Towards Consensus Gene Ages
Benjamin J. Liebeskind1,2,*, Claire D. McWhite1 and Edward M. Marcotte1
- Author Affiliations
1Center for Systems and Synthetic Biology, Institute for Cellular and Molecular Biology, & Department of Molecular Biosciences, University of Texas at Austin, Austin, TX 78712
2Center for Computational Biology and Bioinformatics, University of Texas at Austin, TX 78712
↵*Author for Correspondence: Benjamin J. Liebeskind, Center for Systems and Synthetic Biology, University of Texas at Austin, Phone: 512-232-3919, Fax: 512-232-3472, Email: firstname.lastname@example.org
Received March 6, 2016. Revision received April 18, 2016. Accepted May 1, 2016.
Correctly estimating the age of a gene or gene family is important for a variety of fields, including molecular evolution, comparative genomics, and phylogenetics, and increasingly for systems biology and disease genetics. However, most studies use only a point estimate of a gene’s age, neglecting the substantial uncertainty involved in this estimation. Here, we characterize this uncertainty by investigating the effect of algorithm choice on gene-age inference and calculate consensus gene ages with attendant error distributions for a variety of model eukaryotes. We use thirteen orthology inference algorithms to create gene-age datasets and then characterize the error around each age-call on a per-gene and per-algorithm basis. Systematic error was found to be a large factor in estimating gene age, suggesting that simple consensus algorithms are not enough to give a reliable point estimate. We also found that different sources of error can affect downstream analyses, such as gene ontology enrichment. Our consensus gene-age datasets, with associated error terms, are made fully available at so that researchers can propagate this uncertainty through their analyses (geneages.org).
Ortholog Phylostratigraphy LECA LUCA
© The Author(s) 2016. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.
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