The PWM is widely used for TFBSs recognition in genomic sequences, e.g. to interpret ChIP-Seq data.
Although it is to be expected that histone marks might vary among tissues and cells, our observations are important for interpreting ChIP data in that they indicate the germline signal varies widely depending on the mark being assayed.
More generally, they make clear that great caution must be used when interpreting ChIP enrichment profiles for individual factors in isolation, as they will include factor-specific as well as collective contributions.
Methods for interpreting ChIP-seq data are currently under intensive development.
To simplify analyzing and interpreting ChIP-Seq data, which typically involves using multiple applications, we describe an integrated, open source, R-based analysis pipeline.
This question arises, for example, when interpreting ChIP-Seq or RNA-Seq data in functional terms.
Other reports [ 32– 34] have questioned the validity of such associations, recommending caution while interpreting ChIP-seq signals in highly expressed genomic regions.
Analysing and interpreting ChIP-seq data typically involves pre-treating the raw reads using multiple applications, which can include mapping of sequences to the human genome, filtering and quality control.
In particular, we have released Galaxy workflows for interpreting ChIP-seq data which use the same quality control (QC) and peak calling standards adopted by the modENCODE and ENCODE communities.
Hence, contrary to transcriptomics microarray data, where low log-ratio values are meaningful as long as the differences between conditions are statistically significant, when interpreting ChIP-on-chip and DNA methylation microarray data, the upper quantile is of most interest, as it generally comprises mostly enriched probes.
