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

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

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

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

Pathway aberrations of murine melanoma cells observed in Paired-End diTag transcriptomes-2

<p><b>Copyright information:</b></p><p>Taken from "Pathway aberrations of murine melanoma cells observed in Paired-End diTag transcriptomes"</p><p>http://www.biomedcentral.com/1471-2407/7/109</p><p>BMC Cancer 2007;7():109-109.</p><p>Published online 26 Jun 2007</p><p>PMCID:PMC1929113.</p><p></p>ere added to the upper left corner of the gene box (green icon, with 4 numbers to represent a particular KEGG gene ID). Each square is associated with a library and highlighted with red, yellow, or blue color to indicate that gene isoforms are all matched, partially matched, or completely unmatched, respectively, by PETs of the library. By placing the cursor on a particular entity (mouse on), one can view the description of all gene isoforms in the gene box (II), or PET counts, in cpm (counts per million), of a corresponding gene isoform for each library (III & IV). In response to a clicking on a solid square, detailed mapping information is displayed (V). Hyperlinks in II lead to all KEGG pathway images that contain the selected gene isoform

<i>Xenopus tropicalis</i> Genome Re-Scaffolding and Re-Annotation Reach the Resolution Required for <i>In Vivo</i> ChIA-PET Analysis

<div><p>Genome-wide functional analyses require high-resolution genome assembly and annotation. We applied ChIA-PET to analyze gene regulatory networks, including 3D chromosome interactions, underlying thyroid hormone (TH) signaling in the frog <i>Xenopus tropicalis</i>. As the available versions of <i>Xenopus tropicalis</i> assembly and annotation lacked the resolution required for ChIA-PET we improve the genome assembly version 4.1 and annotations using data derived from the paired end tag (PET) sequencing technologies and approaches (e.g., DNA-PET [gPET], RNA-PET etc.). The large insert (~10Kb, ~17Kb) paired end DNA-PET with high throughput NGS sequencing not only significantly improved genome assembly quality, but also strongly reduced genome “fragmentation”, reducing total scaffold numbers by ~60%. Next, RNA-PET technology, designed and developed for the detection of full-length transcripts and fusion mRNA in whole transcriptome studies (ENCODE consortia), was applied to capture the 5' and 3' ends of transcripts. These amendments in assembly and annotation were essential prerequisites for the ChIA-PET analysis of TH transcription regulation. Their application revealed complex regulatory configurations of target genes and the structures of the regulatory networks underlying physiological responses. Our work allowed us to improve the quality of <i>Xenopus tropicalis</i> genomic resources, reaching the standard required for ChIA-PET analysis of transcriptional networks. We consider that the workflow proposed offers useful conceptual and methodological guidance and can readily be applied to other non-conventional models that have low-resolution genome data.</p></div

RNA-PET efficiently captures transcripts ends.

<p>A. Overlap between RNA-Seq reads and Ensembl and RNA-PET-based models. B. Demarcation of gene model boundaries by RNA-PET. The histogram shows the relative size of Ensembl gene models in bins of various sizes. C. Enrichment of RNA-Pol II around Ensembl gene models and RNA-PET-based models. This shows that RNA-Pol II density fits well with RNA-PET based models, but not Ensembl models.</p

Examples of genome annotation improvements.

<p>Track order: Ensembl models, RNA-PET based models, RNA-PET ditags and RNA-Seq reads density. A, B, C: <i>sumo1</i>, <i>cadm2</i> and <i>kiaa1958</i> loci. D: Un-annotated gene split over scaffold_1031 and scaffold_1460.</p

Benefit of genome re-annotation with RNA-PET for ChIA-PET analysis.

<p>A. Genomic view of an un-annotated gene. Track order: Ensembl genes, RNA-PET-based models, ChIA-PET TR binding density, RNA Pol-II binding density, RNA-Seq reads density with (+T₃) and without (-T₃) THs treatment. B. Close up of TR binding sites. Track order: Ensembl genes, RNA-PET-based genes, location of ChIP-qPCR probes, RNA-PET PETs, TR binding density and RNA Pol-II binding density. C: ChIP-qPCR validation of TR binding at locations shown in B. Ab: antibody, T₃: 3’,5,3’ triiodothyronine treatment. D: Transcriptional induction assayed by RT-qPCR.</p