A robust framework for detecting structural variations in a genome.
BACKGROUND
Recently, structural genomic variants have come to the forefront as a significant source of variation in the human population, but the identification of these variants in a large genome remains a challenge. The complete sequencing of a human individual is prohibitive at current costs, while current polymorphism detection technologies, such as SNP arrays, are not able to identify many of the large scale events. One of the most promising methods to detect such variants is the computational mapping of clone-end sequences to a reference genome.


RESULTS
Here, we present a probabilistic framework for the identification of structural variants using clone-end sequencing. Unlike previous methods, our approach does not rely on an a priori determined mapping of all reads to the reference. Instead, we build a framework for finding the most probable assignment of sequenced clones to potential structural variants based on the other clones. We compare our predictions with the structural variants identified in three previous studies. While there is a statistically significant correlation between the predictions, we also find a significant number of previously uncharacterized structural variants. Furthermore, we identify a number of putative cross-chromosomal events, primarily located proximally to the centromeres of the chromosomes.


BACKGROUND
Our dataset, results and source code are available at http://compbio.cs.toronto.edu/structvar/.
Lee
Seunghak
S
Department of Computer Science, University of Toronto, Toronto, ON M5S 3G4, Canada. seunghak@cs.toronto.edu
Cheran
Elango
E
Brudno
Michael
M
D000465:Algorithms; D001483:Base Sequence; D002874:Chromosome Mapping / Q000379:methods*; D003198:Computer Simulation; D014644:Genetic Variation / Q000235:genetics*; D016678:Genome / Q000235:genetics*; D008957:Models, Genetic; D015233:Models, Statistical; D008969:Molecular Sequence Data; D017422:Sequence Analysis, DNA / Q000379:methods*