ChIPseqSpikeInFree: A Spike-in Free ChIP-Seq Normalization Approach for Detecting Global Changes in Histone Modifications Background

https://github.com/stjude/ChIPseqSpikeInFree

Example:
> library(“ChIPseqSpikeInFree”)
> metaFile <- "/DATA/Data_Denise_ChIPSeq_Protocol2/Data_H3K27me3/sample_meta__part1.txt" > ChIPseqSpikeInFree(bamFiles = bams, chromFile = “hg19”, metaFile = metaFile, prefix = “k27”)
#–>ave.SF = 2.46
cat ${sample_id}.dedup.sorted.bed | wc -l #–>19887819
15000000/(19887819*2,46)=0,306597771
genomeCoverageBed -bg -scale 0.306597771 -i V_8_0_untreated_D1_H3K27me3.dedup.sorted.bed -g hg19.chromSizes > V_8_0_untreated_D1_H3K27me3.bedGraph
bedGraphToBigWig V_8_0_untreated_D1_H3K27me3.bedGraph hg19.chromSizes V_8_0_untreated_D1_H3K27me3.bw

A Spike-in Free ChIP-Seq Normalization Approach for Detecting Global Changes in Histone Modifications
Background

Traditional reads per million (RPM) normalization method is inappropriate for the evaluation of ChIP-seq data when the treatment or mutation has the global effect. Changes in global levels of histone modifications can be detected by using exogenous reference spike-in controls. However, most of the ChIP-seq studies have overlooked the normalization problem that have to be corrected with spike-in. A method that retrospectively renormalize data sets without spike-in is lacking.

We observed that some highly enriched regions were retained despite global changes by oncogenic mutations or drug treatment and that the proportion of reads within these regions was inversely associated with total histone mark levels. Therefore, we developped ChIPseqSpikeInFree, a novel ChIP-seq normalization method to effectively determine scaling factors for samples across various conditions and treatments, which does not rely on exogenous spike-in chromatin or peak detection to reveal global changes in histone modification occupancy. This method is capable of revealing the similar magnitude of global changes as the spike-in method.

In summary, ChIPseqSpikeInFree can estimate scaling factors for ChIP-seq samples without exogenous spike-in or without input. When ChIP-seq is done with spike-in protocol but high variation of Spike-In reads between samples are observed, ChIPseqSpikeInFree can help you determine a more reliable scaling factor than ChIP-Rx method. It’s not recommended to run ChIPseqSpikeInFree blindly without any biological evidences like Western Blotting to prove the global change at protein level between your control and treatment samples.

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