Controversies in modern evolutionary biology: the imperative for error detection and quality control
- Equal contributors
1 Department of Integrated Structural Biology, IGBMC (Institut de Génétique et de Biologie Moléculaire et Cellulaire) CNRS/INSERM/Université de Strasbourg, 1 rue Laurent Fries, Illkirch, F-67404, France
2 Medical Biochemistry Department, Federal University of Rio de Janeiro, Avenida Carlos Chagas Filho 373, Rio de Janeiro, 21941-902, Brazil
3 UMR-CNRS 6632 Evolution Biologique et Modélisation, Université de Provence, 3, Place Victor Hugo, Marseille, 13331, France
BMC Genomics 2012, 13:5 doi:10.1186/1471-2164-13-5Published: 4 January 2012
The data from high throughput genomics technologies provide unique opportunities for studies of complex biological systems, but also pose many new challenges. The shift to the genome scale in evolutionary biology, for example, has led to many interesting, but often controversial studies. It has been suggested that part of the conflict may be due to errors in the initial sequences. Most gene sequences are predicted by bioinformatics programs and a number of quality issues have been raised, concerning DNA sequencing errors or badly predicted coding regions, particularly in eukaryotes.
We investigated the impact of these errors on evolutionary studies and specifically on the identification of important genetic events. We focused on the detection of asymmetric evolution after duplication, which has been the subject of controversy recently. Using the human genome as a reference, we established a reliable set of 688 duplicated genes in 13 complete vertebrate genomes, where significantly different evolutionary rates are observed. We estimated the rates at which protein sequence errors occur and are accumulated in the higher-level analyses. We showed that the majority of the detected events (57%) are in fact artifacts due to the putative erroneous sequences and that these artifacts are sufficient to mask the true functional significance of the events.
Initial errors are accumulated throughout the evolutionary analysis, generating artificially high rates of event predictions and leading to substantial uncertainty in the conclusions. This study emphasizes the urgent need for error detection and quality control strategies in order to efficiently extract knowledge from the new genome data.