Detecting clusters of mutations.
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...
Guardado en:
| Autores principales: | , , |
|---|---|
| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Public Library of Science (PLoS)
2008
|
| Materias: | |
| Acceso en línea: | https://doaj.org/article/30ee153206db49ecbf9e29b851503ea5 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| id |
oai:doaj.org-article:30ee153206db49ecbf9e29b851503ea5 |
|---|---|
| record_format |
dspace |
| spelling |
oai:doaj.org-article:30ee153206db49ecbf9e29b851503ea52021-11-25T06:18:28ZDetecting clusters of mutations.1932-620310.1371/journal.pone.0003765https://doaj.org/article/30ee153206db49ecbf9e29b851503ea52008-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/19018282/?tool=EBIhttps://doaj.org/toc/1932-6203Positive 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.Tong ZhouPeter J EnyeartClaus O WilkePublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 3, Iss 11, p e3765 (2008) |
| institution |
DOAJ |
| collection |
DOAJ |
| language |
EN |
| topic |
Medicine R Science Q |
| spellingShingle |
Medicine R Science Q Tong Zhou Peter J Enyeart Claus O Wilke Detecting clusters of mutations. |
| description |
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. |
| format |
article |
| author |
Tong Zhou Peter J Enyeart Claus O Wilke |
| author_facet |
Tong Zhou Peter J Enyeart Claus O Wilke |
| author_sort |
Tong Zhou |
| title |
Detecting clusters of mutations. |
| title_short |
Detecting clusters of mutations. |
| title_full |
Detecting clusters of mutations. |
| title_fullStr |
Detecting clusters of mutations. |
| title_full_unstemmed |
Detecting clusters of mutations. |
| title_sort |
detecting clusters of mutations. |
| publisher |
Public Library of Science (PLoS) |
| publishDate |
2008 |
| url |
https://doaj.org/article/30ee153206db49ecbf9e29b851503ea5 |
| work_keys_str_mv |
AT tongzhou detectingclustersofmutations AT peterjenyeart detectingclustersofmutations AT clausowilke detectingclustersofmutations |
| _version_ |
1718413915646328832 |