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Scaffold filling, contig fusion and comparative gene order inference.

Muñoz A, Zheng C, Zhu Q, Albert VA, Rounsley S, Sankoff D - BMC Bioinformatics (2010)

Bottom Line: 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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Mathematics and Statistics, University of Ottawa, Ottawa, K1N 6N5, Canada.

ABSTRACT

Background: 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?

Results: 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.

Conclusions: 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.

Show MeSH
Types of partially sequenced and incompletely assembled genomes. Shaded areas represent sequenced contigs. Dots represent identified genes. The set of contigs within each outlined portion has a known order.
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Figure 1: Types of partially sequenced and incompletely assembled genomes. Shaded areas represent sequenced contigs. Dots represent identified genes. The set of contigs within each outlined portion has a known order.

Mentions: In an idealized completely sequenced and gene-identified genome, complete gene orders would be known for each chromosome (Figure 1a). When genome sequencing is not supplemented by finishing techniques, however, three different types of incomplete gene order data can result. When a strategy such as shotgun sequencing of unordered clones is employed, we have only isolated contigs constructed from overlapping reads, which would contain no internal gaps but could be relatively short assemblies (Figure 1b).


Scaffold filling, contig fusion and comparative gene order inference.

Muñoz A, Zheng C, Zhu Q, Albert VA, Rounsley S, Sankoff D - BMC Bioinformatics (2010)

Types of partially sequenced and incompletely assembled genomes. Shaded areas represent sequenced contigs. Dots represent identified genes. The set of contigs within each outlined portion has a known order.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2902449&req=5

Figure 1: Types of partially sequenced and incompletely assembled genomes. Shaded areas represent sequenced contigs. Dots represent identified genes. The set of contigs within each outlined portion has a known order.
Mentions: In an idealized completely sequenced and gene-identified genome, complete gene orders would be known for each chromosome (Figure 1a). When genome sequencing is not supplemented by finishing techniques, however, three different types of incomplete gene order data can result. When a strategy such as shotgun sequencing of unordered clones is employed, we have only isolated contigs constructed from overlapping reads, which would contain no internal gaps but could be relatively short assemblies (Figure 1b).

Bottom Line: 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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Mathematics and Statistics, University of Ottawa, Ottawa, K1N 6N5, Canada.

ABSTRACT

Background: 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?

Results: 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.

Conclusions: 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.

Show MeSH