A representation of a compressed de Bruijn graph for pan-genome analysis that enables search

TL;DR

This paper introduces a more efficient algorithm and a space-saving representation for compressed de Bruijn graphs, enabling faster pan-genome analysis and pattern search within large genomic datasets.

Contribution

It presents a new construction algorithm that outperforms previous methods and a space-efficient graph representation that supports pattern searching.

Findings

The new algorithm is faster than splitMEM in theory and practice.

The representation allows pattern search within the pan-genome.

Enhanced efficiency benefits large-scale genomic analyses.

Abstract

Recently, Marcus et al. (Bioinformatics 2014) proposed to use a compressed de Bruijn graph to describe the relationship between the genomes of many individuals/strains of the same or closely related species. They devised an $O(n \log g)$ time algorithm called splitMEM that constructs this graph directly (i.e., without using the uncompressed de Bruijn graph) based on a suffix tree, where $n$ is the total length of the genomes and $g$ is the length of the longest genome. In this paper, we present a construction algorithm that outperforms their algorithm in theory and in practice. Moreover, we propose a new space-efficient representation of the compressed de Bruijn graph that adds the possibility to search for a pattern (e.g. an allele - a variant form of a gene) within the pan-genome.