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The identification of differences between data sets can be challenging when global modification changes occur, such as in the case of studying the effects of chromatin modifying enzyme inhibitors. Additionally, inaccurate quantification of starting material or technical variation during processing results in variation across sample data.
Currently available bioinformatic-based normalization methods are not applicable for normalizing across data sets in these instances, and the only reliable way to overcome bias and variation is to add a known standard (spike-in) into all samples. Active Motif offers spike-in reagents for ATAC-Seq, ChIP-Seq, CUT&RUN, and CUT&Tag.
Overcome variation between ATAC-Seq datasets to compare and see actual differences.
Analyze ChIP-seq data with confidence and identify true biological difference between samples.
Analyze CUT&Tag-IT R-loop Assay datasets confidently to detect true biological differences.
