Scaffold filling, contig fusion and comparative gene order inference
1 School of Information Technology and Engineering, University of Ottawa, Ottawa, K1N 6N5, Canada
2 Département d'informatique et de recherche opérationnelle, Université de Montréal, Montréal, H3C 3J7, Canada
3 Department of Computer Science, Princeton University, Princeton, NJ 08544, USA
4 Department of Biological Sciences, University at Buffalo, Buffalo, NY 14260, USA
5 School of Plant Sciences and BIO5 Institute, University of Arizona, Tucson, AZ 85719, USA
6 Department of Mathematics and Statistics, University of Ottawa, Ottawa, K1N 6N5, Canada
BMC Bioinformatics 2010, 11:304 doi:10.1186/1471-2105-11-304Published: 4 June 2010
There has been a trend in increasing the phylogenetic scope of genome sequencing without finishing the sequence of the genome. Increasing numbers of genomes are being published in scaffold or contig form. Rearrangement algorithms, however, including gene order-based phylogenetic tools, require whole genome data on gene order or syntenic block order. How then can we use rearrangement algorithms to compare genomes available in scaffold form only? Can the comparative evidence predict the location of unsequenced genes?
Our method involves optimally filling in genes missing from the scaffolds, while incorporating the augmented scaffolds directly into the rearrangement algorithms as if they were chromosomes. This is accomplished by an exact, polynomial-time algorithm. We then correct for the number of extra fusion/fission operations required to make scaffolds comparable to full assemblies. We model the relationship between the ratio of missing genes actually absent from the genome versus merely unsequenced ones, on one hand, and the increase of genomic distance after scaffold filling, on the other. We estimate the parameters of this model through simulations and by comparing the angiosperm genomes Ricinus communis and Vitis vinifera.
The algorithm solves the comparison of genomes with 18,300 genes, including 4500 missing from one genome, in less than a minute on a MacBook, putting virtually all genomes within range of the method.