miphy: identify and quantify rapidly evolving members of large gene families

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ID: 227264
2018
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Abstract
After transitioning to a new environment, species often exhibit rapid phenotypic innovation. One of the fastest mechanisms for this is duplication followed by specialization of existing genes. When this happens to a member of a gene family, it tends to leave a detectable phylogenetic signature of lineage-specific expansions and contractions. These can be identified by analyzing the gene family across several species and identifying patterns of gene duplication and loss that do not correlate with the known relationships between those species. This signature, termed phylogenetic instability, has been previously linked to adaptations that change the way an organism samples and responds to its environment; conversely, low phylogenetic instability has been previously linked to proteins with endogenous functions. With the increase in genome-level data, there is a need to identify and quantify phylogenetic instability. Here, we present Minimizing Instability in Phylogenetics (MIPhy), a tool that solves this problem by quantifying the incongruence of a gene’s evolutionary history. The motivation behind MIPhy was to produce a tool to aid in interpreting phylogenetic trees. It can predict which members of a gene family are under adaptive evolution, working only from a gene tree and the relationship between the species under consideration. While it does not conduct any estimation of positive selection—which is the typical indication of adaptive evolution—the results tend to agree. We demonstrate the usefulness of MIPhy by accurately predicting which members of the mammalian cytochrome P450 gene superfamily metabolize xenobiotics and which metabolize endogenous compounds. Our predictions correlate very well with known substrate specificities of the human enzymes. We also analyze the Caenorhabditis collagen gene family and use MIPhy to predict genes that produce an observable phenotype when knocked down in C. elegans, and show that our predictions correlate well with existing knowledge. The software can be downloaded and installed from https://github.com/dave-the-scientist/miphy and is also available as an online web tool at http://www.miphy.wasmuthlab.org.
Reference Key
curran2018peerjmiphy: Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;David M. Curran;John S. Gilleard;James D. Wasmuth
Journal pediatrics
Year 2018
DOI
10.7717/peerj.4873
URL
https://peerj.com/articles/4873/
Keywords
gene family evolutionmedicine

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