The DNA walk and its demonstration of deterministic chaos—relevance to genomic alterations in lung cancer

Hewelt, Blake; Li, Haiqing; Jolly, Mohit Kumar; Kulkarni, Prakash; Mambetsariev, Isa; Salgia, Ravi

doi:10.1093/bioinformatics/bty1021

Cited by 10 publications

(20 citation statements)

References 38 publications

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“…To convert the DNA sequence into a numeric sequence that can be used as input for DDA we chose the 1D DNA walk representation in which the walker takes a step upwards if the nucleotide is a pyrimidine and downwards if it is a purine (38). DNA walks have been used for instance to identify long-range power-law correlations, periodicities and various fractal properties of genomic sequences (12, 39, 40).…”

Section: Methodsmentioning

confidence: 99%

“…Here we present DNA-DDA, a method that relies on the principles of nonlinear dynamics to predict A/B compartments from the DNA sequence alone. Nonlinear dynamical systems have fractals and chaos among their paradigms, both widespread in biological systems (10)(11)(12). The genome-wide contact maps obtained from Hi-C experiments have been shown to be (bi-)fractal (13), and their intricate self-similar patterns bear the hallmarks of chaos.…”

Section: Introductionmentioning

confidence: 99%

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Predicting 3D genome architecture directly from the nucleotide sequence with DNA-DDA

Lainscsek

Taher

2022

Preprint

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3D genome architecture is characterized by multi-scale patterns and plays an essential role in proper cellular function. Chromatin conformation capturing experiments have revealed many properties underlying 3D genome architecture. However, they are complex, costly, and time consuming, and therefore only a limited number of cell types have been examined using these techniques. Increasing effort is being directed towards deriving computational methods that can predict chromatin conformation and associated structures. Here we present DNA-DDA, a purely sequence-based method that relies on nonlinear dynamics to predict genome-wide chromatin contacts and A/B compartments. We show that the DNA sequence and A/B compartment labels of a 20Mbp-long region of the human genome is sufficient to predict the 3D architecture of the entire genome. Although this is a proof-of-concept study, our method shows promise in elucidating the mechanisms responsible for genome folding as well as modeling the impact of genetic variation on 3D genome architecture and the processes regulated thereby.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predicting 3D genome architecture directly from the nucleotide sequence with DNA-DDA

Lainscsek

Taher

2022

Preprint

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“…All aforementioned advantages for sequences visualization motivate researchers to visualize bio sequences in the form of 2D or 3D images [15,[30][31][32]. Theoretically, all kinds of K-mer histogram of sequences [6,8,30], one hot representation, representation methods based on physicochemical properties [33], representation methods based on a combination vector of several types of information [34], and DNA-walk representation can be adopted to encode biological sequences as 2D images.…”

Section: Introductionmentioning

confidence: 99%

“…To preserve spatial information of the sequence, DNA-walk [32][33][34] has been proposed, which produces an image containing an observable and recognizable pattern. In general, in this method, each nucleotide is represented as a two-dimensional vector, although it can be…”

Section: Introductionmentioning

confidence: 99%

WalkIm: Compact image-based encoding for high-performance classification of biological sequences using simple tuning-free CNNs

2022

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The classification of biological sequences is an open issue for a variety of data sets, such as viral and metagenomics sequences. Therefore, many studies utilize neural network tools, as the well-known methods in this field, and focus on designing customized network structures. However, a few works focus on more effective factors, such as input encoding method or implementation technology, to address accuracy and efficiency issues in this area. Therefore, in this work, we propose an image-based encoding method, called as WalkIm, whose adoption, even in a simple neural network, provides competitive accuracy and superior efficiency, compared to the existing classification methods (e.g. VGDC, CASTOR, and DLM-CNN) for a variety of biological sequences. Using WalkIm for classifying various data sets (i.e. viruses whole-genome data, metagenomics read data, and metabarcoding data), it achieves the same performance as the existing methods, with no enforcement of parameter initialization or network architecture adjustment for each data set. It is worth noting that even in the case of classifying high-mutant data sets, such as Coronaviruses, it achieves almost 100% accuracy for classifying its various types. In addition, WalkIm achieves high-speed convergence during network training, as well as reduction of network complexity. Therefore WalkIm method enables us to execute the classifying neural networks on a normal desktop system in a short time interval. Moreover, we addressed the compatibility of WalkIm encoding method with free-space optical processing technology. Taking advantages of optical implementation of convolutional layers, we illustrated that the training time can be reduced by up to 500 time. In addition to all aforementioned advantages, this encoding method preserves the structure of generated images in various modes of sequence transformation, such as reverse complement, complement, and reverse modes.

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“…[26],[27],[30]–[33]. For instance, DNA walks are recently applied to diagnose cancers following gene mutations [34],[35]. It was found that the mutated areas of DNA chains show a higher level of randomness, and it is measured by calculating the fractal dimension of walks.…”

Section: Introductionmentioning

confidence: 99%

Visual and Quantitative Analyses of Virus Genomic Sequences using a Metric-based Algorithm

Belinsky¹,

Kouzaev

2021

Preprint

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The worldwide spread of SARS-CoV-2 virus increases interest in the research of virus genomics and the creation of more advanced study methods. This work aims to develop a new fast DNA walk algorithm for one-dimensional visualization of RNAs based on a big-data method and comparative examination of several viruses and their lines and strains. In this work, a new metric-based algorithm for quantitative and visual analyses of RNAs is proposed and considered. It allows finding any fragments of genomic sequences using the Hamming distance between the binary-expressed RNA characters and symbols of a fragment under the search and building one-dimensional trajectories of genomic walks convenient for quantitative and qualitative analyses of RNAs and DNAs. Similarly, human-language texts can be processed and compared with genomic sequences.This algorithm is used to investigate the complete genomic sequences of SARS CoV-2, MERS, Dengue, and Ebola viruses available from Genbank and GISAID databases. The distributions of atg codon-starting triplets along with these sequences are built and considered as their atg-schemes. Additionally to the atg-walks, single-symbols distributions are calculated to detect the codon-content mutations, which do not change the atg-triplet coordinates along with genomic sequences. The visual analyses of distributions consisting of several hundred triplets enable us to define the level of stability of RNAs towards essential mutations and perform their classing. Statistical studies are applied to distributions of the inter-atg and inter-symbol distances along with genomic sequences. The fractal dimension values of these distributions are calculated, enabling them to correspond to the mutations discovered by Hamming walks and fractal-dimension values of several ten virus samples investigated here. The developed metric-based-based algorithm allows building one-dimensional RNA schemes of different scale levels and effectively analyzing the virus mutations with their classing.

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The DNA walk and its demonstration of deterministic chaos—relevance to genomic alterations in lung cancer

Cited by 10 publications

References 38 publications

Predicting 3D genome architecture directly from the nucleotide sequence with DNA-DDA

Predicting 3D genome architecture directly from the nucleotide sequence with DNA-DDA

WalkIm: Compact image-based encoding for high-performance classification of biological sequences using simple tuning-free CNNs

Visual and Quantitative Analyses of Virus Genomic Sequences using a Metric-based Algorithm

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