Abstract

SummaryThe need to include the genetic variation within a population into a reference genome led to the concept of a genome sequence graph. Nodes of such a graph are labeled with DNA sequences occurring in represented genomes. Due to double-stranded nature of DNA, each node may be oriented in one of two possible ways, resulting in marking one end of the labeling sequence as in-side and the other as out-side. Edges join pairs of sides and reflect adjacency between node sequences in genomes constituting the graph. Linearization of a sequence graph aims at orienting and ordering graph nodes in a way that makes it more efficient for visualization and further analysis, e.g. access and traversal. We propose a new linearization algorithm, called ALIBI – Algorithm for Linearization by Incremental graph BuIlding. The evaluation shows that ALIBI is computationally very efficient and generates high-quality results.

Highlights

  • Reference genomes serve as most important genetic resources for particular populations

  • SUMMARY The need to include the genetic variation within a population into a reference genome led to the concept of a genome sequence graph

  • Linearization of a sequence graph aims at orienting and ordering graph nodes in a way that makes it more efficient for visualization and further analysis, e.g. access and traversal

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Summary

Introduction

Reference genomes serve as most important genetic resources for particular populations. They provide coordinate systems for gene annotations, targets for sequencing read mapping and downstream analysis, including variant detection, open chromatin areas and protein binding sites identification, 3-dimensional structure reconstruction, etc. When used as a target for read mapping, it introduces bias toward the reference alleles Brandt et al (2015). To overcome these drawbacks, the idea of common representation of a variety of genomes within a population has evolved, leading to the concept of genome sequence graph

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