ArticleBMC bioinformatics2014
PhylDiag: identifying complex synteny blocks that include tandem duplications using phylogenetic gene trees.
Article in BMC bioinformatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 13 citations in OpenAlex.
- Reconstruction of hundreds of reference ancestral genomes across the eukaryotic kingdom.Nature ecology & evolution · 2023Article
- Review
- Comparative epigenomics in the Brassicaceae reveals two evolutionarily conserved modes of PRC2-mediated gene regulation.Genome biology · 2017Article
- Article
- Efficient Gene Tree Correction Guided by Genome Evolution.PloS one · 2016Article
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Authors and funding
3 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundExtant genomes share regions where genes have the same order and orientation, which are thought to arise from the conservation of an ancestral order of genes during evolution. Such regions of so-called conserved synteny, or synteny blocks, must be precisely identified and quantified, as a prerequisite to better understand the evolutionary history of genomes.
resultsHere we describe PhylDiag, a software that identifies statistically significant synteny blocks in pairwise comparisons of eukaryote genomes. Compared to previous methods, PhylDiag uses gene trees to define gene homologies, thus allowing gene deletions to be considered as events that may break the synteny. PhylDiag also accounts for gene orientations, blocks of tandem duplicates and lineage specific de novo gene births. Starting from two genomes and the corresponding gene trees, PhylDiag returns synteny blocks with gaps less than or equal to the maximum gap parameter gap(max). This parameter is theoretically estimated, and together with a utility to graphically display results, contributes to making PhylDiag a user friendly method. In addition, putative synteny blocks are subject to a statistical validation to verify that they are unlikely to be due to a random combination of genes.
conclusionsWe benchmark several known metrics to measure 2D-distances in a matrix of homologies and we compare PhylDiag to i-ADHoRe 3.0 on real and simulated data. We show that PhylDiag correctly identifies small synteny blocks even with insertions, deletions, incorrect annotations or micro-inversions. Finally, PhylDiag allowed us to identify the most relevant distance metric for 2D-distance calculation between homologies.
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