A Novel Method to Analyze the Similarity of Biological Sequences

Abstract

In this paper, we propose a new method based on the 2-D graphical representation to analyze the similarity of biological sequences and classify the protein secondary structure sequences. Instead of computing some characteristics from the distance matrix, the average area surrounded by the curve and X axis is computed as a new invariant. The new method is tested on two sets: the coding sequences of 30 mitochondrial genes from NCBI and 12 protein secondary structure sequences. The similarity/disimilarity and phylogenetic tree (dendrogram) of these sequences verify the validity of our method.     

A new 2-D graphical representation of DNA sequences and their numerical characterization

We consider a novel 2-D graphical representation of DNA sequences according to chemical structures of bases, reflecting distribution of bases with different chemical structure, preserving information on sequential adjacency of bases, and allowing numerical characterization. The representation avoids loss of information accompanying alternative 2-D representations in which the curve standing for DNA overlaps and intersects itself. Based on this representation we present a numerical characterization approach by the leading eigenvalues of the matrices associated with the DNA sequences. The utility of the approach is illustrated on the coding sequences of the first exon of human beta-globin gene.     

基于3-D图形表示的DNA序列的相似性分析

基于DNA序列的3D图形表示,通过L／L矩阵的规范化最大特征值组成的3维向量来刻画了DNA序列,并基于这种方法,用β-globin基因的第一个外显子分析了11个物种的相似性问题。     

A 3D graphical representation of RNA secondary structures

In this paper, we proposed a 3-D graphical representation of RNA secondary structures. Based on this representation, we outline an approach by constructing a 3-component vector whose components are the normalized leading eigenvalues of the L/L matrices associated with RNA secondary structure. The examination of similarities/dissimilarities among the secondary structure at the 3'-terminus of different viruses illustrates the utility of the approach.     

2-D graphical representation for characteristic sequences of DNA and its application

DNA sequencing has resulted in an abundance of data on DNA sequences for various species. Hence, the characterization and comparison of sequences become more important but still difficult tasks. In this paper, we first give a 2-D ladderlike graphical representation for the characteristic sequences of a DNA sequence, and then construct a 3-component vector, in which the normalized ALE-indices extracted from such three 2-D graphs via D/D matrices are individual components, to characterize the DNA sequence. The examination of similarities/dissimilarities among sequences of the beta-globin genes of different species illustrates the utility of the approach.     

A 3D Graphical Representation of RNA Secondary Structures

Abstract

In this paper, we proposed a 3-D graphical representation of RNA secondary structures. Based on this representation, we outline an approach by constructing a 3-component vector whose components are the normalized leading eigenvalues of the L/L matrices associated with RNA secondary structure. The examination of similarities/dissimilarities among the secondary structure at the 3′-terminus of different viruses illustrates the utility of the approach.     

Two-dimensional immune profiles improve antigen microarray-based characterization of humoral immunity

Antigen arrays are becoming widely used tools for the characterization of the complexity of humoral immune responses. Current antibody profiling techniques provide modest and indirect information about the effector functions of the antibodies that bind to particular antigens. Here we introduce an antigen array-based approach for obtaining immune profiles reflecting antibody functionality. This technology relies on the parallel measurement of antibody binding and complement activation by features of the array. By comparing sera from animals immunized against the same antigen under different conditions, we show that identifying the position of an antigen in a 2-D space, derived from antibody binding and complement deposition, permits distinction between immune profiles characterized by diverse antibody isotype distributions. Additionally, the technology provides a biologically interpretable graphical representation of the relationship between antigen and host. Our data suggest that 2-D immune profiling could enrich the data obtained from proteomic scale serum profiling studies.     

New method for global alignment of 2 DNA sequences by the tree data structure

We introduce a new approach to investigate problem of DNA sequence alignment. The method consists of three parts: (i) simple alignment algorithm, (ii) extension algorithm for largest common substring, (iii) graphical simple alignment tree (GSA tree). The approach firstly obtains a graphical representation of scores of DNA sequences by the scoring equation R₀*R−S₀*S−T₀*(a+bk). Then a GSA tree is constructed to facilitate solving the problem for global alignment of 2 DNA sequences. Finally we give several practical examples to illustrate the utility and practicality of the approach.     

Protein surface representation and analysis by dimension reduction

Background

Protein structures are better conserved than protein sequences, and consequently more functional information is available in structures than in sequences. However, proteins generally interact with other proteins and molecules via their surface regions and a backbone-only analysis of protein structures may miss many of the functional and evolutionary features. Surface information can help better elucidate proteins' functions and their interactions with other proteins. Computational analysis and comparison of protein surfaces is an important challenge to overcome to enable efficient and accurate functional characterization of proteins.

Methods

In this study we present a new method for representation and comparison of protein surface features. Our method is based on mapping the 3-D protein surfaces onto 2-D maps using various dimension reduction methods. We have proposed area and neighbor based metrics in order to evaluate the accuracy of this surface representation. In order to capture functionally relevant information, we encode geometric and biochemical features of the protein, such as hydrophobicity, electrostatic potential, and curvature, into separate color channels in the 2-D map. The resulting images can then be compared using efficient 2-D image registration methods to identify surface regions and features shared by proteins.

Results

We demonstrate the utility of our method and characterize its performance using both synthetic and real data. Among the dimension reduction methods investigated, SNE, LandmarkIsomap, Isomap, and Sammon's mapping provide the best performance in preserving the area and neighborhood properties of the original 3-D surface. The enriched 2-D representation is shown to be useful in characterizing the functional site of chymotrypsin and able to detect structural similarities in heat shock proteins. A texture mapping using the 2-D representation is also proposed as an interesting application to structure visualization.

     

On graphical and numerical representation of protein sequences

A new approach using a 3-D Cartesian coordinate system to represent protein sequences has been derived. By the 3-D Graphical representation we make a comparison of sequences belonging to nine different proteins.     

PDB@: An offline toolkit for exploration and analysis of PDB files

Protein Data Bank (PDB) is a freely accessible archive of the 3-D structural data of biological molecules. Structure based studies offers a unique vantage point in inferring the properties of a protein molecule from structural data. This is too big a task to be done manually. Moreover, there is no single tool, software or server that comprehensively analyses all structure-based properties. The objective of the present work is to develop an offline computational toolkit, PDB@ containing in-built algorithms that help categorizing the structural properties of a protein molecule. The user has the facility to view and edit the PDB file to his need. Some features of the present work are unique in itself and others are an improvement over existing tools. Also, the representation of protein properties in both graphical and textual formats helps in predicting all the necessary details of a protein molecule on a single platform.     

TMRPres2D: high quality visual representation of transmembrane protein models

The 'TransMembrane protein Re-Presentation in 2-Dimensions' (TMRPres2D) tool, automates the creation of uniform, two-dimensional, high analysis graphical images/models of alpha-helical or beta-barrel transmembrane proteins. Protein sequence data and structural information may be acquired from public protein knowledge bases, emanate from prediction algorithms, or even be defined by the user. Several important biological and physical sequence attributes can be embedded in the graphical representation.     

On Graphical and Numerical Representation of Protein Sequences

Abstract

A new approach using a 3-D Cartesian coordinate system to represent protein sequences has been derived. By the 3-D Graphical representation we make a comparison of sequences belonging to nine different proteins.     

Mathematical representation with graphic reconstruction on a microcomputer for an arterial tree

The branching arterial tree is considered as a collection of numerous points and lines. When treated with vertex analysis, it can be expressed with a new mathematical representation, and graphical reconstruction can be carried out on a microcomputer. This new method is useful for recording an arterial tree precisely on anatomical books or comparing arteries under hypertension with those under normotension in order that the early morphological changes of hypertensive vascular disease can be revealed.     

Multiresolution image registration for two-dimensional gel electrophoresis

In proteomic research, two-dimensional electrophoresis (2-D) is an important tool for investigating differential patterns of qualitative and quantitative protein expression. The strength of the technique is due to its unrivalled power of being able to separate simultaneously thousands of proteins. The key to the comparison of 2-D protein profiles, however, lies in the use of a fast and robust image matching process which is essential to the subsequent quantification procedure. To satisfy the growing demand for a robust and fully automatic method of matching 2-D gel protein separation profiles, we describe in this paper a novel registration technique based on image intensity distribution rather than selected features. The method uses a multiresolution representation of the gel profiles and exploits the fact that coarse approximations to the optimal matching can be extracted efficiently from low-resolution images. This permits the removal of misalignments at different scales in a systematic manner and the strength of the new method has been confirmed by a double blind trial of 111 2-D gel pairs. The proposed method requires neither landmarks nor an a priori image alignment, and takes about five seconds for processing a typical gel pair on a standard personal computer.     

A protein map and its application

Graphical representation of gene sequences provides a simple way of viewing, sorting, and comparing various gene structures. Here we first report a two-dimensional graphical representation for protein sequences. With this method, we constructed the moment vectors for protein sequences, and mathematically proved that the correspondence between moment vectors and protein sequences is one-to-one. Therefore, each protein sequence can be represented as a point in a map, which we call protein map, and cluster analysis can be used for comparison between the points. Sixty-six proteins from five protein families were analyzed using this method. Our data showed that for proteins in the same family, their corresponding points in the map are close to each other. We also illustrate the efficiency of this approach by performing an extensive cluster analysis of the protein kinase C family. These results indicate that this protein map could be used to mathematically specify the similarity of two proteins and predict properties of an unknown protein based on its amino acid sequence.     

蛋白质序列的一种新的三维图形表示及其应用

基于氨基酸的五字母模型,给出蛋白质序列的一种新的三维图形表示,然后构造一个12维向量来刻画蛋白质序列,这个向量的分量是与12个图形相对应的D／D矩阵的正规化的ALE-指标。最后基于s结构蛋白对冠状病毒进行系统发生分析来阐明该方法的有用性。     

Describing the organization of dominance relationships by dominance-directed tree method

Methods to describe dominance hierarchies are a key tool in primatology studies. Most current methods are appropriate for analyzing linear and near-linear hierarchies; however, more complex structures are common in primate groups. We propose a method termed "dominance-directed tree." This method is based on graph theory and set theory to analyze dominance relationships in social groups. The method constructs a transitive matrix by imposing transitivity to the dominance matrix and produces a graphical representation of the dominance relationships, which allows an easy visualization of the hierarchical position of the individuals, or subsets of individuals. The method is also able to detect partial and complete hierarchies, and to describe situations in which hierarchical and nonhierarchical principles operate. To illustrate the method, we apply a dominance tree analysis to artificial data and empirical data from a group of Cebus apella.