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: email@example.com
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.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact firstname.lastname@example.org
FREE PDF GRATIS: Genome Biol Evol