1Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK2Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK3European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, UK
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Motivation:The de Bruijn graph is a simple and efficient data structure that is used in many areas of sequence analysis including genome assembly, read error correction and variant calling. The data structure has a single parameter k, is straightforward to implement and is tractable for large genomes with high sequencing depth. It also enables representation of multiple samples simultaneously to facilitate comparison. However, unlike the string graph, a de Bruijn graph does not retain long range information that is inherent in the read data. For this reason, applications that rely on de Bruijn graphs can produce sub-optimal results given their input data.Results:We present a novel assembly graph data structure: the Linked de Bruijn Graph (LdBG). Constructed by adding annotations on top of a de Bruijn graph, it stores long range connectivity information through the graph. We show that with error-free data it is possible to losslessly store and recover sequence from a Linked de Bruijn graph. With assembly simulations we demonstrate that the LdBG data structure outperforms both our de Bruijn graph and the String Graph Assembler (SGA). Finally we apply the LdBG to Klebsiella pneumoniae short read data to make large (12 kbp) variant calls, which we validate using PacBio sequencing data, and to characterize the genomic context of drug-resistance genes.Availability and implementation:Linked de Bruijn Graphs and associated algorithms are implemented as part of McCortex, which is available under the MIT license at https://github.com/mcveanlab/mccortex.Supplementary information:Supplementary data are available at Bioinformatics online.