Year-end Sale % OFF 
Abstract
Transcription factors (TFs) play an important role in gene regulation. The interconnections among TFs, chromatin interactions, epigenetic marks and cis-regulatory elements form a complex gene transcription apparatus. Our previous work, ChIP-Array, combined TF binding and transcriptome data to construct gene regulatory networks (GRNs). Here we present an enhanced version, ChIP-Array 2, to integrate additional types of omics data including long-range chromatin interaction, open chromatin region and histone modification data to dissect more comprehensive GRNs involving diverse regulatory components. Moreover, we substantially extended our motif database for human, mouse, rat, fruit fly, worm, yeast and Arabidopsis, and curated large amount of omics data for users to select as input or backend support. With ChIP-Array 2, we compiled a library containing regulatory networks of 18 TFs/chromatin modifiers in mouse embryonic stem cell (mESC). The web server and the mESC library are publicly free and accessible athttp://jjwanglab.org/chip-array.
Highlights
Deciphering gene regulatory network (GRN) is crucial to understanding the mechanisms of various biological processes and the onset of diseases
For instance, genome-wide open chromatin regions are helpful to improve the quality of Transcription factors (TFs)-target identification, since TF binding can be impeded and dissociated by nucleosome [8], and most of TF binding sites (TFBSs) identified by the Encyclopedia of DNA Elements (ENCODE) consortium are located in high DNA-accessible regions [9]
In addition to directly determining the targets as the intersection of TFBSenriched and differentially expressed genes (DEGs) in previous version, we offer another target detection method, ‘Rank Product’, which is based on relative peak position to transcription start site (TSS), peak intensity and gene expression change [2,15]
Summary
Deciphering gene regulatory network (GRN) is crucial to understanding the mechanisms of various biological processes and the onset of diseases. In spite of the development of high-throughput technologies and the emergence of multiple omics data conducted on different regulatory factors, existing tools have not sufficiently integrated those data for GRN construction Most of these tools either combine only ChIP-X and transcriptome data to reduce the false-positive rate [2,10,11,12], or require a large number of samples to build their models for the identification of interactions among different types of factors, such as genetic variation, DNA methylation and. Enriched Gene Ontology (GO) and Pathways from the resulting GRN are displayed in the result page With this new web server, we constructed a network library containing regulatory networks of 18 TFs/chromatin modifiers in mouse embryonic stem cell (mESC) and made it freely accessible. Complement to single factor analysis, we offer an opportunity for users to study the synergy among regulatory factors by co-occupancy analysis (Supplementary Section 1.3)
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
