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Towards Consensus Gene Ages.

Benjamin J Liebeskind¹, Claire D McWhite², Edward M Marcotte²

¹Department of Molecular Biosciences, Center for Systems and Synthetic Biology, Institute for Cellular and Molecular Biology, University of Texas at Austin Center for Computational Biology and Bioinformatics, University of Texas at Austin bliebeskind@austin.utexas.edu.

Genome Biology and Evolution

|June 5, 2016

Summary

This summary is machine-generated.

Estimating gene age is crucial, but current methods often ignore uncertainty. This study reveals significant error in gene age estimation, highlighting the need for methods that account for uncertainty in evolutionary analyses.

Keywords:

LECA LUCA ortholog phylostratigraphy

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Area of Science:

Evolutionary biology
Genomics
Bioinformatics

Background:

Accurate gene age estimation is vital for molecular evolution, comparative genomics, phylogenetics, systems biology, and disease genetics.
Current gene age studies often rely on point estimates, overlooking significant uncertainty inherent in the estimation process.

Purpose of the Study:

To characterize the uncertainty in gene age estimation.
To investigate the impact of different orthology inference algorithms on gene age inference.
To provide consensus gene ages with associated error distributions for model eukaryotes.

Main Methods:

Utilized 13 distinct orthology inference algorithms to generate gene age datasets.
Quantified error associated with gene age calls on a per-gene and per-algorithm basis.
Calculated consensus gene ages incorporating error distributions.

Main Results:

Identified systematic error as a major factor influencing gene age estimation accuracy.
Demonstrated that simple consensus algorithms are insufficient for reliable point estimates.
Showcased how different error sources can impact downstream analyses, including gene ontology enrichment.

Conclusions:

Gene age estimation is subject to substantial uncertainty influenced by algorithm choice.
Reliable gene age inference requires methods that explicitly address and quantify estimation error.
The developed consensus gene-age datasets with error terms are provided to facilitate robust downstream analyses in evolutionary and genomic research.