Identifying Transcriptional Regulatory Modules Among Different Chromatin States in Mouse Neural Stem Cells

Gene expression regulation is a complex process involving the interplay between transcription factors and chromatin states. Significant progress has been made toward understanding the impact of chromatin states on gene expression. Nevertheless, the mechanism of transcription factors binding combinatorially in different chromatin states to enable selective regulation of gene expression remains an interesting research area. We introduce a nonparametric Bayesian clustering method for inhomogeneous Poisson processes to detect heterogeneous binding patterns of multiple proteins including transcription factors to form regulatory modules in different chromatin states. We applied this approach on ChIP-seq data for mouse neural stem cells containing 21 proteins and observed different groups or modules of proteins clustered within different chromatin states. These chromatin-state-specific regulatory modules were found to have significant influence on gene expression. We also observed different motif preferences for certain TFs between different chromatin states. Our results reveal a degree of interdependency between chromatin states and combinatorial binding of proteins in the complex transcriptional regulatory process. The software package is available on Github at - https://github.com/BSharmi/DPM-LGCP

Retinal-input-induced epigenetic dynamics in the developing mouse dorsal lateral geniculate nucleus

Abstract DNA methylation plays important roles in the regulation of nervous system development and in cellular responses to environmental stimuli such as light-derived signals. Despite great efforts in understanding the maturation and refinement of visual circuits, we lack a clear understanding of how changes in DNA methylation correlate with visual activity in the developing subcortical visual system, such as in the dorsal lateral geniculate nucleus (dLGN), the main retino-recipient region in the dorsal thalamus. Here, we explored epigenetic dynamics underlying dLGN development at ages before and after eye opening in wild-type mice and mutant mice in which retinal ganglion cells fail to form. We observed that development-related epigenetic changes tend to co-localize together on functional genomic regions critical for regulating gene expression, while retinal-input-induced epigenetic changes are enriched on repetitive elements. Enhancers identified in neurons are prone to methylation dynamics during development, and activity-induced enhancers are associated with retinal-input-induced epigenetic changes. Intriguingly, the binding motifs of activity-dependent transcription factors, including EGR1 and members of MEF2 family, are enriched in the genomic regions with epigenetic aberrations in dLGN tissues of mutant mice lacking retinal inputs. Overall, our study sheds new light on the epigenetic regulatory mechanisms underlying the role of retinal inputs on the development of mouse dLGN

CyNetSVM: A Cytoscape App for Cancer Biomarker Identification Using Network Constrained Support Vector Machines

<div><p>One of the important tasks in cancer research is to identify biomarkers and build classification models for clinical outcome prediction. In this paper, we develop a CyNetSVM software package, implemented in Java and integrated with Cytoscape as an app, to identify network biomarkers using network-constrained support vector machines (NetSVM). The Cytoscape app of NetSVM is specifically designed to improve the usability of NetSVM with the following enhancements: (1) user-friendly graphical user interface (GUI), (2) computationally efficient core program and (3) convenient network visualization capability. The CyNetSVM app has been used to analyze breast cancer data to identify network genes associated with breast cancer recurrence. The biological function of these network genes is enriched in signaling pathways associated with breast cancer progression, showing the effectiveness of CyNetSVM for cancer biomarker identification. The CyNetSVM package is available at Cytoscape App Store and <a href="http://sourceforge.net/projects/netsvmjava" target="_blank">http://sourceforge.net/projects/netsvmjava</a>; a sample data set is also provided at sourceforge.net.</p></div

ROC curve of the classification of patients in Loi <i>et al</i>. data.

<p>ROC curve of the classification of patients in Loi <i>et al</i>. data.</p

Functional enrichment of genes identified from Loi <i>et al</i>. data in signaling pathways and associated p-values.

<p>Functional enrichment of genes identified from Loi <i>et al</i>. data in signaling pathways and associated p-values.</p

Computational time of the CyNetSVM app as tested with different network sizes and cross-validation folds.

<p>Computational time of the CyNetSVM app as tested with different network sizes and cross-validation folds.</p

Network identified from METABRIC discovery data.

<p>Network identified from METABRIC discovery data.</p

Functional enrichment of genes identified from the discovery dataset in signaling pathways and associated p-values.

<p>Functional enrichment of genes identified from the discovery dataset in signaling pathways and associated p-values.</p

Means and standard deviations of accuracy for phenotype prediction and AUC for network identification on simulation data with different SNR.

<p>Means and standard deviations of accuracy for phenotype prediction and AUC for network identification on simulation data with different SNR.</p

Network identified from Loi <i>et al</i>. data.

<p>Network identified from Loi <i>et al</i>. data.</p