Chaos game representation for comparison of whole genomes

Jijoy, Joseph; Sasikumar, R.

doi:10.1186/1471-2105-7-243

Cited by 78 publications

(43 citation statements)

References 12 publications

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“…Further studies by Almeida et al (2001) showed that CGR is a generalized Markov chain probability table which can accommodate non-integer orders, and that CGR is a powerful sequence modelling tool because of its computational efficiency and scale-independence (Almeida and Vinga, 2002. Such alignment-free methods have been successfully applied for sequence comparison, phylogeny, detection of horizontal transfers, detection of oligonucleotides of interest, meta-genomic studies (Deschavanne et al, 1999;Pride et al, 2003;Sandberg et al, 2003;Teeling et al, 2004;Chapus et al, 2005;Wang et al, 2005;Dufraigne et al, 2005;Joseph and Sasikumar, 2006).…”

Section: Introductionmentioning

confidence: 99%

New method for comparing DNA primary sequences based on a discrimination measure

Feng

Park

et al. 2010

Journal of Theoretical Biology

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We introduce a new approach to compare DNA primary sequences. The core of our method is a new measure of pairwise distances among sequences. Using the primitive discrimination substrings of sequence S and Q, a discrimination measure DM(S, Q) is defined for the similarity analysis of them. The proposed method does not require multiple alignments and is fully automatic. To illustrate its utility, we construct phylogenetic trees on two independent data sets. The results indicate that the method is efficient and powerful.

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Section: Introductionmentioning

confidence: 99%

New method for comparing DNA primary sequences based on a discrimination measure

Feng

Park

et al. 2010

Journal of Theoretical Biology

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“…Based on oligonucleotide frequencies, the Chaos Game Representations has been used for visual representation and statistical characterization of genome sequence as well as alignment comparisons and establishment of phylogenetic trees [23], [24], [25], [26]. However, following the evolution of frequencies from the beginning to the end of a given sequence has not been exploited.…”

Section: Overview On Chaos Game Representation (Cgrs)mentioning

confidence: 99%

Building Specific Signals from Frequency Chaos Game and Revealing Periodicities Using a Smoothed Fourier Analysis

Messaoudi

ElloumiOueslati

Lachiri

2014

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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Investigating the roles and functions of DNA within genomes is becoming a primary focus of genomic research. Thus, the research works are moving towards cooperation between different scientific disciplines which aims at facilitating the interpretation of genetic information. In order to characterize the DNA of living organisms, signal processing tools appear to be very suitable for such study. However, a DNA sequence must be converted into a numerical sequence before processing; which defines the concept of DNA coding. In line with this, we propose a new one dimensional model based on the chaos game representation theory called Frequency Chaos Game Signal: FCGS. Then, we perform a Smoothed Fourier Transform to enhance hidden periodicities in the C.elegans DNA sequences. Through this study, we demonstrate the performance of our coding approach in highlighting characteristic periodicities. Indeed, several periodicities are shown to be involved in the 1D spectra and the 2D spectrograms of FCGSs. To investigate further about the contribution of our method in the enhancement of characteristic spectral attributes, a comparison with a range of binary indicators is established.

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“…A recent article has examined the use of CGRs to visualize and compare whole genomes [11]. The authors examine the use of CGRs to facilitate alignment between DNA sequences as well as similarities.…”

Section: Cgr Overviewmentioning

confidence: 99%

Visualization of DNA/RNA Structure using Temporal CGRs

Dunham

Quick

Wang

et al. 2006

Sixth IEEE Symposium on BioInformatics and BioEngineering (BIBE'06)

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We present a new DNA/RNA sequence visualization technique, Temporal Chaos Game Representation (TCGR), which is an extension of Frequency Chaos Game Representation (FCGR). Unlike earlier CGR variants, TCGR illustrates the distribution of nucleotide sub-patterns at different positions within a sequence, and it can be applied to sets of sequences as well as individual sequences. TCGRs are suitable for short or long sequences. Preliminary experiments examining patterns of nucleotide occurrences in micro-RNA data illustrate not only the informative nature of the images, but clearly indicate the variability of distributions based on position, and the similarity and differences of distributions across various species.

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Chaos game representation for comparison of whole genomes

Cited by 78 publications

References 12 publications

New method for comparing DNA primary sequences based on a discrimination measure

New method for comparing DNA primary sequences based on a discrimination measure

Building Specific Signals from Frequency Chaos Game and Revealing Periodicities Using a Smoothed Fourier Analysis

Visualization of DNA/RNA Structure using Temporal CGRs

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