Detecting clusters of mutations

• Published in: PloS one Vol. 3; no. 11; p. e3765
• Main Authors: Zhou, Tong, Enyeart, Peter J, Wilke, Claus O
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 19.11.2008; Public Library of Science (PLoS)
• Subjects: Accessibility; Accuracy; Acids; Alcohol; Algorithms; Amino Acid Sequence; Animals; Bacteria; Biochemistry/Molecular Evolution; Bioinformatics; Biophysics/Structural Genomics; Buried structures; Cluster Analysis; Clusters; Computational Biology/Comparative Sequence Analysis; Dehydrogenases; Deoxyribonucleic acid; Divergence; DNA; DNA - metabolism; Escherichia coli - genetics; Evolution; Evolutionary algorithms; Evolutionary Biology/Bioinformatics; Evolutionary Biology/Evolutionary and Comparative Genetics; Evolutionary Biology/Genomics; Evolutionary Biology/Human Evolution; Genetic aspects; Genetic research; Genome; Genomes; Genomics; Humans; Hypotheses; Insects; Mathematical functions; Methods; Models, Genetic; Molecular biology; Molecular Conformation; Molecular Sequence Data; Mutation; Neural networks; Pattern recognition; Permutations; Positive selection; Protein structure; Proteins; Proteins - chemistry; Residues; Sequence Homology, Amino Acid; Solvents; Solvents - chemistry; Statistical analysis; United States > US; Texas
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0003765

Positive selection for protein function can lead to multiple mutations within a small stretch of DNA, i.e., to a cluster of mutations. Recently, Wagner proposed a method to detect such mutation clusters. His method, however, did not take into account that residues with high solvent accessibility are inherently more variable than residues with low solvent accessibility. Here, we propose a new algorithm to detect clustered evolution. Our algorithm controls for different substitution probabilities at buried and exposed sites in the tertiary protein structure, and uses random permutations to calculate accurate P values for inferred clusters. We apply the algorithm to genomes of bacteria, fly, and mammals, and find several clusters of mutations in functionally important regions of proteins. Surprisingly, clustered evolution is a relatively rare phenomenon. Only between 2% and 10% of the genes we analyze contain a statistically significant mutation cluster. We also find that not controlling for solvent accessibility leads to an excess of clusters in terminal and solvent-exposed regions of proteins. Our algorithm provides a novel method to identify functionally relevant divergence between groups of species. Moreover, it could also be useful to detect artifacts in automatically assembled genomes.