Finding Statistically Significant Repeats in Nucleic Acids and Proteins.

DNA repeats have great importance for biological research and a large number of tools for determining repeats have been developed. Herein we define a method for extracting a statistically significant subset of a determined set of repeats. Our aim was to identify a subset of repeats in the input sequences that are not expected to occur with a number of their appearances in a random sequence of the same length. It is expected that results obtained in such manner would reduce the quantity of processed material and could thereby represent a more important biological signal. With DNA, RNA, and protein sequences serving as input material, we also examined the possibility of statistical filtering of repeats in sequences over an arbitrary alphabet. A new method for selecting statistically significant repeats from a set of determined repeats has been defined. The proposed method was tested on a large number of randomly generated sequences. The application of the method on biological sequences revealed that for some viruses, shorter repeats are more statistically significant than longer ones because of their frequent appearance, whereas for bacteria, the majority of identified repeats are statistically significant.