Accurate gene-tree reconstruction by learning gene- and species-specific substitution rates across multiple complete genomes is a research paper published in Genome Research (2007). On theSindex it has a DataRank of 0.646. It has been cited 73 times.
Comparative genomics provides a general methodology for discovering functional DNA elements and understanding their evolution. The availability of many related genomes enables more powerful analyses, but requires rigorous phylogenetic methods to resolve orthologous genes and regions. Here, we use 12 recently sequenced Drosophila genomes and nine fungal genomes to address the problem of accurate gene-tree reconstruction across many complete genomes. We show that existing phylogenetic methods that treat each gene tree in isolation show large-scale inaccuracies, largely due to insufficient phylogenetic information in individual genes. However, we find that gene trees exhibit common properties that can be exploited for evolutionary studies and accurate phylogenetic reconstruction. Evolutionary rates can be decoupled into gene-specific and species-specific components, which can be learned across complete genomes. We develop a phylogenetic reconstruction methodology that exploits these properties and achieves significantly higher accuracy, addressing the species-level heterotachy and enabling studies of gene evolution in the context of species evolution.
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Base Score Contribution
0.646
From this paper's citation signal
Citation Network Contribution
0
Citation network not refreshed for this result
This paper's DataRank is currently driven only by its base citation score. Citation network data was not refreshed for this result.
Learn more about DataRank methodology →DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 100% comes from its base citations and 0% from the citation network.
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.