第983回千葉医学会例会・第16回神経内科例会

Gene Set Enrichment Analyses of genes ranked with respect to the number of times each gene is predicted as ‘pluripotent’ in LASSO models. (PDF 153 kb

Additional file 4: Table S2. of Computational inference of a genomic pluripotency signature in human and mouse stem cells

Pairwise correlation coefficients between selected histone modification marks including count and breadth data features. (XLSX 42 kb

Additional file 3: Figure S2. of Computational inference of a genomic pluripotency signature in human and mouse stem cells

Epigenetic and protein binding dataset correlations. Correlations between a subset of epigenetic and protein binding data features (assessed by Spearman correlation coefficient) indicate that certain datasets and features are highly correlated with each other in human (A-B-C) and mouse (D-E-F) embryonic stem cells. (PDF 11059 kb

A breast cancer case study with QuIN.

<p>(A) Workflow of the case study analysis. (1) Upload the DNASE-Seq and Interaction data into QuIN, constructing an MCF-7 interaction network where each node represents an open chromatin site. (2) Annotate the network with Non-Coding Variants (NCVs) in MCF-7 and cancer associated gene lists. (3) Perform target discovery between NCVs (source) and promoters and cancer gene lists (targets) and find all direct and indirect associations between NCVs and their gene targets. (B) A simplified network example showing the interactions between a node harboring an NCV (shown in purple) and known oncogenes (green), genes associated with poor prognosis in breast cancer (red), and tumor suppressor genes (blue). Nodes shown were selected based on their overlap with an annotation or if the node is necessary to connect the NCV to the annotated node. Width of the edges correspond to the relative number of paired end tags supporting the edge.</p

QuIN: A Web Server for Querying and Visualizing Chromatin Interaction Networks

<div><p>Recent studies of the human genome have indicated that regulatory elements (e.g. promoters and enhancers) at distal genomic locations can interact with each other via chromatin folding and affect gene expression levels. Genomic technologies for mapping interactions between DNA regions, e.g., ChIA-PET and HiC, can generate genome-wide maps of interactions between regulatory elements. These interaction datasets are important resources to infer distal gene targets of non-coding regulatory elements and to facilitate prioritization of critical loci for important cellular functions. With the increasing diversity and complexity of genomic information and public ontologies, making sense of these datasets demands integrative and easy-to-use software tools. Moreover, network representation of chromatin interaction maps enables effective data visualization, integration, and mining. Currently, there is no software that can take full advantage of network theory approaches for the analysis of chromatin interaction datasets. To fill this gap, we developed a web-based application, QuIN, which enables: 1) building and visualizing chromatin interaction networks, 2) annotating networks with user-provided private and publicly available functional genomics and interaction datasets, 3) querying network components based on gene name or chromosome location, and 4) utilizing network based measures to identify and prioritize critical regulatory targets and their direct and indirect interactions. <b>AVAILABILITY:</b> QuIN’s web server is available at <a href="http://quin.jax.org" target="_blank">http://quin.jax.org</a> QuIN is developed in Java and JavaScript, utilizing an Apache Tomcat web server and MySQL database and the source code is available under the GPLV3 license available on GitHub: <a href="https://github.com/UcarLab/QuIN/" target="_blank">https://github.com/UcarLab/QuIN/</a>.</p></div

Comparison of ChIA-PET gene targets with nearest gene targets.

<p>(A) A cartoon describing different approaches to associate NCVs with gene targets (Nearest TSS, Direct Targets, & Indirect Targets). (B) Enrichment p-values (based on Fisher’s exact test) of cancer related genes (known oncogenes (green), tumor suppressor genes (blue), poor prognosis genes (red), and the combined gene list (purple)) among NCV gene targets obtained via nearest TSS, direct target, indirect target associations. (C) Boxplot showing the differential expression (between cancer and normal tissues) for NCV target genes obtained via nearest TSS, direct target, indirect target associations.</p

A screenshot of QuIN’s web interface highlighting its features.

<p>(1) menus for uploading data and building networks, (2) options for visualizing and annotating a network, (3) target discovery menu for visualizing and exporting direct and indirect targets from source annotations to target annotations (4) network visualization panel, (5) options for searching, querying, or exporting the network, (6) the menu for performing GO Enrichment Analysis on the current subnetwork, (7) tools for summarizing network construction statistics, centrality measures and enrichment of interactions between annotations, (8) dialog box showing additional information about a selected node, including centrality measures, SNPs, and associated diseases.</p

Data flow diagram of QuIN.

<p>QuIN allows users to upload diverse data types and formats and it enables building, querying, annotating, and analyzing chromatin interaction networks. QuIN also integrates publically available databases for network annotation and enrichment.</p