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

Abstract

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. AVAILABILITY: QuIN’s web server is available at http://quin.jax.org 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: https://github.com/UcarLab/QuIN/.

Chromatin interaction networks revealed unique connectivity patterns of broad H3K4me3 domains and super enhancers in 3D chromatin

“…ChIA-PET Tool 27 interactions were preferred over the alternative Mango 30 , since calls obtained via Mango were sparse and did not include as many broad domain or super enhancer interactions (Table S1 ). To minimize the number of false positive interactions, we instead filtered interaction calls using QuIN 31 , by selecting only those having both anchors overlapping DNase I hypersensitive sites (DHS) defined from DNAse-seq open chromatin peaks, which reduces false positive calls and likely captures active regulatory loci. These peaks where then used to define the nodes of the interaction networks, which were constructed using QuIN 31 .…”

“…To minimize the number of false positive interactions, we instead filtered interaction calls using QuIN 31 , by selecting only those having both anchors overlapping DNase I hypersensitive sites (DHS) defined from DNAse-seq open chromatin peaks, which reduces false positive calls and likely captures active regulatory loci. These peaks where then used to define the nodes of the interaction networks, which were constructed using QuIN 31 . MCF-7, K562, and GM12878 open chromatin peaks were called using MACS2 software 32 (version 2.1) after pooling replicates (GSE32970 and GSE29692 28 ).…”

“…Network metrics were calculated using QuIN 31 . Connectivity degree refers to the number of edges connected to a node.…”

TriPOINT: a software tool to prioritize important genes in pathways and their non-coding regulators

“…TriPOINT is implemented in Java, incorporating pathway graphs from the GRAPHITE (Sales et al , 2012) R package through RServe (Urbanek, 2003; ISSN 1609-395X). Methods from our software QuIN (Thibodeau et al , 2016) are utilized to integrate chromatin interaction data to identify non-coding regulators. Finally, the Cytoscape (Shannon, 2003) java application is used as a platform for visualization of TriPOINT JSON files which are easily imported and display pathways augmented with differential expression values and non-coding information (see Fig.…”

“…The first approach utilizes chromatin interaction loops from genome-wide assays such as ChIA-PET (Fullwood et al , 2009), HiC (Lieberman-Aiden et al , 2009) or HiChIP (Mumbach et al , 2016) datasets. Methods available from our software QuIN (Thibodeau et al , 2016) were employed to construct a chromatin interaction network to identify loci directly interacting with genes in a pathway. If chromatin interaction data are not yet available for the given cell type, TriPOINT attempts to identify non-coding regulators based on proximity, assigning non-coding regions provided by the user to genes within a user-defined distance from the transcription start site.…”