ChEA: transcription factor regulation inferred from integrating genome-wide ChIP-X experiments

By Alexander Lachmann, Huilei Xu, Jayanth Krishnan, Seth I. Berger, Amin R. Mazloom, Avi Ma’ayan. Bioinform., 2010

982 citations recorded by the source corpus.

MOTIVATION Experiments such as ChIP-chip, ChIP-seq, ChIP-PET and DamID (the four methods referred herein as ChIP-X) are used to profile the binding of transcription factors to DNA at a genome-wide scale. Such experiments provide hundreds to thousands of potential binding sites for a given transcription factor in proximity to gene coding regions. RESULTS In order to integrate data from such studies and utilize it for further biological discovery, we collected interactions from such experiments to construct a mammalian ChIP-X database. The database contains 189,933 interactions, manually extracted from 87 publications, describing the binding of 92 transcription factors to 31,932 target genes. We used the database to analyze mRNA expression data where we perform gene-list enrichment analysis using the ChIP-X database as the prior biological knowledge gene-list library. The system is delivered as a web-based interactive application called ChIP Enrichment Analysis (ChEA). With ChEA, users can input lists of mammalian gene symbols for which the program computes over-representation of transcription factor targets from the ChIP-X database. The ChEA database allowed us to reconstruct an initial network of transcription factors connected based on shared overlapping targets and binding site proximity. To demonstrate the utility of ChEA we present three case studies. We show how by combining the Connectivity Map (CMAP) with ChEA, we can rank pairs of compounds to be used to target specific transcription factor activity in cancer cells. AVAILABILITY The ChEA software and ChIP-X database is freely available online at: http://amp.pharm.mssm.edu/lib/chea.jsp.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/8120fc62e2f041fab701ac074e8ac1aea7a26210

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Citations in the corpus

miRNet 2.0: network-based visual analytics for miRNA functional analysis and systems biology (2020, positive): “0 (30), ENCODE (31), JASPAR (32) and ChEA (33).”
Large-Scale Exome Sequencing Study Implicates Both Developmental and Functional Changes in the Neurobiology of Autism (2019, methodology): “First, we searched for target sets in available databases: ChEA (Lachmann et al., 2010) and ENCODE (Birney et al.”
Integrative Analysis Identifies Four Molecular and Clinical Subsets in Uveal Melanoma. (2018, methodology): “(iv) ChEA (Lachmann et al., 2010): Data from the Gene Expression Atlas (Petryszak et al.”
Network-based inference of protein activity helps functionalize the genetic landscape of cancer (2016, methodology): “…providing accurate, tissue-specific assessments of protein regulons should be equally effective, we found that ARACNe outperformed competing algorithms that derive regulons from genome-wide ChIP databases, including ChEA 25 and ENCODE 26 , and literature curated Ingenuity networks 27 (see below).”
Benchmark and integration of resources for the estimation of human transcription factor activities (2018, background): “Another strategy is high-throughput measurements of TF-DNA binding such as chromatin immunoprecipitation (ChIP) (Johnson et al. 2007; Lachmann et al. 2010) or”
Enrichr: a comprehensive gene set enrichment analysis web server 2016 update (2016, methodology): “We then performed enrichment analysis against the ChIP-X enrichment analysis (ChEA) gene set library, ranking TFs with the different scoring methods (32).”
Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool (2013, methodology): “This analysis shows interesting signature patterns: first, we noticed a cluster of transcriptional regulators from ChEA that only appears for the cancer cell lines of ovarian, skin and small intestine cancers.”
NetworkAnalyst 3.0: a visual analytics platform for comprehensive gene expression profiling and meta-analysis (2019, methodology): “…(19) and miRTarbase (20) have been used to obtain experimentally validated miRNA–gene target information, while ENCODE (21), JASPAR (22) and CHEA (23) have been used to obtain TF–gene target Users can double click a meta-node (large semi-transparent circles) to view all its associated genes…”