Dataset Information

Cactus graphs for genome comparisons.

ABSTRACT: We introduce a data structure, analysis, and visualization scheme called a cactus graph for comparing sets of related genomes. In common with multi-break point graphs and A-Bruijn graphs, cactus graphs can represent duplications and general genomic rearrangements, but additionally, they naturally decompose the common substructures in a set of related genomes into a hierarchy of chains that can be visualized as two-dimensional multiple alignments and nets that can be visualized in circular genome plots. Supplementary Material is available at www.liebertonline.com/cmb .

SUBMITTER: Paten B

PROVIDER: S-EPMC8884192 | biostudies-literature | 2011 Mar

REPOSITORIES: biostudies-literature

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Cactus graphs for genome comparisons.

Paten Benedict B Diekhans Mark M Earl Dent D John John St JS Ma Jian J Suh Bernard B Haussler David D

Journal of computational biology : a journal of computational molecular cell biology 20110301 3

We introduce a data structure, analysis, and visualization scheme called a cactus graph for comparing sets of related genomes. In common with multi-break point graphs and A-Bruijn graphs, cactus graphs can represent duplications and general genomic rearrangements, but additionally, they naturally decompose the common substructures in a set of related genomes into a hierarchy of chains that can be visualized as two-dimensional multiple alignments and nets that can be visualized in circular genome ...[more]

PMID: 21385048

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Project description:The complex correlation structure of a collection of orthologous DNA sequences is uniquely captured by the "ancestral recombination graph" (ARG), a complete record of coalescence and recombination events in the history of the sample. However, existing methods for ARG inference are computationally intensive, highly approximate, or limited to small numbers of sequences, and, as a consequence, explicit ARG inference is rarely used in applied population genomics. Here, we introduce a new algorithm for ARG inference that is efficient enough to apply to dozens of complete mammalian genomes. The key idea of our approach is to sample an ARG of [Formula: see text] chromosomes conditional on an ARG of [Formula: see text] chromosomes, an operation we call "threading." Using techniques based on hidden Markov models, we can perform this threading operation exactly, up to the assumptions of the sequentially Markov coalescent and a discretization of time. An extension allows for threading of subtrees instead of individual sequences. Repeated application of these threading operations results in highly efficient Markov chain Monte Carlo samplers for ARGs. We have implemented these methods in a computer program called ARGweaver. Experiments with simulated data indicate that ARGweaver converges rapidly to the posterior distribution over ARGs and is effective in recovering various features of the ARG for dozens of sequences generated under realistic parameters for human populations. In applications of ARGweaver to 54 human genome sequences from Complete Genomics, we find clear signatures of natural selection, including regions of unusually ancient ancestry associated with balancing selection and reductions in allele age in sites under directional selection. The patterns we observe near protein-coding genes are consistent with a primary influence from background selection rather than hitchhiking, although we cannot rule out a contribution from recurrent selective sweeps.

Dataset Information

Cactus graphs for genome comparisons.

Publications

Cactus graphs for genome comparisons.

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