Re-Cracking the Nucleosome Positioning Code
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Mark R Segal
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. This question was seemingly and recently resolved in the affirmative: a wide-ranging series of experimental and computational analyses provided extensive evidence that the instructions for wrapping DNA around nucleosomes are contained in the DNA itself. This subsequently labeled second genetic code was based on data-driven, structural, and biophysical considerations. It was subjected to an extensive suite of validation procedures, with one conclusion being that intrinsic, genome-encoded, nucleosome organization explains approximately 50% of in vivo nucleosome positioning. Here, we revisit both the nature of the underlying sequence preferences, and the performance of the proposed code. A series of new analyses, employing spectral envelope (Fourier transform) methods for assessing key sequence periodicities, classification techniques for evaluating predictive performance, and discriminatory motif finding methods for devising alternate models, are applied. The findings from the respective analyses indicate that signature dinucleotide periodicities are absent from the bulk of the high affinity nucleosome-bound sequences, and that the predictive performance of the code is modest. We conclude that further exploration of the role of sequence-based preferences in genome-wide nucleosome positioning is warranted. This work offers a methodologic counterpart to a recent, high resolution determination of nucleosome positioning that also questions the accuracy of the proposed code and, further, provides illustrations of techniques useful in assessing sequence periodicity and predictive performance.
©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
Articles in the same Issue
- Article
- Self-Organizing Maps with Statistical Phase Synchronization (SOMPS) for Analyzing Cell Cycle-Specific Gene Expression Data
- Coalescent Time Distributions in Trees of Arbitrary Size
- Quantifying the Association between Gene Expressions and DNA-Markers by Penalized Canonical Correlation Analysis
- Nonparametric Functional Mapping of Quantitative Trait Loci Underlying Programmed Cell Death
- Accommodating Uncertainty in a Tree Set for Function Estimation
- Drifting Markov Models with Polynomial Drift and Applications to DNA Sequences
- Comparing the Characteristics of Gene Expression Profiles Derived by Univariate and Multivariate Classification Methods
- Calculating Confidence Intervals for Prediction Error in Microarray Classification Using Resampling
- Structure Learning in Nested Effects Models
- Correcting the Estimated Level of Differential Expression for Gene Selection Bias: Application to a Microarray Study
- Adapting Prediction Error Estimates for Biased Complexity Selection in High-Dimensional Bootstrap Samples
- Adaptive Choice of the Number of Bootstrap Samples in Large Scale Multiple Testing
- Re-Cracking the Nucleosome Positioning Code
- Semi-Parametric Differential Expression Analysis via Partial Mixture Estimation
- A SNP Streak Model for the Identification of Genetic Regions Identical-by-descent
- Detecting Two-Locus Gene-Gene Effects Using Monotonisation of the Penetrance Matrix
- Modeling DNA Methylation in a Population of Cancer Cells
- Phenotyping Genetic Diseases Using an Extension of µ-Scores for Multivariate Data
- The Estimator of the Optimal Measure of Allelic Association: Mean, Variance and Probability Distribution When the Sample Size Tends to Infinity
- Predicting Protein Concentrations with ELISA Microarray Assays, Monotonic Splines and Monte Carlo Simulation
- A Comparison of Normalization Techniques for MicroRNA Microarray Data
- Collapsing SNP Genotypes in Case-Control Genome-Wide Association Studies Increases the Type I Error Rate and Power
- Estimating Number of Clusters Based on a General Similarity Matrix with Application to Microarray Data
- Data Distribution of Short Oligonucleotide Expression Arrays and Its Application to the Construction of a Generalized Intellectual Framework
- Approximately Sufficient Statistics and Bayesian Computation
- A Composite-Conditional-Likelihood Approach for Gene Mapping Based on Linkage Disequilibrium in Windows of Marker Loci
- Statistical Methods in Integrative Analysis for Gene Regulatory Modules
- Reducing Spatial Flaws in Oligonucleotide Arrays by Using Neighborhood Information
- Pattern Classification of Phylogeny Signals
- A Unification of Multivariate Methods for Meta-Analysis of Genetic Association Studies
- Importance Sampling for the Infinite Sites Model
- Supervised Distance Matrices
- Addressing the Shortcomings of Three Recent Bayesian Methods for Detecting Interspecific Recombination in DNA Sequence Alignments
- A Sparse PLS for Variable Selection when Integrating Omics Data
- Software Communication
- TRAB: Testing Whether Mutation Frequencies Are Above an Unknown Background
Articles in the same Issue
- Article
- Self-Organizing Maps with Statistical Phase Synchronization (SOMPS) for Analyzing Cell Cycle-Specific Gene Expression Data
- Coalescent Time Distributions in Trees of Arbitrary Size
- Quantifying the Association between Gene Expressions and DNA-Markers by Penalized Canonical Correlation Analysis
- Nonparametric Functional Mapping of Quantitative Trait Loci Underlying Programmed Cell Death
- Accommodating Uncertainty in a Tree Set for Function Estimation
- Drifting Markov Models with Polynomial Drift and Applications to DNA Sequences
- Comparing the Characteristics of Gene Expression Profiles Derived by Univariate and Multivariate Classification Methods
- Calculating Confidence Intervals for Prediction Error in Microarray Classification Using Resampling
- Structure Learning in Nested Effects Models
- Correcting the Estimated Level of Differential Expression for Gene Selection Bias: Application to a Microarray Study
- Adapting Prediction Error Estimates for Biased Complexity Selection in High-Dimensional Bootstrap Samples
- Adaptive Choice of the Number of Bootstrap Samples in Large Scale Multiple Testing
- Re-Cracking the Nucleosome Positioning Code
- Semi-Parametric Differential Expression Analysis via Partial Mixture Estimation
- A SNP Streak Model for the Identification of Genetic Regions Identical-by-descent
- Detecting Two-Locus Gene-Gene Effects Using Monotonisation of the Penetrance Matrix
- Modeling DNA Methylation in a Population of Cancer Cells
- Phenotyping Genetic Diseases Using an Extension of µ-Scores for Multivariate Data
- The Estimator of the Optimal Measure of Allelic Association: Mean, Variance and Probability Distribution When the Sample Size Tends to Infinity
- Predicting Protein Concentrations with ELISA Microarray Assays, Monotonic Splines and Monte Carlo Simulation
- A Comparison of Normalization Techniques for MicroRNA Microarray Data
- Collapsing SNP Genotypes in Case-Control Genome-Wide Association Studies Increases the Type I Error Rate and Power
- Estimating Number of Clusters Based on a General Similarity Matrix with Application to Microarray Data
- Data Distribution of Short Oligonucleotide Expression Arrays and Its Application to the Construction of a Generalized Intellectual Framework
- Approximately Sufficient Statistics and Bayesian Computation
- A Composite-Conditional-Likelihood Approach for Gene Mapping Based on Linkage Disequilibrium in Windows of Marker Loci
- Statistical Methods in Integrative Analysis for Gene Regulatory Modules
- Reducing Spatial Flaws in Oligonucleotide Arrays by Using Neighborhood Information
- Pattern Classification of Phylogeny Signals
- A Unification of Multivariate Methods for Meta-Analysis of Genetic Association Studies
- Importance Sampling for the Infinite Sites Model
- Supervised Distance Matrices
- Addressing the Shortcomings of Three Recent Bayesian Methods for Detecting Interspecific Recombination in DNA Sequence Alignments
- A Sparse PLS for Variable Selection when Integrating Omics Data
- Software Communication
- TRAB: Testing Whether Mutation Frequencies Are Above an Unknown Background
