ArticleBMC genomics2015
ProCARs: Progressive Reconstruction of Ancestral Gene Orders.
Article in BMC genomics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- Review
- A chromosome-anchored eggplant genome sequence reveals key events in Solanaceae evolution.Scientific reports · 2019Article
- Reconstructing Yeasts Phylogenies and Ancestors from Whole Genome Data.Scientific reports · 2017Article
- Ancestral Genome Reconstruction on Whole Genome Level.Current genomics · 2017Review
- Fast ancestral gene order reconstruction of genomes with unequal gene content.BMC bioinformatics · 2016Article
- Reconstructing ancestral gene orders with duplications guided by synteny level genome reconstruction.BMC bioinformatics · 2016Article
- Reconstruction of ancestral gene orders using intermediate genomes.BMC bioinformatics · 2015Article
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Authors and funding
4 authors.
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Abstract
backgroundIn the context of ancestral gene order reconstruction from extant genomes, there exist two main computational approaches: rearrangement-based, and homology-based methods. The rearrangement-based methods consist in minimizing a total rearrangement distance on the branches of a species tree. The homology-based methods consist in the detection of a set of potential ancestral contiguity features, followed by the assembling of these features into Contiguous Ancestral Regions (CARs).
resultsIn this paper, we present a new homology-based method that uses a progressive approach for both the detection and the assembling of ancestral contiguity features into CARs. The method is based on detecting a set of potential ancestral adjacencies iteratively using the current set of CARs at each step, and constructing CARs progressively using a 2-phase assembling method.
conclusionWe show the usefulness of the method through a reconstruction of the boreoeutherian ancestral gene order, and a comparison with three other homology-based methods: AnGeS, InferCARs and GapAdj. The program, written in Python, and the dataset used in this paper are available at http://bioinfo.lifl.fr/procars/.
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