2016
DOI: 10.1371/journal.pcbi.1004809
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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 facilita… Show more

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Cited by 10 publications
(6 citation statements)
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“…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 .…”
Section: Methodsmentioning
confidence: 99%
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“…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 .…”
Section: Methodsmentioning
confidence: 99%
“…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 ).…”
Section: Methodsmentioning
confidence: 99%
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“…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.…”
Section: Methodsmentioning
confidence: 99%
“…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.…”
Section: Methodsmentioning
confidence: 99%