Similarity/dissimilarity calculation methods of DNA sequences: A survey

Use of 2D FFT and DTW in Protein Sequence Comparison

Protein sequence comparison remains a challenging work for the researchers owing to the computational complexity due to the presence of 20 amino acids compared with only four nucleotides in Genome sequences. Further, protein sequences of different species are of different lengths; it throws additional changes to the researchers to develop methods, specially alignment-free methods, to compare protein sequences. In this work, an efficient technique to compare protein sequences is developed by a graphical representation. First, the classified grouping of 20 amino acids with a cardinality of 4 based on polar class is considered to narrow down the representational range from 20 to 4. Then a unit vector technique based on a two-quadrant Cartesian system is proposed to provide a new two-dimensional graphical representation of the protein sequence. Now, two approaches are proposed to cope with the varying lengths of protein sequences from various species: one uses Dynamic Time Warping (DTW), while the other one uses a two-dimensional Fast Fourier Transform (2D FFT). Next, the effectiveness of these two techniques is analyzed using two evaluation criteria-quantitative measures based on symmetric distance (SD) and computational speed. An analysis is performed on five data sets of 9 ND4, 9 ND5, 9 ND6, 12 Baculovirus, and 24 TF proteins under the two methods. It is found that the FFT-based method produces the same results as DTW but in less computational time. It is found that the result of the proposed method agrees with the known biological reference. Further, the present method produces better clustering than the existing ones.

Classification Maps: A New Mathematical Tool Supporting the Diagnosis of Age-Related Macular Degeneration

OBJECTIVE

A new diagnostic graphical tool-classification maps-supporting the detection of Age-Related Macular Degeneration (AMD) has been constructed.

METHODS

The classification maps are constructed using the ordinal regression model. In the ordinal regression model, the ordinal variable (the dependent variable) is the degree of the advancement of AMD. The other variables, such as CRT (Central Retinal Thickness), GCC (Ganglion Cell Complex), MPOD (Macular Pigment Optical Density), ETDRS (Early Treatment Diabetic Retinopathy Study), Snellen and Age have also been used in the analysis and are represented on the axes of the maps.

RESULTS

Here, 132 eyes were examined and classified to the AMD advancement level according to the four-point Age-Related Eye Disease Scale (AREDS): AREDS 1, AREDS 2, AREDS 3 and AREDS 4. These data were used for the creation of two-dimensional classification maps for each of the four stages of AMD.

CONCLUSIONS

The maps allow us to perform the classification of the patient's eyes to particular stages of AMD. The pairs of the variables represented on the axes of the maps can be treated as diagnostic identifiers necessary for the classification to particular stages of AMD.

Getting the bugs out of AI: Advancing ecological research on arthropods through computer vision

Deep learning for computer vision has shown promising results in the field of entomology, however, there still remains untapped potential. Deep learning performance is enabled primarily by large quantities of annotated data which, outside of rare circumstances, are limited in ecological studies. Currently, to utilize deep learning systems, ecologists undergo extensive data collection efforts, or limit their problem to niche tasks. These solutions do not scale to region agnostic models. However, there are solutions that employ data augmentation, simulators, generative models, and self-supervised learning that can supplement limited labelled data. Here, we highlight the success of deep learning for computer vision within entomology, discuss data collection efforts, provide methodologies for optimizing learning from limited annotations, and conclude with practical guidelines for how to achieve a foundation model for entomology capable of accessible automated ecological monitoring on a global scale.

A fast and efficient algorithm for DNA sequence similarity identification

DNA sequence similarity analysis is necessary for enormous purposes including genome analysis, extracting biological information, finding the evolutionary relationship of species. There are two types of sequence analysis which are alignment-based (AB) and alignment-free (AF). AB is effective for small homologous sequences but becomes NP-hard problem for long sequences. However, AF algorithms can solve the major limitations of AB. But most of the existing AF methods show high time complexity and memory consumption, less precision, and less performance on benchmark datasets. To minimize these limitations, we develop an AF algorithm using a 2D \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$k-mer$$\end{document}k-mer count matrix inspired by the CGR approach. Then we shrink the matrix by analyzing the neighbors and then measure similarities using the best combinations of pairwise distance (PD) and phylogenetic tree methods. We also dynamically choose the value of k for \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$k-mer$$\end{document}k-mer. We develop an efficient system for finding the positions of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$k-mer$$\end{document}k-mer in the count matrix. We apply our system in six different datasets. We achieve the top rank for two benchmark datasets from AFproject, 100% accuracy for two datasets (16 S Ribosomal, 18 Eutherian), and achieve a milestone for time complexity and memory consumption in comparison to the existing study datasets (HEV, HIV-1). Therefore, the comparative results of the benchmark datasets and existing studies demonstrate that our method is highly effective, efficient, and accurate. Thus, our method can be used with the top level of authenticity for DNA sequence similarity measurement.

4D-Dynamic Representation of DNA/RNA Sequences: Studies on Genetic Diversity of Echinococcus multilocularis in Red Foxes in Poland

The 4D-Dynamic Representation of DNA/RNA Sequences, an alignment-free bioinformatics method recently developed by us, has been used to study the genetic diversity of Echinococcus multilocularis in red foxes in Poland. Sequences of three mitochondrial genes, i.e., NADH dehydrogenase subunit 2 (nad2), cytochrome b (cob), and cytochrome c oxidase subunit 1 (cox1), are analyzed. The sequences are represented by sets of material points in a 4D space, i.e., 4D-dynamic graphs. As a visualization of the sequences, projections of the graphs into 3D space are shown. The differences between 3D graphs corresponding to European, Asian, and American haplotypes are small. Numerical characteristics (sequence descriptors) applied in the studies can recognize the differences. The concept of creating descriptors of 4D-dynamic graphs has been borrowed from classical dynamics; these are coordinates of the centers or mass and moments of inertia of 4D-dynamic graphs. Based on these descriptors, classification maps are constructed. The concentrations of points in the maps indicate one Polish haplotype (EmPL9) of Asian origin.

Non-standard bioinformatics characterization of SARS-CoV-2

A non-standard bioinformatics method, 4D-Dynamic Representation of DNA/RNA Sequences, aiming at an analysis of the information available in nucleotide databases, has been formulated. The sequences are represented by sets of "material points" in a 4D space - 4D-dynamic graphs. The graphs representing the sequences are treated as "rigid bodies" and characterized by values analogous to the ones used in the classical dynamics. As the graphical representations of the sequences, the projections of the graphs into 2D and 3D spaces are used. The method has been applied to an analysis of the complete genome sequences of the 2019 novel coronavirus. As a result, 2D and 3D classification maps are obtained. The coordinate axes in the maps correspond to the values derived from the exact formulas characterizing the graphs: the coordinates of the centers of mass and the 4D moments of inertia. The points in the maps represent sequences and their coordinates are used as the classifiers. The main result of this work has been derived from the 3D classification maps. The distribution of clusters of points which emerged in these maps, supports the hypothesis that SARS-CoV-2 may have originated in bat and in pangolin. Pilot calculations for Zika virus sequence data prove that the proposed approach is also applicable to a description of time evolution of genome sequences of viruses.

Genetic Similarity Analysis Based on Positive and Negative Sequence Patterns of DNA

Similarity analysis of DNA sequences can clarify the homology between sequences and predict the structure of, and relationship between, them. At the same time, the frequent patterns of biological sequences explain not only the genetic characteristics of the organism, but they also serve as relevant markers for certain events of biological sequences. However, most of the aforementioned biological sequence similarity analysis methods are targeted at the entire sequential pattern, which ignores the missing gene fragment that may induce potential disease. The similarity analysis of such sequences containing a missing gene item is a blank. Consequently, some sequences with missing bases are ignored or not effectively analyzed. Thus, this paper presents a new method for DNA sequence similarity analysis. Using this method, we first mined not only positive sequential patterns, but also sequential patterns that were missing some of the base terms (collectively referred to as negative sequential patterns). Subsequently, we used these frequent patterns for similarity analysis on a two-dimensional plane. Several experiments were conducted in order to verify the effectiveness of this algorithm. The experimental results demonstrated that the algorithm can obtain various results through the selection of frequent sequential patterns and that accuracy and time efficiency was improved.

A Generalized Iterative Map for Analysis of Protein Sequences

AIM AND OBJECTIVE

The similarities comparison of biological sequences is an important task in bioinformatics. The methods of the similarities comparison for biological sequences are divided into two classes: sequence alignment method and alignment-free method. The graphical representation of biological sequences is a kind of alignment-free method, which constitutes a tool for analyzing and visualizing the biological sequences. In this article, a generalized iterative map of protein sequences was suggested to analyze the similarities of biological sequences.

MATERIALS AND METHODS

Based on the normalized physicochemical indexes of 20 amino acids, each amino acid can be mapped into a point in 5D space. A generalized iterative function system was introduced to outline a generalized iterative map of protein sequences, which can not only reflect various physicochemical properties of amino acids but also incorporate with different compression ratios of the component of a generalized iterative map. Several properties were proved to illustrate the advantage of the generalized iterative map. The mathematical description of the generalized iterative map was suggested to compare the similarities and dissimilarities of protein sequences. Based on this method, similarities/dissimilarities were compared among ND5 protein sequences, as well as ND6 protein sequences of ten different species.

RESULTS

By correlation analysis, the ClustalW results were compared with our similarity/dissimilarity results and other graphical representation results to show the utility of our approach. The comparison results show that our approach has better correlations with ClustalW for all species than other approaches and illustrate the effectiveness of our approach.

CONCLUSION

Two examples show that our method not only has good performances and effects in the similarity/dissimilarity analysis of protein sequences but also does not require complex computation.

A novel alignment-free DNA sequence similarity analysis approach based on top-k n-gram match-up

DNA sequence similarity analysis is an essential task in computational biology and bioinformatics. In nearly all research that explores evolutionary relationships, gene function analysis, protein structure prediction and sequence retrieving, it is necessary to perform similarity calculations. As an alternative to alignment-based sequence comparison methods, which result in high computational cost, alignment-free methods have emerged that calculate similarity by digitizing the sequence in a different space. In this paper, we proposed an alignment-free DNA sequence similarity analysis method based on top-k n-gram matches, with the prediction that common repeating DNA subsections indicate high similarity between DNA sequences. In our method, we determined DNA sequence similarities by measuring similarity among feature vectors created according to top-k n-gram match-up scores without the use of similarity functions. We applied the similarity calculation for three different DNA data sets of different lengths. The phylogenetic relationships revealed by our method show that our trees coincide almost completely with the results of the MEGA software, which is based on sequence alignment. Our findings show that a certain number of frequently recurring common sequence patterns have the power to characterize DNA sequences.

DNA sequence similarity analysis using image texture analysis based on first-order statistics

Similarity is one of the key processes of DNA sequence analysis in computational biology and bioinformatics. In nearly all research that explores evolutionary relationships, gene function analysis, protein structure prediction and sequence retrieving, it is necessary to perform similarity calculations. One major task in alignment-free DNA sequence similarity calculations is to develop novel mathematical descriptors for DNA sequences. In this paper, we present a novel approach to DNA sequence similarity analysis studies using similarity calculations of texture images. Texture analysis methods, which are a subset of digital image processing methods, are used here with the assumption that these calculations can be adapted to alignment-free DNA sequence similarity analysis methods. Gray-level textures were created by the values assigned to the nucleotides in the DNA sequences. Similarity calculations were made between these textures using histogram-based texture analyses based on first-order statistics. We obtained texture features for 3 different DNA data sets of different lengths, and calculated the similarity matrices. The phylogenetic relationships revealed by our method shows our trees to be similar to the results of the MEGA software, which is based on sequence alignment. Our findings show that texture analysis metrics can be used to characterize DNA sequences.

A Statistical Similarity/Dissimilarity Analysis of Protein Sequences Based on a Novel Group Representative Vector

Similarity/dissimilarity analysis is a key way of understanding the biology of an organism by knowing the origin of the new genes/sequences. Sequence data are grouped in terms of biological relationships. The number of sequences related to any group is susceptible to be increased every day. All the present alignment-free methods approve the utility of their approaches by producing a similarity/dissimilarity matrix. Although this matrix is clear, it measures the degree of similarity among sequences individually. In our work, a representative of each of three groups of protein sequences is introduced. A similarity/dissimilarity vector is evaluated instead of the ordinary similarity/dissimilarity matrix based on the group representative. The approach is applied on three selected groups of protein sequences: beta globin, NADH dehydrogenase subunit 5 (ND5), and spike protein sequences. A cross-grouping comparison is produced to ensure the singularity of each group. A qualitative comparison between our approach, previous articles, and the phylogenetic tree of these protein sequences proved the utility of our approach.

Measuring Similarity among Protein Sequences Using a New Descriptor

The comparison of protein sequences according to similarity is a fundamental aspect of today's biomedical research. With the developments of sequencing technologies, a large number of protein sequences increase exponentially in the public databases. Famous sequences' comparison methods are alignment based. They generally give excellent results when the sequences under study are closely related and they are time consuming. Herein, a new alignment-free method is introduced. Our technique depends on a new graphical representation and descriptor. The graphical representation of protein sequence is a simple way to visualize protein sequences. The descriptor compresses the primary sequence into a single vector composed of only two values. Our approach gives good results with both short and long sequences within a little computation time. It is applied on nine beta globin, nine ND5 (NADH dehydrogenase subunit 5), and 24 spike protein sequences. Correlation and significance analyses are also introduced to compare our similarity/dissimilarity results with others' approaches, results, and sequence homology.