Gene-pseudogene evolution: A probabilistic approach

N/ACitations
Citations of this article
43Readers
Mendeley users who have this article in their library.

This article is free to access.

Abstract

Over the last decade, methods have been developed for the reconstruction of gene trees that take into account the species tree. Many of these methods have been based on the probabilistic duplication-loss model, which describes how a gene-tree evolves over a species-tree with respect to duplication and losses, as well as extension of this model, e.g., the DLRS (Duplication, Loss, Rate and Sequence evolution) model that also includes sequence evolution under relaxed molecular clock. A disjoint, almost as recent, and very important line of research has been focused on non protein-coding, but yet, functional DNA. For instance, DNA sequences being pseudogenes in the sense that they are not translated, may still be transcribed and the thereby produced RNA may be functional. We extend the DLRS model by including pseudogenization events and devise an MCMC framework for analyzing extended gene families consisting of genes and pseudogenes with respect to this model, i.e., reconstructing genetrees and identifying pseudogenization events in the reconstructed gene-trees. By applying the MCMC framework to biologically realistic synthetic data, we show that gene-trees as well as pseudogenization points can be inferred well. We also apply our MCMC framework to extended gene families belonging to the Olfactory Receptor and Zinc Finger superfamilies. The analysis indicate that both these super families contains very old pseudogenes, perhaps so old that it is reasonable to suspect that some are functional. In our analysis, the sub families of the Olfactory Receptors contains only lineage specific pseudogenes, while the sub families of the Zinc Fingers contains pseudogene lineages common to several species.

Cite

CITATION STYLE

APA

Mahmudi, O., Sennblad, B., Arvestad, L., Nowick, K., & Lagergren, J. (2015). Gene-pseudogene evolution: A probabilistic approach. BMC Genomics, 16, 1–11. https://doi.org/10.1186/1471-2164-16-S10-S12

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free