Chromatin Immunoprecipitation (ChIP) Assay Kit

Protocol tips

Upstream tips	Protocol tips	Downstream tips
	-Just because an antibody works well in a Western blot does not always indicate it will perform well in chromatin immunoprecipitation. Unlike a Western blot that detects proteins that have been denatured, a ChIP antibody must recognize the target protein in its native state. Use ChIP-validated antibody. -Use 2-10 μg of your ChIP antibody depending on the abundance of your protein target.

Protocol tips
-Just because an antibody works well in a Western blot does not always indicate it will perform well in chromatin immunoprecipitation. Unlike a Western blot that detects proteins that have been denatured, a ChIP antibody must recognize the target protein in its native state. Use ChIP-validated antibody. -Use 2-10 μg of your ChIP antibody depending on the abundance of your protein target.

Publication protocol

Binding and Expression Target Analysis (BETA) [30] is an integrative tool for the analysis of transcription factors and chromatin regulator binding sites from ChIP-Seq by correlating with differentially expressed genes from RNA-Seq. The main purpose of this tool is to determine the activating and repressing function of each gene, which is detected using a nonparametric statistical test.

For this study, BETA was used for the integration of ChIP with RNA-Seq. Peak files of each cell line were correlated with their corresponding differentially expressed gene data according to their methylation state. The operation was carried out using hg19 as the reference genome, and by setting the Benjamini-Hochberg false discovery rate (FDR) for differentially expressed genes at ≤ 0.05. The result from this analysis consisted of a list of upregulated and down regulated genes along with their ranks based on the regulatory potential of factor binding and differential expression upon factor binding.

Enrichment analysis of high-throughput data was calculated inclusive of parameters like Chi-square, Fisher's exact test, Binomial probability and Hypergeometric distribution [31]. The genes identified from high-throughput screening are then annotated with Gene Ontology (GO) [32] terms. Cytoscape [33], an open source tool for visualization and analysis of complex networks, was used for the analysis of expressed gene data network. Cytoscape gives the ability to integrate arbitrary data on the graph, while serving as a platform for its visual representation. Moreover, the interface has the means to implement external methods in the form of plug-ins. Biological Networks Gene Ontology tool (BinGO) [34], a plugin for ontology analysis in Cytoscape, was used for ontological analysis of biological processes, and the connections between selected genes were studied using GeneMANIA [35], a tool for generating hypothesis on gene function and analysis of gene sets. For BinGO, overrepresented genes were specified for the visualization, which were selected using a hypergeometric test, with an FDR of 0.05. GO biological process was selected as the preferred ontology process for Homo sapiens, exclusively. GeneMANIA parameters were specifically restricted to genetic interactions, physical interactions, and shared pathways, to further isolate and study relevant connections between selected genes. All weightage provided were set to automatic. Comparison between the upregulated and downregulated genes of high grade data, and analysis of key genes were visualised in the form of a Venn diagram using the tool FunRich [36], an enrichment tool which provides graphical output for data visualization. FunRich was also used for assigning ontology process to a few selected genes from UniProt database [37] for an FDR of ≤ 0.05. Pathway analysis was conducted using ClueGo [38] by selecting KEGG pathways for the desired ontology. Genes that occur below a threshold of p-value ≤ 0.05 were investigated at global level network specificity. Pathways relevant to cancer were investigated using Pathview [39], with the help of individual cell line gene expression data from DEGUST (http://degust.erc.monash.edu/) using edgeR [40]. HGPEC [41], a plugin for Cytoscape which uses a random walk with restart on heterogeneous data (RWRH) algorithm, was used to predict the disease-gene association on heterogeneous network. In this method, a list of known genes from given diseases are used as training data. A complete different set of genes, which do not appear on the training set of genes, serve as the candidate gene set. The RWRH algorithm then uses the training data to identify and classify all candidate genes and disease in the heterogeneous network.

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Download the product protocol from Merck Millipore for Chromatin Immunoprecipitation (ChIP) Assay Kit below.

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