Graphical Representation Of DNA Sequences Research Articles

Numerical Characterization of DNA Sequences for Alignment-free Sequence Comparison - A Review.

Biological macromolecules, namely, DNA, RNA, and protein, have their building blocks organized in a particular sequence and the sequential arrangement encodes the evolutionary history of the organism (species). Hence, biological sequences have been used for studying evolutionary relationships among the species. This is usually carried out by Multiple Sequence Algorithms (MSA). Due to certain limitations of MSA, alignment-free sequence comparison methods were developed. The present review is on alignment-free sequence comparison methods carried out using the numerical characterization of DNA sequences. The graphical representation of DNA sequences by chaos game representation and other 2-dimensional and 3-dimensional methods are discussed. The evolution of numerical characterization from the various graphical representations and the application of the DNA invariants thus computed in phylogenetic analysis are presented. The extension of computing molecular descriptors in chemometrics to the calculation of a new set of DNA invariants and their use in alignment-free sequence comparison in an N-dimensional space and construction of phylogenetic trees are also reviewed. The phylogenetic tress constructed by the alignment-free sequence comparison methods using DNA invariants were found to be better than those constructed using alignment-based tools such as PHLYIP and ClustalW. One of the graphical representation methods is now extended to study viral sequences of infectious diseases for the identification of conserved regions to design peptidebased vaccines by combining numerical characterization and graphical representation.

Estimation of Similarity between DNA S equence s and Its Graphical Representation

Bioinformatics, which is now a well known field of study, originated in the context of biological sequence analysis. Recently graphical representation takes place for the research on DNA sequence. Research in biological sequence is mainly based on the function and its structure. Bioinformatics finds wide range of applications specifically in the domain of molecular biology which focuses on the analysis of molecules viz. DNA, RNA, Protein etc. In this review, we mainly deal with the similarity analysis between sequences and graphical representation of DNA sequence.

2D Graphical Representation of DNA Sequences Based on Variant Map

Mining and analyzing the information and structure of DNA sequences is an important method of exploring and explaining the mysteries of biology. In addition to traditional biological detection methods for DNA sequences, technological advances have allowed researchers to utilize computers to analyze DNA sequences, such as in DNA visualization. However, DNA visualization techniques have disadvantages, such as difficult operation and complex calculation. Therefore, in this paper, we propose an algorithm called VARCH based on a variant map that is a part of variant logic construction. VARCH is a high-efficiency and concise visualization algorithm for DNA sequences. The principle of this approach is that the number of base combinations in each DNA subsequence is counted, and a matrix is constructed based on the full arrangement of different base combinations. Finally, the matrix is mapped to the 2D coordinate plane, obtaining the image of DNA sequences. The experimental results demonstrate that VARCH can effectively display features of DNA sequences.

A New Numerical Method for DNA Sequence Analysis Based on 8-Dimensional Vector Representation

Background: The multiple sequence alignment (MSA) algorithms are the traditional ways to compare and analyze DNA sequences. However, for large DNA sequences, these algorithms require a long time computationally. Objective: Here we will propose a new numerical method to characterize and compare DNA sequences quickly. Method: Based on a new 2-dimensional (2D) graphical representation of DNA sequences, we can obtain an 8-dimensional vector using two basic concepts of probability, the mean and the variance. Results: We perform similarity/dissimilarity analyses among two real DNA data sets, the coding sequences of the first exon of beta-globin gene of 11 species and 31 mammalian mitochondrial genomes, respectively. Conclusion: Our results are in agreement with the existing analyses in our literatures. We also compare our approach with other methods and find that ours is more effective.

One novel representation of DNA sequence based on the global and local position information

One novel representation of DNA sequence combining the global and local position information of the original sequence has been proposed to distinguish the different species. First, for the sufficient exploitation of global information, one graphical representation of DNA sequence has been formulated according to the curve of Fermat spiral. Then, for the consideration of local characteristics of DNA sequence, attaching each point in the curve of Fermat spiral with the related mass has been applied based on the relationships of neighboring four nucleotides. In this paper, the normalized moments of inertia of the curve of Fermat spiral which composed by the points with mass has been calculated as the numerical description of the corresponding DNA sequence on the first exons of beta-global genes. Choosing the Euclidean distance as the measurement of the numerical descriptions, the similarity between species has shown the performance of proposed method.

2D graphical representation of dna sequence based on horizon lines from a probabilistic view

2D graphical representation of dna sequence based on horizon lines from a probabilistic view

Spectral-dynamic representation of DNA sequences

A graphical representation of DNA sequences in which the distribution of a particular base B=A,C,G,T is represented by a set of discrete lines has been formulated. The methodology of this approach has been borrowed from two areas of physics: spectroscopy and dynamics. Consequently, the set of discrete lines is referred to as the B-spectrum. Next, the B-spectrum is transformed to a rigid body composed of material points. In this way a dynamic representation of the DNA sequence has been obtained. The centers of mass of these rigid bodies, divided by their moments of inertia, have been taken as the descriptors of the spectra and, thus, of the DNA sequences. The performance of this method on a standard set of data commonly applied by authors introducing new approaches to bioinformatics (the first exons of β-globin genes of different species) proved to be very good.

DUC-Curve, a highly compact 2D graphical representation of DNA sequences and its application in sequence alignment

A highly compact and simple 2D graphical representation of DNA sequences, named DUC-Curve, is constructed through mapping four nucleotides to a unit circle with a cyclic order. DUC-Curve could directly detect nucleotide, di-nucleotide compositions and microsatellite structure from DNA sequences. Moreover, it also could be used for DNA sequence alignment. Taking geometric center vectors of DUC-Curves as sequence descriptor, we perform similarity analysis on the first exons of β-globin genes of 11 species, oncogene TP53 of 27 species and twenty-four Influenza A viruses, respectively. The obtained reasonable results illustrate that the proposed method is very effective in sequence comparison problems, and will at least play a complementary role in classification and clustering problems.

Similarity Estimation Between DNA Sequences Based on Local Pattern Histograms of Binary Images

Graphical representation of DNA sequences is one of the most popular techniques for alignment-free sequence comparison. Here, we propose a new method for the feature extraction of DNA sequences represented by binary images, by estimating the similarity between DNA sequences using the frequency histograms of local bitmap patterns of images. Our method shows linear time complexity for the length of DNA sequences, which is practical even when long sequences, such as whole genome sequences, are compared. We tested five distance measures for the estimation of sequence similarities, and found that the histogram intersection and Manhattan distance are the most appropriate ones for phylogenetic analyses.

Novel Graphical Representation and Numerical Characterization of DNA Sequences

Modern sequencing technique has provided a wealth of data on DNA sequences, which has made the analysis and comparison of sequences a very important but difficult task. In this paper, by regarding the dinucleotide as a 2-combination of the multiset { ∞ · A , ∞ · G , ∞ · C , ∞ · T } , a novel 3-D graphical representation of a DNA sequence is proposed, and its projections on planes (x,y), (y,z) and (x,z) are also discussed. In addition, based on the idea of “piecewise function”, a cell-based descriptor vector is constructed to numerically characterize the DNA sequence. The utility of our approach is illustrated by the examination of phylogenetic analysis on four datasets.

A Novel Numerical Characterization for Graphical Representations of DNA Sequences

A Novel Numerical Characterization for Graphical Representations of DNA Sequences

Two Dimensional Yau-Hausdorff Distance with Applications on Comparison of DNA and Protein Sequences.

Comparing DNA or protein sequences plays an important role in the functional analysis of genomes. Despite many methods available for sequences comparison, few methods retain the information content of sequences. We propose a new approach, the Yau-Hausdorff method, which considers all translations and rotations when seeking the best match of graphical curves of DNA or protein sequences. The complexity of this method is lower than that of any other two dimensional minimum Hausdorff algorithm. The Yau-Hausdorff method can be used for measuring the similarity of DNA sequences based on two important tools: the Yau-Hausdorff distance and graphical representation of DNA sequences. The graphical representations of DNA sequences conserve all sequence information and the Yau-Hausdorff distance is mathematically proved as a true metric. Therefore, the proposed distance can preciously measure the similarity of DNA sequences. The phylogenetic analyses of DNA sequences by the Yau-Hausdorff distance show the accuracy and stability of our approach in similarity comparison of DNA or protein sequences. This study demonstrates that Yau-Hausdorff distance is a natural metric for DNA and protein sequences with high level of stability. The approach can be also applied to similarity analysis of protein sequences by graphic representations, as well as general two dimensional shape matching.

An Improved Mathematical Object for Graphical Representation of DNA Sequences

An improved mathematical object is presented for analyzing the similarity and dissimilarity of DNA sequences. The kernel of it is to employ the changes of the x-coordinates and y-coordinates of the points corresponding to the bases of DNA sequence in the method of graphical representation, respectively. Compared with the traditional methods based on the distances between points, a more detailed consideration on the measurement between points is made in this new method, so more information of DNA sequence is exploited. It can distinguish any form of symmetrical curves corresponding to DNA sequences, and this advantage can hardly be achieved by traditional methods. Furthermore, the contrast experiments show that this method increases the precision by about 50%. Keywords: DNA sequence, graphical representation, mathematical object, sequence comparison, similarity/dissimilarity matrix, x, y-coordinates.

Mapping the space of genomic signatures.

We propose a computational method to measure and visualize interrelationships among any number of DNA sequences allowing, for example, the examination of hundreds or thousands of complete mitochondrial genomes. An "image distance" is computed for each pair of graphical representations of DNA sequences, and the distances are visualized as a Molecular Distance Map: Each point on the map represents a DNA sequence, and the spatial proximity between any two points reflects the degree of structural similarity between the corresponding sequences. The graphical representation of DNA sequences utilized, Chaos Game Representation (CGR), is genome- and species-specific and can thus act as a genomic signature. Consequently, Molecular Distance Maps could inform species identification, taxonomic classifications and, to a certain extent, evolutionary history. The image distance employed, Structural Dissimilarity Index (DSSIM), implicitly compares the occurrences of oligomers of length up to k (herein k = 9) in DNA sequences. We computed DSSIM distances for more than 5 million pairs of complete mitochondrial genomes, and used Multi-Dimensional Scaling (MDS) to obtain Molecular Distance Maps that visually display the sequence relatedness in various subsets, at different taxonomic levels. This general-purpose method does not require DNA sequence alignment and can thus be used to compare similar or vastly different DNA sequences, genomic or computer-generated, of the same or different lengths. We illustrate potential uses of this approach by applying it to several taxonomic subsets: phylum Vertebrata, (super)kingdom Protista, classes Amphibia-Insecta-Mammalia, class Amphibia, and order Primates. This analysis of an extensive dataset confirms that the oligomer composition of full mtDNA sequences can be a source of taxonomic information. This method also correctly finds the mtDNA sequences most closely related to that of the anatomically modern human (the Neanderthal, the Denisovan, and the chimp), and that the sequence most different from it in this dataset belongs to a cucumber.

4D Graphical representation research of DNA sequences

Graphical representation of DNA sequences is a key component in studying biological problems. In order to gain new insights in DNA sequences, this paper combined the digitized methods of single-base, base pairs and coding in triplet bases with the times of base appearing, and then a novel 4D graphical representation method of DNA sequences was put forward. It was a one-to-one correspondence of the arbitrary DNA sequence and 4D graphical representation, that avoided causing non-unique 4D graphical representation and overlapping lines. The method could reflect the biological information features of DNA sequence more comprehensively and effectively without any losses. Based on the 4D graphical representation, we used the geometric center of 4D graphical representation as eigenvalue of DNA sequences analyses, which kept the original features of the data, and then established the Euclidean distances and included angles between vectors' terminal point for similarity analyses of the first extron of the beta-globulin gene among 11 species. Finally, we established the graph of systematic hierarchical cluster analysis of 11 species to observe more easily the relationship between species. A positive outcome was reached, and the results were in accord with biological taxonomy, which also supported the rationality and effectiveness of the novel 4D graphical representation.

A novel representation of DNA sequence based on CMI coding

Graphical representation of DNA sequences provides a simple and intuitive way of analyzing and sorting various gene sequences. It is attractive to researchers to propose much more appropriate methods. In this study, a new graphical representation is presented. The method adopts the CMI coding to represent four nucleotides-A, G, C and T. Our approach considers not only the sequences’ structure but also the chemical structure for DNA sequence. We take several sets of data to test our method. The results of our experiment demonstrate that our representation is effective.

A Novel Circular Mapping Technique for Spectral Classification of Exons and Introns in Human DNA Sequences

Signals that represent information may be classified into two forms: numeric and symbolic. Symbolic signals such as DNA symbolic sequences cannot be directly processed with digital signal processing (DSP) techniques. The only way to apply DSP in genomic field is the mapping of DNA symbolic sequences to numerical sequences. Hence, biological properties are reflected in a numerical domain. This opens a field to present a set of tools for solving genomic problems. In literature many techniques have been developed for numerical representation of DNA sequences. The main drawback of these techniques is that each nucleotide is represented by a numerical value depending on nucleotide type only ignoring its position in codon and DNA sequence. In this paper a new approach for DNA symbolic to numeric representation called Circular Mapping (CM) is introduced. Its based on graphical representation of DNA sequence that maps each nucleotide by a complex numerical value depending not only on nucleotide type but also on its position in codons. The main applications of this method are the gene prediction that aims to locate the protein-coding regions and the classification of exons and introns in DNA sequences. The proposed approach showed significant improvement in exons and introns classification as compared with the existing techniques. The efficiency of this method in classification depends on the right choice of the mapping angle ( ) as indicated by the power spectral analysis results over the sequences of the human genome (GRch37/hg19).

3D-dynamic representation of DNA sequences

A new 3D graphical representation of DNA sequences is introduced. This representation is called 3D-dynamic representation. It is a generalization of the 2D-dynamic dynamic representation. The sequences are represented by sets of “material points” in the 3D space. The resulting 3D-dynamic graphs are treated as rigid bodies. The descriptors characterizing the graphs are analogous to the ones used in the classical dynamics. The classification diagrams derived from this representation are presented and discussed. Due to the third dimension, “the history of the graph” can be recognized graphically because the 3D-dynamic graph does not overlap with itself. Specific parts of the graphs correspond to specific parts of the sequence. This feature is essential for graphical comparisons of the sequences. Numerically, both 2D and 3D approaches are of high quality. In particular, a difference in a single base between two sequences can be identified and correctly described (one can identify which base) by both 2D and 3D methods.

A 2D graphical representation of the sequences of DNA based on triplets and its application.

In this paper, we first present a new concept of ‘weight’ for 64 triplets and define a different weight for each kind of triplet. Then, we give a novel 2D graphical representation for DNA sequences, which can transform a DNA sequence into a plot set to facilitate quantitative comparisons of DNA sequences. Thereafter, associating with a newly designed measure of similarity, we introduce a novel approach to make similarities/dissimilarities analysis of DNA sequences. Finally, the applications in similarities/dissimilarities analysis of the complete coding sequences of β-globin genes of 11 species illustrate the utilities of our newly proposed method.

A Novel 2-D Graphical Representation for DNA Sequences Based on the Chemical Properties

A Novel 2-D Graphical Representation for DNA Sequences Based on the Chemical Properties