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

Gurzadyan VG, Yan H, Vlahovic G, Kashin A, Killela P, Reitman Z, Sargsyan S, Yegorian G, Milledge G, Vlahovic B - R Soc Open Sci (2015)

Bottom Line: Using data generated by next-generation sequencing technologies, we have analysed the exome sequences of brain tumour patients with matched tumour 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.Moreover, the prediction of a mutation associated with 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.

View Article: PubMed Central - PubMed

Affiliation: NSF Computational Center of Research Excellence and NASA University Research Center for Aerospace Device , NCCU, Durham, NC, USA ; Yerevan Physics Institute and Yerevan State University , Yerevan, Armenia.

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 analysed the exome sequences of brain tumour patients with matched tumour 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 diagnostics. Moreover, the prediction of a mutation associated with 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.

No MeSH data available.


Related in: MedlinePlus

The same as in figure 1, but with the averaged values of the function Φ for 100-base rows for the highest recurrent specific mutations in the studied dataset as listed in table 2.
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RSOS150143F2: The same as in figure 1, but with the averaged values of the function Φ for 100-base rows for the highest recurrent specific mutations in the studied dataset as listed in table 2.

Mentions: Then, rows containing the most frequent specific mutations in the same dataset of 30 patients (table 2) were analysed. Obviously, more frequent mutations provide higher statistics, and table 2 and figure 2 are represented to show the scales of the input frequency numbers versus the results. The mutations, i.e. the genes, the mutant positions and amino acid changes, are known for the codes listed in table 2, and from the individual mutation reports of the performed studies [12] one can list all mutations contained within each tumour; we intend to address these issues in further publications on the applications of the method discussed here. The results for the mean Φ with standard error bars are presented in figure 2. One can see that certain specific mutations can be distinguished by the value of mean Φ.Figure 2.


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

Gurzadyan VG, Yan H, Vlahovic G, Kashin A, Killela P, Reitman Z, Sargsyan S, Yegorian G, Milledge G, Vlahovic B - R Soc Open Sci (2015)

The same as in figure 1, but with the averaged values of the function Φ for 100-base rows for the highest recurrent specific mutations in the studied dataset as listed in table 2.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4555851&req=5

RSOS150143F2: The same as in figure 1, but with the averaged values of the function Φ for 100-base rows for the highest recurrent specific mutations in the studied dataset as listed in table 2.
Mentions: Then, rows containing the most frequent specific mutations in the same dataset of 30 patients (table 2) were analysed. Obviously, more frequent mutations provide higher statistics, and table 2 and figure 2 are represented to show the scales of the input frequency numbers versus the results. The mutations, i.e. the genes, the mutant positions and amino acid changes, are known for the codes listed in table 2, and from the individual mutation reports of the performed studies [12] one can list all mutations contained within each tumour; we intend to address these issues in further publications on the applications of the method discussed here. The results for the mean Φ with standard error bars are presented in figure 2. One can see that certain specific mutations can be distinguished by the value of mean Φ.Figure 2.

Bottom Line: Using data generated by next-generation sequencing technologies, we have analysed the exome sequences of brain tumour patients with matched tumour 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.Moreover, the prediction of a mutation associated with 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.

View Article: PubMed Central - PubMed

Affiliation: NSF Computational Center of Research Excellence and NASA University Research Center for Aerospace Device , NCCU, Durham, NC, USA ; Yerevan Physics Institute and Yerevan State University , Yerevan, Armenia.

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 analysed the exome sequences of brain tumour patients with matched tumour 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 diagnostics. Moreover, the prediction of a mutation associated with 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.

No MeSH data available.


Related in: MedlinePlus