Detecting somatic mutations in genomic sequences by means of Kolmogorov-Arnold analysis

摘要

The Kolmogorov-Arnold stochasticity parameter technique is applied for thefirst time to the study of cancer genome sequencing, to reveal mutations. Usingdata generated by next generation sequencing technologies, we have analyzed theexome sequences of brain tumor patients with matched tumor and normal blood. Weshow that mutations contained in sequencing data can be revealed using thistechnique thus providing a new methodology for determining subsequences ofgiven length containing mutations i.e. its value differs from those ofsubsequences without mutations. A potential application for this techniqueinvolves simplifying the procedure of finding segments with mutations, speedingup genomic research, and accelerating its implementation in clinicaldiagnostic. Moreover, the prediction of a mutation associated to a family offrequent mutations in numerous types of cancers based purely on the value ofthe Kolmogorov function, indicates that this applied marker may recognizegenomic sequences that are in extremely low abundance and can be used inrevealing new types of mutations.