Browse
Submit Data
Databases
API
Help

Dataset Information

27 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

A web server for interactive and zoomable Chaos Game Representation images.

ABSTRACT: Chaos Game Representation (CGR) is a generalized scale-independent Markov transition table, which is useful for the visualization and comparative study of genomic signature, or for the study of characteristic sequence motifs. However, in order to fully utilize the scale-independent properties of CGR, it should be accessible through scale-independent user interface instead of static images. Here we describe a web server and Perl library for generating zoomable CGR images utilizing Google Maps API, which is also easily searchable for specific motifs. The web server is freely accessible at http://www.g-language.org/wiki/cgr/, and the Perl library as well as the source code is distributed with the G-language Genome Analysis Environment under GNU General Public License.

SUBMITTER: Arakawa K

PROVIDER: S-EPMC2753581 | biostudies-literature | 2009 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

A web server for interactive and zoomable Chaos Game Representation images.

Arakawa Kazuharu K Oshita Kazuki K Tomita Masaru M

Source code for biology and medicine 20090917

Chaos Game Representation (CGR) is a generalized scale-independent Markov transition table, which is useful for the visualization and comparative study of genomic signature, or for the study of characteristic sequence motifs. However, in order to fully utilize the scale-independent properties of CGR, it should be accessible through scale-independent user interface instead of static images. Here we describe a web server and Perl library for generating zoomable CGR images utilizing Google Maps API ...[more]

PMID: 19761591

Similar Datasets

Chaos game representation for comparison of whole genomes.

Project description:BACKGROUND: Chaos game representation of genome sequences has been used for visual representation of genome sequence patterns as well as alignment-free comparisons of sequences based on oligonucleotide frequencies. However the potential of this representation for making alignment-based comparisons of whole genome sequences has not been exploited. RESULTS: We present here a fast algorithm for identifying all local alignments between two long DNA sequences using the sequence information contained in CGR points. The local alignments can be depicted graphically in a dot-matrix plot or in text form, and the significant similarities and differences between the two sequences can be identified. We demonstrate the method through comparison of whole genomes of several microbial species. Given two closely related genomes we generate information on mismatches, insertions, deletions and shuffles that differentiate the two genomes. CONCLUSION: Addition of the possibility of large scale sequence alignment to the repertoire of alignment-free sequence analysis applications of chaos game representation, positions CGR as a powerful sequence analysis tool.

| S-EPMC1482720 | biostudies-literature

Chaos game representation and its applications in bioinformatics.

Project description:Chaos game representation (CGR), a milestone in graphical bioinformatics, has become a powerful tool regarding alignment-free sequence comparison and feature encoding for machine learning. The algorithm maps a sequence to 2-dimensional space, while an extension of the CGR, the so-called frequency matrix representation (FCGR), transforms sequences of different lengths into equal-sized images or matrices. The CGR is a generalized Markov chain and includes various properties, which allow a unique representation of a sequence. Therefore, it has a broad spectrum of applications in bioinformatics, such as sequence comparison and phylogenetic analysis and as an encoding of sequences for machine learning. This review introduces the construction of CGRs and FCGRs, their applications on DNA and proteins, and gives an overview of recent applications and progress in bioinformatics.

| S-EPMC8636998 | biostudies-literature

Chaos game representation dataset of SARS-CoV-2 genome.

Project description:As of April 16, 2020, the novel coronavirus disease (called COVID-19) spread to more than 185 countries/regions with more than 142,000 deaths and more than 2,000,000 confirmed cases. In the bioinformatics area, one of the crucial points is the analysis of the virus nucleotide sequences using approaches such as data stream, digital signal processing, and machine learning techniques and algorithms. However, to make feasible this approach, it is necessary to transform the nucleotide sequences string to numerical values representation. Thus, the dataset provides a chaos game representation (CGR) of SARS-CoV-2 virus nucleotide sequences. The dataset provides the CGR of 100 instances of SARS-CoV-2 virus, 11540 instances of other viruses from the Virus-Host DB dataset, and three instances of Riboviria viruses from NCBI (Betacoronavirus RaTG13, bat-SL-CoVZC45, and bat-SL-CoVZXC21).

| S-EPMC7182522 | biostudies-literature

Analysis of earthquake hypocenter characteristics using chaos game representation.

Project description:This paper proposes a new method to approach earthquake hypocenter studies based on Chaos Game Representation (CGR), a method initially used for making fractal structures and applied for studying DNA sequences. Applying the CGR method, this study aims at checking whether any relation exists between earthquakes occurring in different depth ranges in a seismically active area. For this purpose, the seismically active areas around the Indian tectonic plate were used. The CGR images gave characteristic patterns, implying that the occurrence of earthquakes in some specific depth range combinations showed higher preference. Statistical data on the frequency of different depth range combinations were derived from these plots. We put forward a mathematical value which we call proximity index, to compare the similarity between two different CGR plots. Proximity index values were used to compare the similarity in seismic activity in two different zones by comparing their respective CGR plots.Supplementary informationThe online version contains supplementary material available at 10.1007/s10596-022-10187-x.

| S-EPMC9791623 | biostudies-literature

Project description:The novel coronavirus (SARS-COV-2) is generally referred to as Covid-19 virus has spread to 213 countries with nearly 7 million confirmed cases and nearly 400,000 deaths. Such major outbreaks demand classification and origin of the virus genomic sequence, for planning, containment, and treatment. Motivated by the above need, we report two alignment-free methods combing with CGR to perform clustering analysis and create a phylogenetic tree based on it. To each DNA sequence we associate a matrix then define distance between two DNA sequences to be the distance between their associated matrix. These methods are being used for phylogenetic analysis of coronavirus sequences. Our approach provides a powerful tool for analyzing and annotating genomes and their phylogenetic relationships. We also compare our tool to ClustalX algorithm which is one of the most popular alignment methods. Our alignment-free methods are shown to be capable of finding closest genetic relatives of coronaviruses.

| S-EPMC7497811 | biostudies-literature

ICM Web: the interactive chromatin modeling web server.

Project description:The interactive chromatin modeling web server (ICM Web) is an interactive tool that allows users to rapidly assess nucleosome stability and fold sequences of DNA into putative chromatin templates. ICM Web takes a sequence composed of As, Cs, Gs, and Ts as input and generates (i) a nucleosome energy level diagram, (ii) coarse-grained representations of free DNA and chromatin and (iii) plots of the helical parameters (Tilt, Roll, Twist, Shift, Slide and Rise) as a function of position. The user can select from several different energy models, nucleosome structures and methods for placing nucleosomes in the energy landscape. Alternatively, if nucleosome footprints are known from experiment, ICM Web can use these positions to create a nucleosome array. The default energy model achieves a correlation coefficient of 0.7 with 100 experimentally determined values of stability and properly predicts the location of six positioned nucleosomes in the mouse mammary tumor virus (MMTV) promoter. ICM Web is suitable for interactively investigating nucleosome stability and chromatin folding for sequences up to tens of kilobases in length. No login is required to use ICM Web.

| S-EPMC2896153 | biostudies-literature

HMMER web server: interactive sequence similarity searching.

Project description:HMMER is a software suite for protein sequence similarity searches using probabilistic methods. Previously, HMMER has mainly been available only as a computationally intensive UNIX command-line tool, restricting its use. Recent advances in the software, HMMER3, have resulted in a 100-fold speed gain relative to previous versions. It is now feasible to make efficient profile hidden Markov model (profile HMM) searches via the web. A HMMER web server (http://hmmer.janelia.org) has been designed and implemented such that most protein database searches return within a few seconds. Methods are available for searching either a single protein sequence, multiple protein sequence alignment or profile HMM against a target sequence database, and for searching a protein sequence against Pfam. The web server is designed to cater to a range of different user expertise and accepts batch uploading of multiple queries at once. All search methods are also available as RESTful web services, thereby allowing them to be readily integrated as remotely executed tasks in locally scripted workflows. We have focused on minimizing search times and the ability to rapidly display tabular results, regardless of the number of matches found, developing graphical summaries of the search results to provide quick, intuitive appraisement of them.

| S-EPMC3125773 | biostudies-literature

RCOVID19: Recurrence-based SARS-CoV-2 features using chaos game representation.

Project description:Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the COVID-19 pandemic. It was first detected in China and was rapidly spread to other countries. Several thousands of whole genome sequences of SARS-CoV-2 have been reported and it is important to compare them and identify distinctive evolutionary/mutant markers. Utilizing chaos game representation (CGR) as well as recurrence quantification analysis (RQA) as a powerful nonlinear analysis technique, we proposed an effective process to extract several valuable features from genomic sequences of SARS-CoV-2. The represented features enable us to compare genomic sequences with different lengths. The provided dataset involves totally 18 RQA-based features for 4496 instances of SARS-CoV-2.

| S-EPMC7411429 | biostudies-literature

iCDA-CGR: Identification of circRNA-disease associations based on Chaos Game Representation.

Project description:Found in recent research, tumor cell invasion, proliferation, or other biological processes are controlled by circular RNA. Understanding the association between circRNAs and diseases is an important way to explore the pathogenesis of complex diseases and promote disease-targeted therapy. Most methods, such as k-mer and PSSM, based on the analysis of high-throughput expression data have the tendency to think functionally similar nucleic acid lack direct linear homology regardless of positional information and only quantify nonlinear sequence relationships. However, in many complex diseases, the sequence nonlinear relationship between the pathogenic nucleic acid and ordinary nucleic acid is not much different. Therefore, the analysis of positional information expression can help to predict the complex associations between circRNA and disease. To fill up this gap, we propose a new method, named iCDA-CGR, to predict the circRNA-disease associations. In particular, we introduce circRNA sequence information and quantifies the sequence nonlinear relationship of circRNA by Chaos Game Representation (CGR) technology based on the biological sequence position information for the first time in the circRNA-disease prediction model. In the cross-validation experiment, our method achieved 0.8533 AUC, which was significantly higher than other existing methods. In the validation of independent data sets including circ2Disease, circRNADisease and CRDD, the prediction accuracy of iCDA-CGR reached 95.18%, 90.64% and 95.89%. Moreover, in the case studies, 19 of the top 30 circRNA-disease associations predicted by iCDA-CGR on circRDisease dataset were confirmed by newly published literature. These results demonstrated that iCDA-CGR has outstanding robustness and stability, and can provide highly credible candidates for biological experiments.

| S-EPMC7259804 | biostudies-literature

A novel numerical representation for proteins: Three-dimensional Chaos Game Representation and its Extended Natural Vector.

Project description:Chaos Game Representation (CGR) was first proposed to be an image representation method of DNA and have been extended to the case of other biological macromolecules. Compared with the CGR images of DNA, where DNA sequences are converted into a series of points in the unit square, the existing CGR images of protein are not so elegant in geometry and the implications of the distribution of points in the CGR image are not so obvious. In this study, by naturally distributing the twenty amino acids on the vertices of a regular dodecahedron, we introduce a novel three-dimensional image representation of protein sequences with CGR method. We also associate each CGR image with a vector in high dimensional Euclidean space, called the extended natural vector (ENV), in order to analyze the information contained in the CGR images. Based on the results of protein classification and phylogenetic analysis, our method could serve as a precise method to discover biological relationships between proteins.

| S-EPMC7390779 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data