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

TL;DR

This paper introduces a novel application of the Kolmogorov-Arnold stochasticity parameter technique to detect somatic mutations in cancer genome sequencing data, potentially simplifying mutation identification and aiding clinical diagnostics.

Contribution

The study applies the Kolmogorov-Arnold method to genomic data for the first time, demonstrating its effectiveness in identifying mutations and predicting mutation-associated sequences.

Findings

Mutations can be detected using the Kolmogorov-Arnold technique.

The method can identify mutation-containing subsequences.

Potential for rapid mutation detection in clinical settings.

Abstract

The Kolmogorov-Arnold stochasticity parameter technique is applied for the first time to the study of cancer genome sequencing, to reveal mutations. Using data generated by next generation sequencing technologies, we have analyzed the exome sequences of brain tumor patients with matched tumor and normal blood. We show that mutations contained in sequencing data can be revealed using this technique thus providing a new methodology for determining subsequences of given length containing mutations i.e. its value differs from those of subsequences without mutations. A potential application for this technique involves simplifying the procedure of finding segments with mutations, speeding up genomic research, and accelerating its implementation in clinical diagnostic. Moreover, the prediction of a mutation associated to a family of frequent mutations in numerous types of cancers based purely on the value of the Kolmogorov function, indicates that this applied marker may recognize genomic sequences that are in extremely low abundance and can be used in revealing new types of mutations.