Using the ENCODE database, in which transcription factor binding has been assessed in multiple cell lines by ChIP-seq, the SNPs in IGFBP3, GRB10, CYP19A1 and LHX4 fall within, or close to, transcription binding sites (Table 5).
For ChIP-seq datasets from multiple cell lines, T-KDE identifies genomic regions where peak centers occur.
T-KDE is an efficient and effective method to predict constitutive protein binding sites using ChIP-seq peaks from multiple cell lines.
Although designed for identifying constitutive binding sites for a protein using ChIP-seq data from multiple cell lines, our method could also be used to identify genomic loci that have concentrations of different protein binding sites ("hot spots"), and conversely "cold spots", using multiple protein ChIP-seq data for the cell line.
Application of T-KDE to 22 proteins with ChIP-seq data from multiple cell lines located substantial numbers of constitutive binding sites for some TFs but almost none for others.
Our goal is different: we use the locations of ChIP-seq peak centers from multiple cell lines (from as few as 6 to as many as 132, in this case) to infer the location of constitutive binding sites.
In this paper, we use a KDE to find those genomic regions that contain the highest density of ChIP-seq peak centers from multiple cell lines/types for a given TF.
It is also found within a DNaseI hypersensitive cluster observed in several cell types and an RNA polymerase II transcription factor binding site assayed by ChIP-seq [ 26] in multiple cell lines suggesting that it affects transcription.
We applied T-KDE using a bandwidth of 100 bp to 22 factors with ChIP-seq data available from ENCODE for multiple cell lines with replicates.
Integration of multiple cell line ESR1 ChIP datasets also increases overlap with ESR1 ChIP-seq peaks from primary cancer samples, further supporting this approach as helpful in identifying true positive ESR1 binding sites in cell line systems.
