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OccuPeak: ChIP-Seq peak calling based on internal background modelling.

de Boer BA, van Duijvenboden K, van den Boogaard M, Christoffels VM, Barnett P, Ruijter JM - PLoS ONE (2014)

Bottom Line: However, the GC-content of reads in Input-seq datasets deviates significantly from that in ChIP-seq datasets.Moreover, we observed that a commonly used peak calling program performed equally well when the use of a simulated uniform background set was compared to an Input-seq dataset.Moreover, peaks called by OccuPeak were significantly enriched with cardiac disease-associated SNPs.

View Article: PubMed Central - PubMed

Affiliation: Department of Anatomy, Embryology & Physiology, Academic Medical Centre, Amsterdam, The Netherlands.

ABSTRACT

Unlabelled: ChIP-seq has become a major tool for the genome-wide identification of transcription factor binding or histone modification sites. Most peak-calling algorithms require input control datasets to model the occurrence of background reads to account for local sequencing and GC bias. However, the GC-content of reads in Input-seq datasets deviates significantly from that in ChIP-seq datasets. Moreover, we observed that a commonly used peak calling program performed equally well when the use of a simulated uniform background set was compared to an Input-seq dataset. This contradicts the assumption that input control datasets are necessary to fatefully reflect the background read distribution. Because the GC-content of the abundant single reads in ChIP-seq datasets is similar to those of randomly sampled regions we designed a peak-calling algorithm with a background model based on overlapping single reads. The application, OccuPeak, uses the abundant low frequency tags present in each ChIP-seq dataset to model the background, thereby avoiding the need for additional datasets. Analysis of the performance of OccuPeak showed robust model parameters. Its measure of peak significance, the excess ratio, is only dependent on the tag density of a peak and the global noise levels. Compared to the commonly used peak-calling applications MACS and CisGenome, OccuPeak had the highest sensitivity in an enhancer identification benchmark test, and performed similar in an overlap tests of transcription factor occupation with DNase I hypersensitive sites and H3K27ac sites. Moreover, peaks called by OccuPeak were significantly enriched with cardiac disease-associated SNPs. OccuPeak runs as a standalone application and does not require extensive tweaking of parameters, making its use straightforward and user friendly.

Availability: http://occupeak.hfrc.nl.

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Related in: MedlinePlus

Performance of MACS using Input-seq and simulated input data.MACS was used to call peaks (only chromosome 1) using the p300(1) dataset. Heart Input-seq data or a simulated uniform background dataset were used as input control. The influence of the input control set on peak-calling performance was measured using overlap with DHSs as outlined in the legend of Figure 8.
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pone-0099844-g003: Performance of MACS using Input-seq and simulated input data.MACS was used to call peaks (only chromosome 1) using the p300(1) dataset. Heart Input-seq data or a simulated uniform background dataset were used as input control. The influence of the input control set on peak-calling performance was measured using overlap with DHSs as outlined in the legend of Figure 8.

Mentions: The observation that single reads show the GC-content of random regions suggests that single reads in ChIP-seq datasets occur randomly in the genome. This implies that background can be modelled on these low frequency reads. To test this hypothesis, we simulated a background set with a random-uniform background and used this set as an input control set. We used this simulated and an actual Input-seq dataset to compare the results on peak calling on the p300(1) dataset with the peak-calling program MACS (Figure 3).


OccuPeak: ChIP-Seq peak calling based on internal background modelling.

de Boer BA, van Duijvenboden K, van den Boogaard M, Christoffels VM, Barnett P, Ruijter JM - PLoS ONE (2014)

Performance of MACS using Input-seq and simulated input data.MACS was used to call peaks (only chromosome 1) using the p300(1) dataset. Heart Input-seq data or a simulated uniform background dataset were used as input control. The influence of the input control set on peak-calling performance was measured using overlap with DHSs as outlined in the legend of Figure 8.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4061025&req=5

pone-0099844-g003: Performance of MACS using Input-seq and simulated input data.MACS was used to call peaks (only chromosome 1) using the p300(1) dataset. Heart Input-seq data or a simulated uniform background dataset were used as input control. The influence of the input control set on peak-calling performance was measured using overlap with DHSs as outlined in the legend of Figure 8.
Mentions: The observation that single reads show the GC-content of random regions suggests that single reads in ChIP-seq datasets occur randomly in the genome. This implies that background can be modelled on these low frequency reads. To test this hypothesis, we simulated a background set with a random-uniform background and used this set as an input control set. We used this simulated and an actual Input-seq dataset to compare the results on peak calling on the p300(1) dataset with the peak-calling program MACS (Figure 3).

Bottom Line: However, the GC-content of reads in Input-seq datasets deviates significantly from that in ChIP-seq datasets.Moreover, we observed that a commonly used peak calling program performed equally well when the use of a simulated uniform background set was compared to an Input-seq dataset.Moreover, peaks called by OccuPeak were significantly enriched with cardiac disease-associated SNPs.

View Article: PubMed Central - PubMed

Affiliation: Department of Anatomy, Embryology & Physiology, Academic Medical Centre, Amsterdam, The Netherlands.

ABSTRACT

Unlabelled: ChIP-seq has become a major tool for the genome-wide identification of transcription factor binding or histone modification sites. Most peak-calling algorithms require input control datasets to model the occurrence of background reads to account for local sequencing and GC bias. However, the GC-content of reads in Input-seq datasets deviates significantly from that in ChIP-seq datasets. Moreover, we observed that a commonly used peak calling program performed equally well when the use of a simulated uniform background set was compared to an Input-seq dataset. This contradicts the assumption that input control datasets are necessary to fatefully reflect the background read distribution. Because the GC-content of the abundant single reads in ChIP-seq datasets is similar to those of randomly sampled regions we designed a peak-calling algorithm with a background model based on overlapping single reads. The application, OccuPeak, uses the abundant low frequency tags present in each ChIP-seq dataset to model the background, thereby avoiding the need for additional datasets. Analysis of the performance of OccuPeak showed robust model parameters. Its measure of peak significance, the excess ratio, is only dependent on the tag density of a peak and the global noise levels. Compared to the commonly used peak-calling applications MACS and CisGenome, OccuPeak had the highest sensitivity in an enhancer identification benchmark test, and performed similar in an overlap tests of transcription factor occupation with DNase I hypersensitive sites and H3K27ac sites. Moreover, peaks called by OccuPeak were significantly enriched with cardiac disease-associated SNPs. OccuPeak runs as a standalone application and does not require extensive tweaking of parameters, making its use straightforward and user friendly.

Availability: http://occupeak.hfrc.nl.

Show MeSH
Related in: MedlinePlus