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Quant Biol    2013, Vol. 1 Issue (1) : 54-70    https://doi.org/10.1007/s40484-013-0006-2
REVIEW
Computational methodology for ChIP-seq analysis
Hyunjin Shin1, Tao Liu1, Xikun Duan2, Yong Zhang2, X. Shirley Liu1()
1. Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute/Harvard School of Public Health, Boston, MA 02115, USA; 2. Department of Bioinformatics, School of Life Science and Technology, Tongji University, Shanghai 200092, China
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Abstract

Chromatin immunoprecipitation coupled with massive parallel sequencing (ChIP-seq) is a powerful technology to identify the genome-wide locations of DNA binding proteins such as transcription factors or modified histones. As more and more experimental laboratories are adopting ChIP-seq to unravel the transcriptional and epigenetic regulatory mechanisms, computational analyses of ChIP-seq also become increasingly comprehensive and sophisticated. In this article, we review current computational methodology for ChIP-seq analysis, recommend useful algorithms and workflows, and introduce quality control measures at different analytical steps. We also discuss how ChIP-seq could be integrated with other types of genomic assays, such as gene expression profiling and genome-wide association studies, to provide a more comprehensive view of gene regulatory mechanisms in important physiological and pathological processes.
Corresponding Author(s): Liu X. Shirley,Email:xsliu@jimmy.harvard.edu   
Issue Date: 05 March 2013
 Cite this article:   
Hyunjin Shin,Tao Liu,Xikun Duan, et al. Computational methodology for ChIP-seq analysis[J]. Quant Biol, 2013, 1(1): 54-70.
 URL:  
https://academic.hep.com.cn/qb/EN/10.1007/s40484-013-0006-2
https://academic.hep.com.cn/qb/EN/Y2013/V1/I1/54
Fig.1  A schematic of peak modelling for ChIP-seq.
(A) The bimodal distributions of+ and – sequence reads (red and blue arrows, respectively) surrounding a transcription factor binding center (marked by the yellow vertical arrow). The distance () between the summits of the+ and – distributions is considered to be an estimate of the length of DNA fragments pulled down by the antibody.
(B) The ChIP enrichment signal can be obtained as the count of the+ and- sequence reads extended by the estimated at every base.
Fig.1  A schematic of peak modelling for ChIP-seq.
(A) The bimodal distributions of+ and – sequence reads (red and blue arrows, respectively) surrounding a transcription factor binding center (marked by the yellow vertical arrow). The distance () between the summits of the+ and – distributions is considered to be an estimate of the length of DNA fragments pulled down by the antibody.
(B) The ChIP enrichment signal can be obtained as the count of the+ and- sequence reads extended by the estimated at every base.
Fig.2  An UCSC genome browser snapshot of ChIP enrichments of NF-κB and H3K79me2 in human lymphoblastoid cell line GM12878 from ENCODE data.
The bottom gene track shows RefSeq genes near the binding sites. While NF-κB shows sharp binding patterns (the 1st green track from the top), H3K79me2 diffuses over a broad region (the 3rd green track). The black horizontal bars represent peaks called by MACS. The control tracks of DNA inputs (the 2nd and 4th green tracks) are used to model the background including the chromatin bias in ChIP-seq. The significance of binding site detection is estimated considering the background from the control; therefore, region A (near chr11 62610000) of NF-κB is not identified as a peak because its background level is relatively high compared to region B (near chr11 62625000) although the ChIP enrichments of these regions are similar.
Fig.2  An UCSC genome browser snapshot of ChIP enrichments of NF-κB and H3K79me2 in human lymphoblastoid cell line GM12878 from ENCODE data.
The bottom gene track shows RefSeq genes near the binding sites. While NF-κB shows sharp binding patterns (the 1st green track from the top), H3K79me2 diffuses over a broad region (the 3rd green track). The black horizontal bars represent peaks called by MACS. The control tracks of DNA inputs (the 2nd and 4th green tracks) are used to model the background including the chromatin bias in ChIP-seq. The significance of binding site detection is estimated considering the background from the control; therefore, region A (near chr11 62610000) of NF-κB is not identified as a peak because its background level is relatively high compared to region B (near chr11 62625000) although the ChIP enrichments of these regions are similar.
Fig.3  A sample analysis for two replicates of NF-κB ChIP-seq from ENCODE data.
(A) The Venn diagram of the peak sets identified from the two individual replicates at the same cut-off for peak calling (7281 and 7525 peaks, respectively). A large intersection indicates high consistency between the replicates (e.g.,>50%).
(B) Another consistency measure is to see the pair-wise correlation of ChIP enrichments of the replicates. Each dot represents the average ChIP enrichments of the replicates every 2 kb. Consistent replicates show a tight distribution around the local regression line (red), resulting in a large correlation coefficient (e.g., 0.86 in this example).
Fig.3  A sample analysis for two replicates of NF-κB ChIP-seq from ENCODE data.
(A) The Venn diagram of the peak sets identified from the two individual replicates at the same cut-off for peak calling (7281 and 7525 peaks, respectively). A large intersection indicates high consistency between the replicates (e.g.,>50%).
(B) Another consistency measure is to see the pair-wise correlation of ChIP enrichments of the replicates. Each dot represents the average ChIP enrichments of the replicates every 2 kb. Consistent replicates show a tight distribution around the local regression line (red), resulting in a large correlation coefficient (e.g., 0.86 in this example).
Fig.4  Useful QC measures for ChIP-seq peak calling.
(A) The average PhastCons score at TF binding sites can be used to assess the quality of ChIP-seq peak calls. The plot indicates that the center of NF-κB binding sites is more evolutionarily conserved than the background.
(B) If ChIP-seq is successful and the factor of interest has specific DNA binding motifs, the motifs should be significantly enriched near the summits of detected binding sites. This example shows that a NF-κB binding motif registered in JASPAR database was found at NF-κB binding sites.
(C) The pie chart visualizes the distribution of H3K36me3 peaks over different categories of elements such as promoter, UTRs, coding exon, and intron. Since H3K36me3 is associated with transcriptional elongation, its peaks are primarily present in exons (i.e., 9.6% in the pie chart) and introns (i.e., 76.7%).
(D) This trend was also observed using the meta-gene plot of H3K36me3 ChIP enrichment. Every gene was normalized to have the same length of 3 kb and then the average ChIP signal was profiled on the meta-gene including 1 kb upstream and downstream of TSS and TTS. The red and purple lines represent the average ChIP enrichments of H3K36me3 on highly expressed (top 10%, red) and lowly expressed (bottom 10%, purple) genes, respectively, which shows that H3K36me3 is positively correlated with gene expression levels.
Fig.4  Useful QC measures for ChIP-seq peak calling.
(A) The average PhastCons score at TF binding sites can be used to assess the quality of ChIP-seq peak calls. The plot indicates that the center of NF-κB binding sites is more evolutionarily conserved than the background.
(B) If ChIP-seq is successful and the factor of interest has specific DNA binding motifs, the motifs should be significantly enriched near the summits of detected binding sites. This example shows that a NF-κB binding motif registered in JASPAR database was found at NF-κB binding sites.
(C) The pie chart visualizes the distribution of H3K36me3 peaks over different categories of elements such as promoter, UTRs, coding exon, and intron. Since H3K36me3 is associated with transcriptional elongation, its peaks are primarily present in exons (i.e., 9.6% in the pie chart) and introns (i.e., 76.7%).
(D) This trend was also observed using the meta-gene plot of H3K36me3 ChIP enrichment. Every gene was normalized to have the same length of 3 kb and then the average ChIP signal was profiled on the meta-gene including 1 kb upstream and downstream of TSS and TTS. The red and purple lines represent the average ChIP enrichments of H3K36me3 on highly expressed (top 10%, red) and lowly expressed (bottom 10%, purple) genes, respectively, which shows that H3K36me3 is positively correlated with gene expression levels.
Fig.5  A distance-based method for inferring the role of a sharply binding TF as a transcriptional activator or repressor.
(A) Each curve is the cumulative distribution of the distances from genes to the nearest AR binding sites in prostate cancer cell line LNCaP. The red and green colors correspond to the sets of genes that are up- and down-regulated by DHT treatment, respectively. The black dotted line indicates the distance distribution of all genes, which can be used as a background distribution. From this analysis, it is can be seen that AR more directly regulates the up-regulated genes than the down-regulated ones.
(B) A similar analysis was done with breast cancer cell line MCF-7. When it compared to AR (A), ER regulates the up- and down-regulated genes by E2 treatment almost equally.
Fig.5  A distance-based method for inferring the role of a sharply binding TF as a transcriptional activator or repressor.
(A) Each curve is the cumulative distribution of the distances from genes to the nearest AR binding sites in prostate cancer cell line LNCaP. The red and green colors correspond to the sets of genes that are up- and down-regulated by DHT treatment, respectively. The black dotted line indicates the distance distribution of all genes, which can be used as a background distribution. From this analysis, it is can be seen that AR more directly regulates the up-regulated genes than the down-regulated ones.
(B) A similar analysis was done with breast cancer cell line MCF-7. When it compared to AR (A), ER regulates the up- and down-regulated genes by E2 treatment almost equally.
Fig.6  An example analysis for the ChIP-seq data of two collaborating factors.
(A) The Venn diagram of Rev-erbα and HDAC3 (histone deacetylase 3) binding in mouse liver. This shows that Rev-erbα and HDAC3 are highly colocalized. However, this example is an extreme case for highly collaborating factors. In general, collaborating factor ChIP-Seq peaks tend to show a smaller intersection than that between two replicates of the same factor.
(B) The scatter plot of the ChIP-seq enrichments of the factors at their union binding sites (blue dots) and a regression line (red line). The correlation coefficient of these two factors was also calculated (0.89).
(C) Heatmap analysis for Rev-erbα and HDAC3 binding sites. The heatmaps also confirm the high association of these two factors (left and right). The binding sites with similar binding patterns are grouped using -means clustering ( = 4) and distinguished by yellow horizontal lines. The 1st and 2nd groups reflect strand bias in binding. The individual groups can be further analyzed by associating with differentially expressed genes.
Fig.6  An example analysis for the ChIP-seq data of two collaborating factors.
(A) The Venn diagram of Rev-erbα and HDAC3 (histone deacetylase 3) binding in mouse liver. This shows that Rev-erbα and HDAC3 are highly colocalized. However, this example is an extreme case for highly collaborating factors. In general, collaborating factor ChIP-Seq peaks tend to show a smaller intersection than that between two replicates of the same factor.
(B) The scatter plot of the ChIP-seq enrichments of the factors at their union binding sites (blue dots) and a regression line (red line). The correlation coefficient of these two factors was also calculated (0.89).
(C) Heatmap analysis for Rev-erbα and HDAC3 binding sites. The heatmaps also confirm the high association of these two factors (left and right). The binding sites with similar binding patterns are grouped using -means clustering ( = 4) and distinguished by yellow horizontal lines. The 1st and 2nd groups reflect strand bias in binding. The individual groups can be further analyzed by associating with differentially expressed genes.
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