Abstract
DNA sequencing is the process of determining the exact order of the nucleotide bases of an individual's genome in order to catalogue sequence variation and understand its biological implications. Whole-genome sequencing techniques produce masses of data in the form of short sequences known as reads. Assembling these reads into a whole genome constitutes a major algorithmic challenge. Most assembly algorithms utilise de Bruijn graphs constructed from reads for this purpose. A critical step of these algorithms is to detect typical motif structures in the graph caused by sequencing errors and genome repeats, and filter them out; one such complex subgraph class is a so-called superbubble. In this paper, we propose an O(n+m)-time algorithm to detect all superbubbles in a directed acyclic graph with n vertices and m (directed) edges, improving the best-known O(mlogm)-time algorithm by Sung et al.
Highlights
Since the publication of the first draft of the human genome [1,2], the field of genomics has changed dramatically
A fundamentally different approach based on de Bruijn graphs was proposed [6], where representation of data elements was organised around words of k nucleotides, or k-mers, instead of reads
Unlike in an overlap graph, in a de Bruijn graph [7], each k − 1 nucleotide long prefix and suffix of the k-mers is represented as a vertex and each k-mer
Summary
Since the publication of the first draft of the human genome [1,2], the field of genomics has changed dramatically. Traditional assembly algorithms rely on the overlap-layout-consensus approach [4], representing each read as a vertex in an overlap graph and each detected overlap as a directed edge between the vertices corresponding to overlapping reads. These methods have proved their use through numerous de novo genome assemblies [5]. A bubble consists of multiple directed unipaths (where a unipath is a path in which all internal vertices are of degree 2) from a vertex v to a vertex u and is commonly caused by a small number of errors in the centre of reads These types of motifs are simple and can be identified and filtered out, more complicated motifs prove to be more challenging.
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