max-Planck-Institute for Mathematics in the Sciences, Leipzig, Germany
  • Institut for Theoretical Chemistry, University of Vienna, Austria
  • Facultad de Ciencias, Universidad National de Colombia, Bogotá, Colombia
  • Santa Fe Institute, NM, USA

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Thomas Gatter, Sarah von Löhneysen, Polina Drozdova, Tom Hartmann, and Peter F. Stadler. Economic Genome Assembly from Low Coverage Illumina and Nanopore Data. In 20th International Workshop on Algorithms in Bioinformatics (WABI 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 172, pp. 10:1-10:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)


Ongoing developments in genome sequencing have caused a fundamental paradigm shift in the field in recent years. With ever lower sequencing costs, projects are no longer limited by available raw data, but rather by computational demands. The high complexity of eukaryotic genomes in concordance with increasing data sizes creates unique demands on methods to assemble full genomes. We describe a new approach to assemble genomes from a combination of low-coverage short and long reads. LazyB starts from a bipartite overlap graph between long reads and restrictively filtered short-read unitigs, which are then reduced to a long-read overlap graph G. Instead of the more conventional approach of removing tips, bubbles, and other local features, LazyB stepwisely extracts subgraphs whose global properties approach a disjoint union of paths. First, a consistently oriented subgraph is extracted, which in a second step is reduced to a directed acyclic graph. In the next step, properties of proper interval graphs are used to extract contigs as maximum weight paths. These are translated into genomic sequences only in the final step. A prototype implementation of LazyB, entirely written in python, not only yields significantly more accurate assemblies of the yeast and fruit fly genomes compared to state-of-the-art pipelines but also requires much less computational effort. Our findings demonstrate a new low-cost method that enables the assembly of even large genomes with low computational effort.

Subject Classification

ACM Subject Classification
  • Theory of computation → Discrete optimization
  • Applied computing → Computational genomics
  • Nanopore sequencing
  • Illumina sequencing
  • genome assembly
  • spanning tree
  • unitigs
  • anchors


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