Email updates

Keep up to date with the latest news and content from BMC Bioinformatics and BioMed Central.

Open Access Highly Accessed Research article

Genome alignment with graph data structures: a comparison

Birte Kehr12*, Kathrin Trappe1, Manuel Holtgrewe1 and Knut Reinert1

Author Affiliations

1 Department of Computer Science, Freie Universität Berlin, Takustr. 9, 14195 Berlin, Germany

2 Max Planck Institute for Molecular Genetics, Ihnestr. 63-73, 14195 Berlin, Germany

For all author emails, please log on.

BMC Bioinformatics 2014, 15:99  doi:10.1186/1471-2105-15-99

Published: 9 April 2014

Abstract

Background

Recent advances in rapid, low-cost sequencing have opened up the opportunity to study complete genome sequences. The computational approach of multiple genome alignment allows investigation of evolutionarily related genomes in an integrated fashion, providing a basis for downstream analyses such as rearrangement studies and phylogenetic inference.

Graphs have proven to be a powerful tool for coping with the complexity of genome-scale sequence alignments. The potential of graphs to intuitively represent all aspects of genome alignments led to the development of graph-based approaches for genome alignment. These approaches construct a graph from a set of local alignments, and derive a genome alignment through identification and removal of graph substructures that indicate errors in the alignment.

Results

We compare the structures of commonly used graphs in terms of their abilities to represent alignment information. We describe how the graphs can be transformed into each other, and identify and classify graph substructures common to one or more graphs. Based on previous approaches, we compile a list of modifications that remove these substructures.

Conclusion

We show that crucial pieces of alignment information, associated with inversions and duplications, are not visible in the structure of all graphs. If we neglect vertex or edge labels, the graphs differ in their information content. Still, many ideas are shared among all graph-based approaches. Based on these findings, we outline a conceptual framework for graph-based genome alignment that can assist in the development of future genome alignment tools.