Year: 2014
Type: article
Abstract: The influence of different parameters, from gene size to expression levels, on the evolution of proteins has been previously studied mostly in yeast [ 1 ] and Drosophila [ 2 ]. The main feature which has been found to explain protein evolutionary rate was the level of gene expression, especially in yeast. Here we investigate these relations further, and extend them to mammals, especially taking in account gene expression in different organs. For expression we used the RNA-seq data from ENCODE [ 3 ] for 22 different tissues of mouse. We used ENCODE data to define which transcript is used as reference to compute features such as gene length or intron number. The relation between evolutionary rate and six gene features: gene expression, gene expression specificity, intron number, intron length, protein length and GC% content were analyzed. We use partial correlation to take into account dependencies between them. We find strong differences between tissues in the impact of expression on evolutionary rate (Figure 1 and http://f1000.com/posters/browse/summary/1094165 ). Over all tissues, an interesting result is that evolutionary rate shows no strong correlation with expression level in mouse if corrected for other parameters. Partial correlations of gene parameters (mouse data) with Pearson correlation coefficient. The width of connections shows the strength of the correlations. Only correlations with p-value > 0.001 are shown. Positive correlations are represented in yellow and negative in blue. Tissues were ordered according to correlation with evolutionary rate. Dependencies between gene features need to be taken into account for an unbiased view of gene evolution. Overall results are consistent with those in Drosophila [ 2 ]. We find important differences between tissues in the relation between expression and evolutionary rate, especially for the central nervous system and testis.
Source: BMC Bioinformatics
Authors: Nadezda Kryuchkova, Marc Robinson-Rechavi
Institutions: SIB Swiss Institute of Bioinformatics, University of Lausanne
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,990
OpenAlex ID: W2068541733