Proteomics in the Light of Integral Value Transformations

In this paper, Proteomics have been studied in the light of Integral Value Transformations (IVTs) which was introduced by Sk. S. Hassan et al in 2010. For case study, a Human olfactory receptor OR1D2 protein sequence has been taken and then different IVTs have been used to evolve OR1D2 into some other proteomic like sequences. It has been observed that some of the generated sequences have been mapped to another olfactory receptor in Human or in some other species. Also it has been corroborated through fractal dimension that some of the fundamental protein properties have been nearly intact, even after the mapping. This study will help to comprehend the proteomic evolutionary network with the help of IVTs

Multifractal characterisation of length sequences of coding and noncoding segments in a complete genome

The coding and noncoding length sequences constructed from a complete genome are characterised by multifractal analysis. The dimension spectrum $D_{q}$ and its derivative, the 'analogous' specific heat $C_{q}$, are calculated for the coding and noncoding length sequences of bacteria, where $q$ is the moment order of the partition sum of the sequences. From the shape of the $$ and $C_{q}$ curves, it is seen that there exists a clear difference between the coding/noncoding length sequences of all organisms considered and a completely random sequence. The complexity of noncoding length sequences is higher than that of coding length sequences for bacteria. Almost all $D_{q}$ curves for coding length sequences are flat, so their multifractality is small whereas almost all $D_{q}$ curves for noncoding length sequences are multifractal-like. We propose to characterise the bacteria according to the types of the $C_{q}$ curves of their noncoding length sequences.Comment: 15 pages with 5 figures, Latex, Accepted for publication in Physica

Measure representation and multifractal analysis of complete genomes

This paper introduces the notion of measure representation of DNA sequences. Spectral analysis and multifractal analysis are then performed on the measure representations of a large number of complete genomes. The main aim of this paper is to discuss the multifractal property of the measure representation and the classification of bacteria. From the measure representations and the values of the $D_{q}$ spectra and related $C_{q}$ curves, it is concluded that these complete genomes are not random sequences. In fact, spectral analyses performed indicate that these measure representations considered as time series, exhibit strong long-range correlation. For substrings with length K=8, the $D_{q}$ spectra of all organisms studied are multifractal-like and sufficiently smooth for the $C_{q}$ curves to be meaningful. The $C_{q}$ curves of all bacteria resemble a classical phase transition at a critical point. But the 'analogous' phase transitions of chromosomes of non-bacteria organisms are different. Apart from Chromosome 1 of {\it C. elegans}, they exhibit the shape of double-peaked specific heat function.Comment: 12 pages with 9 figures and 1 tabl

Quantumlike Chaos in the Frequency Distributions of the Bases A, C, G, T in Drosophila DNA

Continuous periodogram power spectral analyses of fractal fluctuations of frequency distributions of bases A, C, G, T in Drosophila DNA show that the power spectra follow the universal inverse power-law form of the statistical normal distribution. Inverse power-law form for power spectra of space-time fluctuations is generic to dynamical systems in nature and is identified as self-organized criticality. The author has developed a general systems theory, which provides universal quantification for observed self-organized criticality in terms of the statistical normal distribution. The long-range correlations intrinsic to self-organized criticality in macro-scale dynamical systems are a signature of quantumlike chaos. The fractal fluctuations self-organize to form an overall logarithmic spiral trajectory with the quasiperiodic Penrose tiling pattern for the internal structure. Power spectral analysis resolves such a spiral trajectory as an eddy continuum with embedded dominant wavebands. The dominant peak periodicities are functions of the golden mean. The observed fractal frequency distributions of the Drosophila DNA base sequences exhibit quasicrystalline structure with long-range spatial correlations or self-organized criticality. Modification of the DNA base sequence structure at any location may have significant noticeable effects on the function of the DNA molecule as a whole. The presence of non-coding introns may not be redundant, but serve to organize the effective functioning of the coding exons in the DNA molecule as a complete unit.Comment: 46 pages, 9 figure

A stitch in time: Efficient computation of genomic DNA melting bubbles

Background: It is of biological interest to make genome-wide predictions of the locations of DNA melting bubbles using statistical mechanics models. Computationally, this poses the challenge that a generic search through all combinations of bubble starts and ends is quadratic. Results: An efficient algorithm is described, which shows that the time complexity of the task is O(NlogN) rather than quadratic. The algorithm exploits that bubble lengths may be limited, but without a prior assumption of a maximal bubble length. No approximations, such as windowing, have been introduced to reduce the time complexity. More than just finding the bubbles, the algorithm produces a stitch profile, which is a probabilistic graphical model of bubbles and helical regions. The algorithm applies a probability peak finding method based on a hierarchical analysis of the energy barriers in the Poland-Scheraga model. Conclusions: Exact and fast computation of genomic stitch profiles is thus feasible. Sequences of several megabases have been computed, only limited by computer memory. Possible applications are the genome-wide comparisons of bubbles with promotors, TSS, viral integration sites, and other melting-related regions.Comment: 16 pages, 10 figure

Distinguish Coding And Noncoding Sequences In A Complete Genome Using Fourier Transform

A Fourier transform method is proposed to distinguish coding and non-coding sequences in a complete genome based on a number sequence representation of the DNA sequence proposed in our previous paper (Zhou et al., J. Theor. Biol. 2005) and the imperfect periodicity of 3 in protein coding sequences. The three parameters P_x(S) (1), P_x(S) (1/3) and P_x(S) (1/36) in the Fourier transform of the number sequence representation of DNA sequences are selected to form a three-dimensional parameter space. Each DNA sequence is then represented by a point in this space. The points corresponding to coding and non-coding sequences in the complete genome of prokaryotes are seen to be divided into different regions. If the point (P_x(�ar S) (1), Px(�ar S) (1/3), P_x(�ar S) (1/36)) for a DNA sequence is situated in the region corresponding to coding sequences, the sequence is distinguished as a coding sequence; otherwise, the sequence is classified as a noncoding one. Fisher's discriminant algorithm is used to study the discriminant accuracy. The average discriminant accuracies pc, pnc, qc and qnc of all 51 prokaryotes obtained by the present method reach 81.02%, 92.27%, 80.77% and 92.24% respectively