Estimating the selection pressure and evolutionary rate of proteins on the non-neutral hypothesis of synonymous mutations
Ye, J.; Cui, C.; Fan, R.; Cui, Q.
Show abstract
The Nonsynonymous/Synonymous substitution rate ratio (Ka/Ks) is a commonly used metric to estimate the selection pressure and evolutionary rate of proteins in comparative genomics, which plays critical roles in molecular evolution in both biology and medicine. A fundamental assumption of Ka/Ks is that synonymous mutations are evolutionarily neutral and not subject to natural selection as they do not alter protein sequences and function. However, a number of studies have demonstrated that synonymous mutations are non-neutral and may lead to diseases through a number of mechanisms, such as altering miRNA regulation. This further implies that synonymous mutations also participate in the process of natural selection and thus Ka/Ks should be redefined as well. For this purpose, here we propose an improved Ka/Ks ratio, iKa/Ks, which re-computed the neutral substitution rate by taking the altered status of miRNA binding into consideration, and thereby incorporate the impact of synonymous mutations on miRNA regulation. As a result, iKa/Ks shows better performance than Ka/Ks when comparing them using their correlation with expression distance. Moreover, case studies showed that iKa/Ks is able to identify the positive/negative selection genes that are missed by Ka/Ks. For example, TMEM72/Tmem72 is estimated to be positively selected by iKa/Ks (1.13) but negatively selected by the conventional Ka/Ks ratio (0.21). Further evidence showed its rapid evolution, which further support the power of the new algorithm.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Somatic Cell Nuclear Transfer Embryos Show Massive Dysregulation of Genes Involved in Transcription Pathway 95%
- From miRNA target gene network to miRNA function: miR-375 might regulate apoptosis and actin dynamics in the heart muscle via Rho-GTPases-dependent pathways 95%
- Unveiling epigenetic regulatory elements associated with breast cancer development 94%
Similar papers in this journal
- Integrating Bioinformatics and Artificial Intelligence Methods to identify disruptive STAT1 variants impacting Protein Stability and Function 94%
- De novo assembly of trachidermus fasciatus genome by nanopore sequencing 94%
- Integrated Analysis of Tissue-specific Gene Expression in Diabetes by Tensor Decomposition Can Identify Possible Associated Diseases. 94%
Similar papers in this journal
- Tensor decomposition- and principal component analysis-based unsupervised feature extraction to select more reasonable differentially expressed genes: Optimization of standard deviation versus state-of-art methods 94%
- Uncovering Hidden Cancer Self-Dependencies through Analysis of shRNA-Level Dependency Scores 94%
- A Deep Learning Method for MiRNA/IsomiR Target Detection 94%