ArticleBMC bioinformatics2015
Reconstruction of ancestral gene orders using intermediate genomes.
Article in BMC bioinformatics, 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.
- Achieving large and distant ancestral genome inference by using an improved discrete quantum-behaved particle swarm optimization algorithm.BMC bioinformatics · 2020Article
- SMORE: Synteny Modulator of Repetitive Elements.Life (Basel, Switzerland) · 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
- Comparative genomics meets topology: a novel view on genome median and halving problems.BMC bioinformatics · 2016Article
- Reconstructing ancestral gene orders with duplications guided by synteny level genome reconstruction.BMC bioinformatics · 2016Article
- Orthologs, turn-over, and remolding of tRNAs in primates and fruit flies.BMC genomics · 2016Article
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1 author.
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Abstract
backgroundThe problem of reconstructing ancestral genomes in a given phylogenetic tree arises in many different comparative genomics fields. Here, we focus on reconstructing the gene order of ancestral genomes, a problem that has been largely studied in the past 20 years, especially with the increasing availability of whole genome DNA sequences. There are two main approaches to this problem: event-based methods, that try to find the ancestral genomes that minimize the number of rearrangement events in the tree; and homology-based, that look for conserved structures, such as adjacent genes in the extant genomes, to build the ancestral genomes.
resultsWe propose algorithms that use the concept of intermediate genomes, arising in optimal pairwise rearrangement scenarios. We show that intermediate genomes have combinatorial properties that make them easy to reconstruct, and develop fast algorithms with better reconstructed ancestral genomes than current event-based methods. The proposed framework is also designed to accept extra information, such as results from homology-based approaches, giving rise to combined algorithms with better results than the original methods.
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