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This article is part of the supplement: Highlights from the Ninth International Society for Computational Biology (ISCB) Student Council Symposium 2013

Open Access Meeting abstract

Determinants of protein evolutionary rates in light of ENCODE functional genomics

Nadezda Kryuchkova12* and Marc Robinson-Rechavi12

  • * Corresponding author: Nadezda Kryuchkova

Author Affiliations

1 Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne, Switzerland

2 Swiss Institute of Bioinformatics (SIB), 1015 Lausanne, Switzerland

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BMC Bioinformatics 2014, 15(Suppl 3):A9  doi:10.1186/1471-2105-15-S3-A9


The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1471-2105/15/S3/A9


Published:11 February 2014

© 2014 Kryuchkova and Robinson-Rechavi; licensee BioMed Central Ltd.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Background

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.

Results

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 webcite). Over all tissues, an interesting result is that evolutionary rate shows no strong correlation with expression level in mouse if corrected for other parameters.

thumbnailFigure 1. 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.

Conclusions

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.

References

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  2. Larracuente AM, Sackton TB, Greenberg AJ, Wong A, Singh ND, Sturgill D, Zhang Y, Oliver B, Clark AG: Evolution of protein-coding genes in Drosophila.

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  3. ENCODE Project Consortium: A user's guide to the encyclopedia of DNA elements (ENCODE).

    PLoS Biol 2011, 9(4):e1001046. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL