Statistical confidence measures for genome maps: application to the validation of genome assemblies

Genome maps are imperative to address the genetic basis of the biology of an organism. While a growing number of genomes are being sequenced providing the ultimate genome maps-this being done at an even faster pace now using new generation sequencers-the process of constructing intermediate maps to build and validate a genome assembly remains an important component for producing complete genome sequences. However, current mapping approach lack statistical confidence measures necessary to identify precisely relevant inconsistencies between a genome map and an assembly.

We propose new methods to derive statistical measures of confidence on genome maps using a comparative model for radiation hybrid data. We describe algorithms allowing to (i) sample from a distribution of maps and (ii) exploit this distribution to construct robust maps. We provide an example of application of these methods on a dog dataset that demonstrates the interest of our approach.

Methods are implemented in two freely available softwares: Carthagene (http://www.inra.fr/mia/T/CarthaGene/) and a companion software (metamap, available at: http://snp.toulouse.inra.fr/~servin/index.cgi/Metamap).