Orientation-dependent Dxz4 contacts shape the 3D structure of the inactive X chromosome.

The mammalian inactive X chromosome (Xi) condenses into a bipartite structure with two superdomains of frequent long-range contacts, separated by a hinge region. Using Hi-C in edited mouse cells with allelic deletions or inversions within the hinge, here we show that the conserved Dxz4 locus is necessary to maintain this bipartite structure. Dxz4 orientation controls the distribution of contacts on the Xi, as shown by a massive reversal in long-range contacts after Dxz4 inversion. Despite an increase in CTCF binding and chromatin accessibility on the Xi in Dxz4-edited cells, only minor changes in TAD structure and gene expression were detected, in accordance with multiple epigenetic mechanisms ensuring X silencing. We propose that Dxz4 represents a structural platform for frequent long-range contacts with multiple loci in a direction dictated by the orientation of its bank of CTCF motifs, which may work as a ratchet to form the distinctive bipartite structure of the condensed Xi.

Multi-scale correlations in continuous genomic data.

Functional genomic quantities such as histone modifications, chromatin accessibility, and evolutionary constraint can now be measured in a nearly continuous fashion across the genome. The genome is highly heterogeneous, and the relationships between different functional annotations may be fluid. Here we present an approach for visualizing, quantifying, and determining the statistical significance of local and regional correlations between high-density continuous genomic datasets. We use wavelets to generate a multi-scale view of each component data set and calculate correlations between data types as a function of genome position over a continuous range of scales in sliding window fashion. We determine the statistical significance of correlations using a non-parametric sampling approach. We apply the wavelet correlation method to histone modification and chromatin accessibility (DNasel sensitivity) data from the NHGRI ENCODE project. We show that DNaseI sensitivity is broadly correlated (though to differing degrees) with a number of different activating histone modifications. We examine the continuous relationship between the repressive histone modification H3K27me3 and the activating mark H3K4me2, and find these modifications to display significant duality, with both significant positively and negatively correlated genomic territories. While the former appear to recapitulate in definitive cells the so-called "bi-valent" pattern originally proposed as a signature of pluripotency, the presence of negatively correlated regions suggests that the regulatory events that underlie the observed modification patterns are complex and highly regionalized in the genome.

Kernel-based data fusion and its application to protein function prediction in yeast.

Kernel methods provide a principled framework in which to represent many types of data, including vectors, strings, trees and graphs. As such, these methods are useful for drawing inferences about biological phenomena. We describe a method for combining multiple kernel representations in an optimal fashion, by formulating the problem as a convex optimization problem that can be solved using semidefinite programming techniques. The method is applied to the problem of predicting yeast protein functional classifications using a support vector machine (SVM) trained on five types of data. For this problem, the new method performs better than a previously-described Markov random field method, and better than the SVM trained on any single type of data.

Analysis of strain and regional variation in gene expression in mouse brain.

BACKGROUND: We performed a statistical analysis of a previously published set of gene expression microarray data from six different brain regions in two mouse strains. In the previous analysis, 24 genes showing expression differences between the strains and about 240 genes with regional differences in expression were identified. Like many gene expression studies, that analysis relied primarily on ad hoc 'fold change' and 'absent/present' criteria to select genes. To determine whether statistically motivated methods would give a more sensitive and selective analysis of gene expression patterns in the brain, we decided to use analysis of variance (ANOVA) and feature selection methods designed to select genes showing strain- or region-dependent patterns of expression. RESULTS: Our analysis revealed many additional genes that might be involved in behavioral differences between the two mouse strains and functional differences between the six brain regions. Using conservative statistical criteria, we identified at least 63 genes showing strain variation and approximately 600 genes showing regional variation. Unlike ad hoc methods, ours have the additional benefit of ranking the genes by statistical score, permitting further analysis to focus on the most significant. Comparison of our results to the previous studies and to published reports on individual genes show that we achieved high sensitivity while preserving selectivity. CONCLUSIONS: Our results indicate that molecular differences between the strains and regions studied are larger than indicated previously. We conclude that for large complex datasets, ANOVA and feature selection, alone or in combination, are more powerful than methods based on fold-change thresholds and other ad hoc selection criteria.