An Alternative Approach to Multiple Genome Comparison

Alban Mancheron 1, * Raluca Uricaru 1 Eric Rivals 1, *
* Corresponding author
1 MAB - Méthodes et Algorithmes pour la Bioinformatique
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : Genome comparison is now a crucial step for genome annotation and identification of regulatory motifs. Genome comparison aims for instance at finding genomic regions either specific to or in one-to-one correspondance between individuals/strains/species. It serves e.g. to pre-annotate a new genome by automatically transfering annotations from a known one. However, efficiency, flexibility and objectives of current methods do not suit the whole spectrum of applications, genome sizes and organizations. Innovative approaches are still needed. Hence, we propose an alternative way of comparing multiple genomes based on segmentation by similarity. In this framework, rather than being formulated as a complex optimization problem, genome comparison is seen as a segmentation question for which a single optimal solution can be found in almost linear time. We apply our method to analyse three strains of a virulent pathogenic bacteria, Ehrlichia~ruminantium, and identify 92 new genes. We also find out that a substantial number of genes thought to be strain specific have potential orthologs in the other strains. Our solution is implemented in an efficient program, QOD, equipped with a user-friendly interface, and enables the automatic transfer of annotations betwen compared genomes or contigs (Video in Supplementary Data [http://nar.oxfordjournals.org/cgi/content/full/gkr177/DC1]). Because it somehow disregards the relative order of genomic blocks, QOD can handle unfinished genomes, which due to the difficulty of sequencing completion may become an interesting characteristic for the future. Availabilty: http://www.atgc-montpellier.fr/qod.
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Alban Mancheron, Raluca Uricaru, Eric Rivals. An Alternative Approach to Multiple Genome Comparison. Nucleic Acids Research, Oxford University Press, 2011, 39 (15), pp.e101. ⟨10.1093/nar/gkr177⟩. ⟨lirmm-00620931⟩

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