GREAT improves functional interpretation of cis-regulatory regions

By Cory Y. McLean, Dave Bristor, M. Hiller, S. Clarke, B. Schaar, C. B. Lowe, Aaron M. Wenger, Gill Bejerano. Nature Biotechnology, 2010

4,375 citations recorded by the source corpus.

We developed the Genomic Regions Enrichment of Annotations Tool (GREAT) to analyze the functional significance of cis-regulatory regions identified by localized measurements of DNA binding events across an entire genome. Whereas previous methods took into account only binding proximal to genes, GREAT is able to properly incorporate distal binding sites and control for false positives using a binomial test over the input genomic regions. GREAT incorporates annotations from 20 ontologies and is available as a web application. Applying GREAT to data sets from chromatin immunoprecipitation coupled with massively parallel sequencing (ChIP-seq) of multiple transcription-associated factors, including SRF, NRSF, GABP, Stat3 and p300 in different developmental contexts, we recover many functions of these factors that are missed by existing gene-based tools, and we generate testable hypotheses. The utility of GREAT is not limited to ChIP-seq, as it could also be applied to open chromatin, localized epigenomic markers and similar functional data sets, as well as comparative genomics sets.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/3e8749ebe886d27c37d96f870cf8c841fdb376a1

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Citations in the corpus

EPD and EPDnew, high-quality promoter resources in the next-generation sequencing era (2012, methodology): “As in the past, we provide for each promoter a single, carefully chosen reference TSS position in order to support regulatory sequence analysis platforms such as RSAT (16) or GREAT (17) which rely on single-base TSS annotation for promoter sequence extraction.”
β-Glucan Reverses the Epigenetic State of LPS-Induced Immunological Tolerance (2016, positive): “Gene ontology on dynamic enhancer clusters was performed using GREAT (McLean et al., 2010).”
ChIPBase v2.0: decoding transcriptional regulatory networks of non-coding RNAs and protein-coding genes from ChIP-seq data (2016, background): “More than half of the observed binding events occurred on protein coding genes are distally binding (28).”
ChIPBase: a database for decoding the transcriptional regulation of long non-coding RNA and microRNA genes from ChIP-Seq data (2012, methodology): “The same strategy was used for lncRNAs; we chose a 30-kb region upstream and a 10-kb region downstream of the TSS of each lncRNA as the regulatory domain (32) of each lncRNA.”
PCGF Homologs, CBX Proteins, and RYBP Define Functionally Distinct PRC1 Family Complexes (2012, methodology): “(C) GO biological process and mouse phenotype analysis of the targeted genomic regions identified in PCGF1, PCGF2, PCGF4, PCGF5, and PCGF6 ChIP-seq, using GREAT (McLean et al., 2010).”
Dynamic DNA methylation across diverse human cell lines and tissues (2013, methodology): “We used GREAT (Genomic Regions Enrichment of An- notations Tool) (McLean et al. 2010) to determine if these methylated non-CpG cytosines are associated with any functional en- richment and found that loci in brain tissue are found near genes enriched for ‘‘blood vessel development’’ (GO:0001568,…”
Analysing and interpreting DNA methylation data (2012, methodology): “Alternatively, we can use the GREAT Web server 115 , which internally maps genomic regions to genes and sta‑ tistically controls for the fact that genes differ in size and in their relative distance to each other.”
Enrichr: a comprehensive gene set enrichment analysis web server 2016 update (2016, methodology): “The updated version of Enrichr features similar functionality developed for the popular tool Genomic Regions Enrichment of Annotations Tool (GREAT) (33) by allowing users to upload BED files describing genomics region peaks.”