Detecting Clusters of Mutations
Author Information
Author(s): Zhou Tong, Enyeart Peter J., Wilke Claus O.
Primary Institution: University of Texas at Austin
Hypothesis
Can a new algorithm effectively detect clusters of mutations in protein-coding sequences while accounting for solvent accessibility?
Conclusion
The study found that clustered evolution is relatively rare, with only 2% to 10% of analyzed genes containing statistically significant mutation clusters.
Supporting Evidence
- Only 2% to 10% of the genes analyzed contained a statistically significant mutation cluster.
- The algorithm provides a novel method to identify functionally relevant divergence between groups of species.
- Controlling for solvent accessibility prevents the detection of spurious clusters in highly variable regions.
Takeaway
The researchers created a new method to find groups of mutations in genes, which helps understand how proteins evolve. They discovered that these mutation groups are not very common.
Methodology
The study used a novel algorithm that controls for solvent accessibility and applies random permutations to calculate accurate P values for inferred clusters.
Potential Biases
The algorithm may miss clusters in tertiary structure that consist of mutations distant in primary structure.
Limitations
The method requires solved protein structures for every gene analyzed, limiting the size of the data sets.
Participant Demographics
The study analyzed genomes from bacteria, flies, and mammals.
Statistical Information
P-Value
p<0.05
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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