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NEXT-peak: a normal-exponential two-peak model for peak-calling in ChIP-seq data.

Kim NK, Jayatillake RV, Spouge JL - BMC Genomics (2013)

Bottom Line: The model therefore estimates total strength of binding (even if some binding locations do not map uniquely into a reference genome, effectively censoring them); it also assigns an error to an estimated binding location.The model also provides a goodness-of-fit test, to screen out spurious peaks and to infer multiple binding events in a region.NEXT-peak is based on rigorous statistics, so its model also provides a principled foundation for a more elaborate statistical analysis of ChIP-seq data.

View Article: PubMed Central - HTML - PubMed

Affiliation: Mathematics and Statistics Department, Old Dominion University, Norfolk, VA 23529, USA. nxkim@odu.edu

ABSTRACT

Background: Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) can locate transcription factor binding sites on genomic scale. Although many models and programs are available to call peaks, none has dominated its competition in comparison studies.

Results: We propose a rigorous statistical model, the normal-exponential two-peak (NEXT-peak) model, which parallels the physical processes generating the empirical data, and which can naturally incorporate mappability information. The model therefore estimates total strength of binding (even if some binding locations do not map uniquely into a reference genome, effectively censoring them); it also assigns an error to an estimated binding location. The comparison study with existing programs on real ChIP-seq datasets (STAT1, NRSF, and ZNF143) demonstrates that the NEXT-peak model performs well both in calling peaks and locating them. The model also provides a goodness-of-fit test, to screen out spurious peaks and to infer multiple binding events in a region.

Conclusions: The NEXT-peak program calls peaks on any test dataset about as accurately as any other, but provides unusual accuracy in the estimated location of the peaks it calls. NEXT-peak is based on rigorous statistics, so its model also provides a principled foundation for a more elaborate statistical analysis of ChIP-seq data.

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A plot of mean bias between top peaks and motif. (a) STAT1. (b) NRSF. (c) ZNF143.The bias is the (signed) distance in bp between an estimated site and the nearest motif site. (Small biases are desirable.) For all datasets, NEXT-peak biases were near zero. NEXT-peak was the only program with near-zero bias for all three datasets.
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Figure 5: A plot of mean bias between top peaks and motif. (a) STAT1. (b) NRSF. (c) ZNF143.The bias is the (signed) distance in bp between an estimated site and the nearest motif site. (Small biases are desirable.) For all datasets, NEXT-peak biases were near zero. NEXT-peak was the only program with near-zero bias for all three datasets.

Mentions: FigureĀ 5 shows mean biases up to 10,000 peaks. As noted in the previous section, NEXT-peak is the only program showing small biases for all three datasets. Any other program shows a noticeable bias in at least one dataset. That is, for ZNF143, only NEXT-peak, QuEST, and HPeak had small biases, but QuEST and HPeak had noticeable biases in STAT1, making their performances highly dependent on the dataset at hand.


NEXT-peak: a normal-exponential two-peak model for peak-calling in ChIP-seq data.

Kim NK, Jayatillake RV, Spouge JL - BMC Genomics (2013)

A plot of mean bias between top peaks and motif. (a) STAT1. (b) NRSF. (c) ZNF143.The bias is the (signed) distance in bp between an estimated site and the nearest motif site. (Small biases are desirable.) For all datasets, NEXT-peak biases were near zero. NEXT-peak was the only program with near-zero bias for all three datasets.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3672025&req=5

Figure 5: A plot of mean bias between top peaks and motif. (a) STAT1. (b) NRSF. (c) ZNF143.The bias is the (signed) distance in bp between an estimated site and the nearest motif site. (Small biases are desirable.) For all datasets, NEXT-peak biases were near zero. NEXT-peak was the only program with near-zero bias for all three datasets.
Mentions: FigureĀ 5 shows mean biases up to 10,000 peaks. As noted in the previous section, NEXT-peak is the only program showing small biases for all three datasets. Any other program shows a noticeable bias in at least one dataset. That is, for ZNF143, only NEXT-peak, QuEST, and HPeak had small biases, but QuEST and HPeak had noticeable biases in STAT1, making their performances highly dependent on the dataset at hand.

Bottom Line: The model therefore estimates total strength of binding (even if some binding locations do not map uniquely into a reference genome, effectively censoring them); it also assigns an error to an estimated binding location.The model also provides a goodness-of-fit test, to screen out spurious peaks and to infer multiple binding events in a region.NEXT-peak is based on rigorous statistics, so its model also provides a principled foundation for a more elaborate statistical analysis of ChIP-seq data.

View Article: PubMed Central - HTML - PubMed

Affiliation: Mathematics and Statistics Department, Old Dominion University, Norfolk, VA 23529, USA. nxkim@odu.edu

ABSTRACT

Background: Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) can locate transcription factor binding sites on genomic scale. Although many models and programs are available to call peaks, none has dominated its competition in comparison studies.

Results: We propose a rigorous statistical model, the normal-exponential two-peak (NEXT-peak) model, which parallels the physical processes generating the empirical data, and which can naturally incorporate mappability information. The model therefore estimates total strength of binding (even if some binding locations do not map uniquely into a reference genome, effectively censoring them); it also assigns an error to an estimated binding location. The comparison study with existing programs on real ChIP-seq datasets (STAT1, NRSF, and ZNF143) demonstrates that the NEXT-peak model performs well both in calling peaks and locating them. The model also provides a goodness-of-fit test, to screen out spurious peaks and to infer multiple binding events in a region.

Conclusions: The NEXT-peak program calls peaks on any test dataset about as accurately as any other, but provides unusual accuracy in the estimated location of the peaks it calls. NEXT-peak is based on rigorous statistics, so its model also provides a principled foundation for a more elaborate statistical analysis of ChIP-seq data.

Show MeSH
Related in: MedlinePlus