Prediction Of Nucleosome Positioning Research Articles

Comparative analysis and prediction of nucleosome positioning using integrative feature representation and machine learning algorithms

BackgroundNucleosome plays an important role in the process of genome expression, DNA replication, DNA repair and transcription. Therefore, the research of nucleosome positioning has invariably received extensive attention. Considering the diversity of DNA sequence representation methods, we tried to integrate multiple features to analyze its effect in the process of nucleosome positioning analysis. This process can also deepen our understanding of the theoretical analysis of nucleosome positioning.ResultsHere, we not only used frequency chaos game representation (FCGR) to construct DNA sequence features, but also integrated it with other features and adopted the principal component analysis (PCA) algorithm. Simultaneously, support vector machine (SVM), extreme learning machine (ELM), extreme gradient boosting (XGBoost), multilayer perceptron (MLP) and convolutional neural networks (CNN) are used as predictors for nucleosome positioning prediction analysis, respectively. The integrated feature vector prediction quality is significantly superior to a single feature. After using principal component analysis (PCA) to reduce the feature dimension, the prediction quality of H. sapiens dataset has been significantly improved.ConclusionsComparative analysis and prediction on H. sapiens, C. elegans, D. melanogaster and S. cerevisiae datasets, demonstrate that the application of FCGR to nucleosome positioning is feasible, and we also found that integrative feature representation would be better.

Prediction of nucleosome positioning by the incorporation of frequencies and distributions of three different nucleotide segment lengths into a general pseudo k-tuple nucleotide composition.

MotivationNucleosome positioning plays important roles in many eukaryotic intranuclear processes, such as transcriptional regulation and chromatin structure formation. The investigations of nucleosome positioning rules provide a deeper understanding of these intracellular processes.ResultsNucleosome positioning prediction was performed using a model consisting of three types of variables characterizing a DNA sequence—the number of five-nucleotide sequences, the number of three-nucleotide combinations in one period of a helix, and mono- and di-nucleotide distributions in DNA fragments. Using recently proposed stringent benchmark datasets with low biases for Saccharomyces cerevisiae, Homo sapiens, Caenorhabditis elegans and Drosophila melanogaster, the present model was shown to have a better prediction performance than the recently proposed predictors. This model was able to display the common and organism-dependent factors that affect nucleosome forming and inhibiting sequences as well. Therefore, the predictors developed here can accurately predict nucleosome positioning and help determine the key factors influencing this process.Supplementary information Supplementary data are available at Bioinformatics online.

An analysis and prediction of nucleosome positioning based on information content

Nucleosome positioning plays a key role in the regulation of many biological processes. In this study, the statistical difference of information content was investigated in nucleosome and linker DNA regions across eukaryotic organisms. By analyzing the information redundancy, D k , in Saccharomyces cerevisiae, Drosophila melanogaster, and Caenorhabditis elegans genomes, the short-range dominance of nucleotide correlation in nucleosome and linker DNA regions was confirmed. Significant difference of the D k value between the nucleosome and linker DNA regions was also found. The underlying reason for many successful oligonucleotide-based predictions of nucleosome positioning in eukaryotic model organisms may be attributed to the short-range dominance of nucleotide correlation in the nucleosome and linker DNA regions. When applying power spectrum analysis to the nucleosome and linker DNA regions, some obvious differences in sequence periodic signals were observed. The parameter F k was introduced to describe particular base correlation. Furthermore, the support vector machine combining F k was used to classify nucleosome and linker DNA regions in Homo sapiens, Oryzias latipes, C. elegans, Candida albicans, and S. cerevisiae. Independent test demonstrated that a good performance can be achieved by using this algorithm. This result further revealed that base correlation information has an important role in nucleosome positioning.

A continuous-index Bayesian hidden Markov model for prediction of nucleosome positioning in genomic DNA

Nucleosomes are units of chromatin structure, consisting of DNA sequence wrapped around proteins called "histones." Nucleosomes occur at variable intervals throughout genomic DNA and prevent transcription factor (TF) binding by blocking TF access to the DNA. A map of nucleosomal locations would enable researchers to detect TF binding sites with greater efficiency. Our objective is to construct an accurate genomic map of nucleosome-free regions (NFRs) based on data from high-throughput genomic tiling arrays in yeast. These high-volume data typically have a complex structure in the form of dependence on neighboring probes as well as underlying DNA sequence, variable-sized gaps, and missing data. We propose a novel continuous-index model appropriate for non-equispaced tiling array data that simultaneously incorporates DNA sequence features relevant to nucleosome formation. Simulation studies and an application to a yeast nucleosomal assay demonstrate the advantages of using the new modeling framework, as well as its robustness to distributional misspecifications. Our results reinforce the previous biological hypothesis that higher-order nucleotide combinations are important in distinguishing nucleosomal regions from NFRs.

Prediction of Nucleosome Positioning in Genomes: Limits and Perspectives of Physical and Bioinformatic Approaches

Nucleosomes, the fundamental repeating subunits of all eukaryotic chromatin, are responsible for packaging DNA into chromosomes inside the cell nucleus and controlling gene expression. While it has been well established that nucleosomes exhibit higher affinity for select DNA sequences, until recently it was unclear whether such preferences exerted a significant, genome-wide effect on nucleosome positioning in vivo. For this reason, an increasing interest is arising on a wide-ranging series of experimental and computational analyses capable of predicting the nucleosome positioning along genomes. Toward this goal, we propose a theoretical model for predicting nucleosome thermodynamic stability in terms of DNA sequence. Based on a statistical mechanical approach, the model allows the calculation of the sequence-dependent canonical ensemble free energy involved in nucleosome formation. The theoretical free energies were evaluated for 90 single nucleosome DNA tracts and successfully compared with those obtained with nucleosome competitive reconstitution. These results, obtained for single nucleosomes, could in principle allow the calculation of the intrinsic affinity of nucleosomes along DNA sequences virtually opening the possibility of predicting the nucleosome positioning along genomes on physical basis. The theoretical nucleosome distribution was compared and validated with that of yeast and human genome experimentally determined. The results interpret on a physical basis the experimental nucleosome positioning and are comparable with those obtained adopting models based on the identification of some recurrent sequence features obtained from the statistical analysis of a very large pool of nucleosomal DNA sequences provided by the positioning maps of genomes.

The curvature vector in nucleosomal DNAs and theoretical prediction of nucleosome positioning.

Using our model for predicting DNA superstructures from the sequence, the average distribution of the phases of curvature along the sequences of the set of the 177 nucleosomal DNAs investigated by Satchwell et al. (J. Mol. Biol. 191 (1986) 659) was calculated. The diagram obtained shows very significant features which allow the visualization of the intrinsic nucleosomal superstructure characterized by two quasi-parallel tracts of a flat left-handed superhelical turn connected by a left-handed inflection in a perpendicular direction; such a superstructure appears to be closely related to the nucleosome model of Travers and Klug (Phil. Trans. R. Soc. Lond. 317 (1987) 537). The nucleosomal curvature phase diagram was then adopted as a sensitive determinant for the nucleosome virtual positioning in DNAs via correlation function, obtaining a good agreement with the experimental mapping of SV40 regulatory region as recently investigated by Ambrose et al. (J. Mol. Biol. 209 (1989) 255). This analysis shows also the presence of a constant phase relation between the virtual nucleosome positions which suggests its possible implication in the nucleosome condensation in chromatin.