Distribution Of Nucleotides Research Articles

Fractals and Hidden Symmetries in DNA

This paper deals with the digital complex representation of a DNA sequence and the analysis of existing correlations by wavelets. The symbolic DNA sequence is mapped into a nonlinear time series. By studying this time series the existence of fractal shapes and symmetries will be shown. At first step, the indicator matrix enables us to recognize some typical patterns of nucleotide distribution. The DNA sequence, of the influenza virus A (H1N1), is investigated by using the complex representation, together with the corresponding walks on DNA; in particular, it is shown that DNA walks are fractals. Finally, by using the wavelet analysis, the existence of symmetries is proven.

Natural insertions within the N-terminal region of the coat protein of Maize dwarf mosaic potyvirus (MDMV) have an effect on the RNA stability

A 13 amino acid residue insertion was found in the N-terminal region of the coat protein of several Maize dwarf mosaic virus isolates (MDMV). These insertions seem to be the result of a direct duplication event, but differ in some positions. In order to evaluate the influence of the insertion on the RNA secondary structure and stability, the RNA secondary structures and minimum free energies (MFE) of all existing MDMV coat protein sequences were estimated using three different softwares, the Vienna RNA Package, NUPACK, and UNAFold, and compared to the secondary structure and MFE of various random sequence collections preserving the nucleotide distribution of MDMV. The bioinformatic analysis showed that the insertion stabilizes the RNA structure of the coat protein gene.

Optimizing nucleotide sequence ensembles for combinatorial protein libraries using a genetic algorithm

Protein libraries are essential to the field of protein engineering. Increasingly, probabilistic protein design is being used to synthesize combinatorial protein libraries, which allow the protein engineer to explore a vast space of amino acid sequences, while at the same time placing restrictions on the amino acid distributions. To this end, if site-specific amino acid probabilities are input as the target, then the codon nucleotide distributions that match this target distribution can be used to generate a partially randomized gene library. However, it turns out to be a highly nontrivial computational task to find the codon nucleotide distributions that exactly matches a given target distribution of amino acids. We first showed that for any given target distribution an exact solution may not exist at all. Formulated as a constrained optimization problem, we then developed a genetic algorithm-based approach to find codon nucleotide distributions that match as closely as possible to the target amino acid distribution. As compared with the previous gradient descent method on various objective functions, the new method consistently gave more optimized distributions as measured by the relative entropy between the calculated and the target distributions. To simulate the actual lab solutions, new objective functions were designed to allow for two separate sets of codons in seeking a better match to the target amino acid distribution.

Nucleosome Positioning and RNA Splicing*

Based on the characteristic of nucleotide distribution in nucleosome positioning and inhibiting sequences, the method of Increment of Diversity with Quadratic Discriminant (IDQD) was applied to the classification of these two types of sequences. The mean area under ROC curve archives 0.958. By using this model, the nucleosome formation potential was analyzed in the regions around the splice sites (GT/AG). The results show that coding regions have a high potential to form the nucleosome and the primary RNA transcripts are rigid, while DNA sequences corresponding to the splice sites and their adjacent intron regions tend to be nucleosome free and the primary transcripts from these regions are relative flexible. Moreover, the negative correlation between nucleosome positioning/inhibiting of DNA sequences and RNA flexibility/rigidity is demonstrated around the splice sites, providing a mechanism for understanding the correlation between the nucleosome positioning of DNA and the splicing of transcribed RNA sequences.

Empirical relationship between intra-purine and intra-pyrimidine differences in conserved gene sequences.

DNA sequences seen in the normal character-based representation appear to have a formidable mixing of the four nucleotides without any apparent order. Nucleotide frequencies and distributions in the sequences have been studied extensively, since the simple rule given by Chargaff almost a century ago that equates the total number of purines to the pyrimidines in a duplex DNA sequence. While it is difficult to trace any relationship between the bases from studies in the character representation of a DNA sequence, graphical representations may provide a clue. These novel representations of DNA sequences have been useful in providing an overview of base distribution and composition of the sequences and providing insights into many hidden structures. We report here our observation based on a graphical representation that the intra-purine and intra-pyrimidine differences in sequences of conserved genes generally follow a quadratic distribution relationship and show that this may have arisen from mutations in the sequences over evolutionary time scales. From this hitherto undescribed relationship for the gene sequences considered in this report we hypothesize that such relationships may be characteristic of these sequences and therefore could become a barrier to large scale sequence alterations that override such characteristics, perhaps through some monitoring process inbuilt in the DNA sequences. Such relationship also raises the possibility of intron sequences playing an important role in maintaining the characteristics and could be indicative of possible intron-late phenomena.

Periodicities in nucleotide distribution in the immunoglobulin light chain locus of Gallus gallus

Fourier spectra of nucleotide sequences from chicken (Gallus gallus) chromosome 15 were obtained. Periodical structures of several fragments of the Ig light chain locus were characterized. The Fourier spectra of the locus and its parts (variable and constant segments, enhancer, and sequences supposedly associated with somatic hypermutation) were shown to be unique among those of other chromosome 15 fragments.

Current–voltage characteristics of double-strand DNA sequences

We use a tight-binding formulation to investigate the transmissivity and the current–voltage (I–V) characteristics of sequences of double-strand DNA molecules. In order to reveal the relevance of the underlying correlations in the nucleotides distribution, we compare the results for the genomic DNA sequence with those of artificial sequences (the long-range correlated Fibonacci and Rudin–Shapiro one) and a random sequence, which is a kind of prototype of a short-range correlated system. The random sequence is presented here with the same first neighbors pair correlations of the human DNA sequence. We found that the long-range character of the correlations is important to the transmissivity spectra, although the I–V curves seem to be mostly influenced by the short-range correlations.

Large-scale genome analysis reveals unique features of microRNAs

Although great progress has been made in identifying microRNAs (miRNAs) and their functions, their essential functional features remain largely unknown. In this study, we systemically investigated the nucleotide and thermodynamic folding distribution characteristics of 3853 miRNAs currently reported for metazoans. We determined that uracil is the dominant nucleotide in both mature and precursor sequences, and that it is particularly enriched at three sites in mature miRNAs: the first, ninth, and the five terminal 3′ nucleotides. The location of these enriched uracil nucleotides is particularly interesting because positions one and nine are the edges of the “seed region”, which is responsible for targeting mRNAs for gene regulation. The prevalence of U residues at these sites may contribute to the mechanism whereby miRNAs target and bind to their corresponding mRNAs. A comparison of the overall lengths of metazoan pre-miRNAs revealed that they ranged from 53 to 215 nt in length with an average of 88.10 ± 14.14 nt, significantly higher than previously reported. Comparisons of miRNA diversity at different taxonomic levels revealed that the 12 features investigated in this study varied significantly among miRNAs represented by different phyla, with particularly high levels of divergence in platyhelminths relative to nematodes, arthropods or vertebrates. By comparison, lower levels of diversity were observed at lower taxonomic levels such that there was a direct relationship between divergence in miRNA features and taxonomic level. We conclude that large-scale genome analysis shows that miRNAs have many more unique features than previously reported. In particular, the distribution of nucleotides suggests an important role for uracil at the boundaries of the ‘seed’ region and at their termini. These results will facilitate the design of new computational programs for identifying novel miRNAs and investigating the mechanism of miRNA-mediated gene regulation.

Multifactor dimensionality reduction analysis identifies specific nucleotide patterns promoting genetic polymorphisms

BackgroundThe fidelity of DNA replication serves as the nidus for both genetic evolution and genomic instability fostering disease. Single nucleotide polymorphisms (SNPs) constitute greater than 80% of the genetic variation between individuals. A new theory regarding DNA replication fidelity has emerged in which selectivity is governed by base-pair geometry through interactions between the selected nucleotide, the complementary strand, and the polymerase active site. We hypothesize that specific nucleotide combinations in the flanking regions of SNP fragments are associated with mutation.ResultsWe modeled the relationship between DNA sequence and observed polymorphisms using the novel multifactor dimensionality reduction (MDR) approach. MDR was originally developed to detect synergistic interactions between multiple SNPs that are predictive of disease susceptibility. We initially assembled data from the Broad Institute as a pilot test for the hypothesis that flanking region patterns associate with mutagenesis (n = 2194). We then confirmed and expanded our inquiry with human SNPs within coding regions and their flanking sequences collected from the National Center for Biotechnology Information (NCBI) database (n = 29967) and a control set of sequences (coding region) not associated with SNP sites randomly selected from the NCBI database (n = 29967). We discovered seven flanking region pattern associations in the Broad dataset which reached a minimum significance level of p ≤ 0.05. Significant models (p << 0.001) were detected for each SNP type examined in the larger NCBI dataset. Importantly, the flanking region models were elongated or truncated depending on the nucleotide change. Additionally, nucleotide distributions differed significantly at motif sites relative to the type of variation observed. The MDR approach effectively discerned specific sites within the flanking regions of observed SNPs and their respective identities, supporting the collective contribution of these sites to SNP genesis.ConclusionThe present study represents the first use of this computational methodology for modeling nonlinear patterns in molecular genetics. MDR was able to identify distinct nucleotide patterning around sites of mutations dependent upon the observed nucleotide change. We discovered one flanking region set that included five nucleotides clustered around a specific type of SNP site. Based on the strongly associated patterns identified in this study, it may become possible to scan genomic databases for such clustering of nucleotides in order to predict likely sites of future SNPs, and even the type of polymorphism most likely to occur.

Complex organizational structure of the genome revealed by genome-wide analysis of single and alternative promoters in Drosophila melanogaster

BackgroundThe promoter is a critical necessary transcriptional cis-regulatory element. In addition to its role as an assembly site for the basal transcriptional apparatus, the promoter plays a key part in mediating temporal and spatial aspects of gene expression through differential binding of transcription factors and selective interaction with distal enhancers. Although many genes have multiple promoters, little attention has been focused on how these relate to one another; nor has much study been directed at relationships between promoters of adjacent genes.ResultsWe have undertaken a systematic investigation of Drosophila promoters. We divided promoters into three groups: unique promoters, first alternative promoters (the most 5' of a gene's multiple promoters), and downstream alternative promoters (the remaining alternative promoters 3' to the first). We observed distinct nucleotide distribution and sequence motif preferences among these three classes. We also investigated the promoters of neighboring genes and found that a greater than expected number of adjacent genes have similar sequence motif profiles, which may allow the genes to be regulated in a coordinated fashion. Consistent with this, there is a positive correlation between similar promoter motifs and related gene expression profiles for these genes.ConclusionsOur results suggest that different regulatory mechanisms may apply to each of the three promoter classes, and provide a mechanism for "gene expression neighborhoods," local clusters of co-expressed genes. As a whole, our data reveal an unexpected complexity of genomic organization at the promoter level with respect to both alternative and neighboring promoters.

A combined statistical model for multiple motifs search

Transcription factor binding sites (TFBS) play key roles in genebior 6.8 wavelet expression and regulation. They are short sequence segments with definite structure and can be recognized by the corresponding transcription factors correctly. From the viewpoint of statistics, the candidates of TFBS should be quite different from the segments that are randomly combined together by nucleotide. This paper proposes a combined statistical model for finding over-represented short sequence segments in different kinds of data set. While the over-represented short sequence segment is described by position weight matrix, the nucleotide distribution at most sites of the segment should be far from the background nucleotide distribution. The central idea of this approach is to search for such kind of signals. This algorithm is tested on 3 data sets, including binding sites data set of cyclic AMP receptor protein in E.coli, PlantProm DB which is a non-redundant collection of proximal promoter sequences from different species, collection of the intergenic sequences of the whole genome of E.Coli. Even though the complexity of these three data sets is quite different, the results show that this model is rather general and sensible.

Identification of conserved microRNAs in Bombyx mori (silkworm) and regulation of fibroin L chain production by microRNAs in heterologous system

MicroRNAs (miRNAs) are an extensive class of small non-coding RNAs with important roles in the regulation of gene expression. Little study has been performed on identifying miRNAs in Bombyx mori (silkworm). Here, 41 conserved miRNAs were identified by a computational homology search approach and some of them were selected and confirmed experimentally. Some characteristics such as position-specific nucleotide distribution, evolutionary conservation and clustered organization have been described. Through general method of miRNA target identification, we also find that at least 4 miRNAs ( miR-33/ -190/ -276/ -7) target fibroin L chain transcripts, suggesting that miRNAs might play an important role in the regulation of silk protein production. In order to validate this, 3′-UTR of fibroin L chain mRNA was cloned and several expression vectors were constructed. Assays of semi-RT-PCR, histochemical and biochemical assays after agroinfiltration indicated that miRNAs can repress reporter gene (GUS) expression with sequence complement. Clearly, agroinfiltration proved a highly efficient method to identify the function of miRNAs inhibiting their targets mRNA in heterologous system.

Sequence-dependence and prediction of nucleotide solvent accessibility in double stranded DNA

Solvent accessibility of amino acid residues in proteins has been widely studied and many methods for its prediction from sequence and evolutionary information are available. Some of the advantages of studying amino acid solvent accessibility also apply to DNA. However, currently there are no methods to estimate the solvent accessibility of nucleotides, as most works on DNA structures have focused on elastic deformations and other structural attributes. In this work, an attempt has been made to analyze the distribution of different nucleotides in various accessibility ranges. Effect of neighboring nucleotides on the predictability of exposure has been evaluated by developing a linear perceptron model that takes sequence information as the input. Five different types of solvent accessibility (overall nucleotide, side chain, main chain, polar and non-polar) have been predicted. From the analysis, it is observed that Thymine stands out in terms of its higher exposed surface area, particularly its side chain and non-polar atoms. It is also concluded that the solvent accessibility of a nucleotide strongly depends on its sequence neighbors and can be predicted with fair success using this information.

Quantifying the Stationarity and Time Reversibility of the Nucleotide Substitution Process

Markov models describing the evolution of the nucleotide substitution process, widely used in phylogeny reconstruction, usually assume the hypotheses of stationarity and time reversibility. Although these models give meaningful results when applied to biological data, it is not clear if the 2 assumptions mentioned above hold and, if not, how much sequence evolution processes deviate from them. To this aim, we introduce 2 sets of indices that can be calculated from the nucleotide distribution and the substitution rates. The stationarity indices (STIs) can be used to test the validity of the equilibrium assumption. The irreversibility indices (IRIs) are derived from the Kolmogorov cycle conditions for time reversibility and quantify the degree of nontime reversibility of a process. We have computed STIs and IRIs for the evolutionary process of 2 lineages, Drosophila simulans and Homo sapiens. In the latter case, we use a modified form of the indices that takes into account the CpG decay process. In both cases, we find statistically significant deviations from the ideal case of a process that has reached stationarity and is time reversible.

Phylogenetic signal in matK vs. trnK: A case study in early diverging eudicots (angiosperms)

The matK gene has been among the most useful loci for resolving plant phylogenetic relationships at different evolutionary time-scales, but much less is known about the phylogenetic utility of the flanking trnK intron, especially for deep level phylogenetics. We compared the relative performance of matK and trnK intron regions for resolving the relationships of the early diverging eudicots (angiosperms). The two regions display similar nucleotide compositions and distributions of rate variation among sites. The trnK intron sequences also provide similar levels of phylogenetic information per-site as matK. Combining the trnK intron sequences with matK increases overall bootstrap support for the early diverging eudicots compared to analyses of matK alone. MP, ML and Bayesian analyses provide strong support for eudicots, the sister group relationship of Ranunculales to remaining eudicots, and a Buxales + Trochodendraceae + core eudicots clade. matK and the trnK intron support conflicting positions for Buxales and Trochodendrales in relation to the core eudicots.

Origins of Replication in Sorangium cellulosum and Microcystis aeruginosa

The genome of Sorangium cellulosum has recently been completely sequenced, and it is the largest bacterial genome sequenced so far. In their report, Schneiker et al. (in Complete genome sequence of the myxobacterium Sorangium cellulosum, Nat. Biotechnol., 2007, 25, 1281–1289) concluded that ‘In the absence of the GC-skew inversion typically seen at the replication origin of bacterial chromosomes, it was not possible to discern the location of oriC’. In addition, the complete genome of Microcystis aeruginosa NIES-843 has also been recently sequenced, and in this report, Kaneko et al. (in Complete genomic structure of the bloom-forming toxic cyanobacterium Microcystis aeruginosa NIES-843, DNA Res., 2007, 14, 247–256) concluded that ‘there was no characteristic pattern, according to GC skew analysis’. Therefore, oriC locations of the above genomes remain unsolved. Using Ori-Finder, a recently developed computer program, in both genomes, we have identified candidate oriC regions that have almost all sequence hallmarks of bacterial oriCs, such as asymmetrical nucleotide distributions, being adjacent to the dnaN gene, and containing DnaA boxes and repeat elements.

Electronic transport in double-strand DNA segments

We report in this work a numerical study of the electronic density of states in π-stacked arrays of DNA double-strand segments made up from the nucleotides guanine G, adenine A, cytosine C and thymine T. In order to reveal the relevance of the underlying correlations in the nucleotides distribution, we compare the results for a genomic DNA sequence, considering a segment of the first sequenced human chromosome 22 (Ch 22), with those of two artificial sequences forming a Rudin-Shapiro (RS) as well as a Fibonacci (FB) polyGC quasiperiodic sequences. Our theoretical method uses an electronic tight-binding Hamiltonian suitable to describe the DNA segments modeled by the quasiperiodic chains.

A Novel Bayesian DNA Motif Comparison Method for Clustering and Retrieval

Characterizing the DNA-binding specificities of transcription factors is a key problem in computational biology that has been addressed by multiple algorithms. These usually take as input sequences that are putatively bound by the same factor and output one or more DNA motifs. A common practice is to apply several such algorithms simultaneously to improve coverage at the price of redundancy. In interpreting such results, two tasks are crucial: clustering of redundant motifs, and attributing the motifs to transcription factors by retrieval of similar motifs from previously characterized motif libraries. Both tasks inherently involve motif comparison. Here we present a novel method for comparing and merging motifs, based on Bayesian probabilistic principles. This method takes into account both the similarity in positional nucleotide distributions of the two motifs and their dissimilarity to the background distribution. We demonstrate the use of the new comparison method as a basis for motif clustering and retrieval procedures, and compare it to several commonly used alternatives. Our results show that the new method outperforms other available methods in accuracy and sensitivity. We incorporated the resulting motif clustering and retrieval procedures in a large-scale automated pipeline for analyzing DNA motifs. This pipeline integrates the results of various DNA motif discovery algorithms and automatically merges redundant motifs from multiple training sets into a coherent annotated library of motifs. Application of this pipeline to recent genome-wide transcription factor location data in S. cerevisiae successfully identified DNA motifs in a manner that is as good as semi-automated analysis reported in the literature. Moreover, we show how this analysis elucidates the mechanisms of condition-specific preferences of transcription factors.

Drifting Markov Models with Polynomial Drift and Applications to DNA Sequences

In this article, we introduce the drifting Markov models (DMMs) which are inhomogeneous Markov models designed for modeling the heterogeneities of sequences (in our case DNA or protein sequences) in a more flexible way than homogeneous Markov chains or even hidden Markov models (HMMs). We focus here on the polynomial drift: the transition matrix varies in a polynomial way. To show the reliability of our models on DNA, we exhibit high similarities between the probability distributions of nucleotides obtained by our models and the frequencies of these nucleotides computed by using a sliding window. In a further step, these DMMs can be used as the states of an HMM: on each of its segments, the observed process can be modeled by a drifting Markov model. Search of rare words in DNA sequences remains possible with DMMs and according to the fits provided, DMMs turn out to be a powerful tool for this purpose. The software is available on request from the author. It will soon be integrated on seq++ library (http://stat.genopole.cnrs.fr/seqpp/).

Signal representation and processing of nucleotide sequences

Sets of related signals can be represented by separating their joint variation and showing the individual signal offsets with respect to this reference. An example is the Genomic Signal Analysis (GSA) of pathogen variability. The conversion of symbolic nucleotide sequences to genomic signals allows to use signal processing methods to analyse genomic data. This approach reveals striking regularities in the distribution of nucleotides and pair of nucleotides along the sequences, in both prokaryotes and eukaryotes. Genomic signals can also be used for sequence prediction, similarly to time series prediction. The methodology is also adequate for studying the development of pathogen multiple resistance to drugs.