Towards Consensus Gene Ages

Abstract

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 13 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).

Evolutionary and genetic features of drug targets

“…In the past years, concerns about the effectiveness of phylostratigraphy were raised, which may challenge the reliability of our previous conclusions. These concerns focused on the homology detection algorithm employed in phylostratigraphy, which determines the accuracy of the gene‐age estimation method . In a series of papers, Moyers and Zhang argued that Basic Local Alignment Search Tool (BLAST), which is used to detect homologs in phylostratigraphy, have complications in detecting remote homologs, and therefore, could be a source of inherent bias .…”

“…The inherent bias of current homology detection algorithms generates inconsistent results, and may impair downstream analyses. Thus, it is noticeable that a relatively consensus gene‐age data set was provided by Liebeskind and colleagues . The data were processed using 13 popular homology detection algorithms.…”

Water as a reactant in the differential expression of proteins in cancer

“…The phylostratigraphic analysis used in these studies assigns ages of genes based on the latest common ancestor whose descendants have all the computationally detected homologs of that gene. To analyze the evolutionary trends of oxidation and hydration state of proteins, I used two sets of gene ages for human protein-coding genes: 16 phylostrata given by Trigos et al (44), and eight gene ages based on consensus among different methods given by Liebeskind et al (45). The phylostrata numbers are not identical in the two studies; the Liebeskind study has three steps between cellular organisms and Eukaryota, providing a greater resolution in earlier evolution, and stops at Mammalia, which corresponds to PS 10 in the Trigos phylostrata.…”

Inferring the Evolutionary History of Your Favorite Protein: A Guide for Molecular Biologists

“…Others, such as Orthologous MAtrix (OMA), focus on identifying pairwise (orthology) relationships between two proteins, while many studies aim for collecting all proteins from an orthologous group (OG) (Box ). Algorithms that infer such groups occasionally split a single group into multiple ones, due to sequence divergence and/or domain dynamics . In general, automatic orthology and homology methods often do not suffice because proteins differ in their evolutionary dynamics and therefore require specific approaches.…”

“…Algorithms that infer such groups occasionally split a single group into multiple ones, due to sequence divergence and/or domain dynamics. [28,29] In general, automatic orthology and homology methods often do not suffice because proteins differ in their evolutionary dynamics and therefore require specific approaches. Combined with manual analysis, one can compose a customized approach for the protein of interest.…”