A Stochastic Phylogenetic Algorithm for Mitochondrial DNA Analysis

DEGBOE: Discrete time Evolution modeling of Gene mutation through Bayesian inference using qualitative Observation of mutation Events

An important aspect of cancer progression concerns the way in which gene mutations accumulate in cellular lineages. Comprehensive efforts into cataloging cancer genes have revealed that tumors demonstrate variability in genes that accumulate mutations which depend on the presence or absence of other mutations. However, understanding the stochastic process by which mutations arise across the genome is an important open problem of this nature in biology due to modeling discrete variate time-series is the most challenging, and, as yet, least well-developed of all areas of research in time-series. In this paper, a DEGBOE framework is proposed to model the mutation time-series given the sequence data of the gene mutations. The method relates the discrete-time, nonlinear and nonstationary series of gene mutations to the time-varying autoregressive moving average model. It presents the observation as a nonlinear function dependent on two variables: gene mutation, and gene-gene interactions characterizing the effects of the varying presence or absence of other gene mutations on a mutations' occurrence and evolution. DEGBOE is applied to model the dynamics of frequently mutated genes in lung cancer, includingEGFR,KRAS, and TP53. The results of the model are analyzed and compared to the original simulated data of theDNAwalk, and experimental lung cancer mutations data. It identifies the driver role of TP53 mutations in lung cancer progression.

Spatial constrains and information content of sub-genomic regions of the human genome

Complexity metrics and machine learning (ML) models have been utilized to analyze the lengths of segmental genomic entities of DNA sequences (exonic, intronic, intergenic, repeat, unique) with the purpose to ask questions regarding the segmental organization of the human genome within the size distribution of these sequences. For this we developed an integrated methodology that is based upon the reconstructed phase space theorem, the non-extensive statistical theory of Tsallis, ML techniques, and a technical index, integrating the generated information, which we introduce and named complexity factor (COFA). Our analysis revealed that the size distribution of the genomic regions within chromosomes are not random but follow patterns with characteristic features that have been seen through its complexity character, and it is part of the dynamics of the whole genome. Finally, this picture of dynamics in DNA is recognized using ML tools for clustering, classification, and prediction with high accuracy.

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The lengths of DNA subgenomic entities satisfied the Tsallis non-extensive statistics

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The size distribution of the subgenomic entities within chromosomes follow specific patterns

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A technical index COFA was introduced to characterize the degree of complexity

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The degree of complexity behavior in DNA is identifiable using ML approaches