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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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ChIP-seq experiment with NEXT-peak model parameters. A genomic location of the center of the TF is denoted as μ. Green bi-directional arrows represent cross-links between a TF and a genomic sequence. Cross-links are assumed to be normally distributed with standard deviation σ. Tags are shown as small black rectangles at the 5′ end of fragments. The distance from a cross-link to a tag location is assumed to be exponentially distributed with mean β. When tags are mapped to a reference genome, then tags are projected onto the corresponding genomic locations. Blue arrows represent “left” tags mapped on the forward strand; red arrows, “right” tags mapped on the backward strand. The tag distribution is the NEXT-peak under the previous assumptions.
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Figure 7: ChIP-seq experiment with NEXT-peak model parameters. A genomic location of the center of the TF is denoted as μ. Green bi-directional arrows represent cross-links between a TF and a genomic sequence. Cross-links are assumed to be normally distributed with standard deviation σ. Tags are shown as small black rectangles at the 5′ end of fragments. The distance from a cross-link to a tag location is assumed to be exponentially distributed with mean β. When tags are mapped to a reference genome, then tags are projected onto the corresponding genomic locations. Blue arrows represent “left” tags mapped on the forward strand; red arrows, “right” tags mapped on the backward strand. The tag distribution is the NEXT-peak under the previous assumptions.

Mentions: The above distribution is a marginal distribution of the normal-exponential joint density, which we call a “normal-exponential” distribution in short. Figure 7 shows a schematic representation for the role of parameters σ and β in a ChIP-seq experiment. Figure 1a shows a normal-exponential density for both left and right tags (as discussed in Results).


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

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

ChIP-seq experiment with NEXT-peak model parameters. A genomic location of the center of the TF is denoted as μ. Green bi-directional arrows represent cross-links between a TF and a genomic sequence. Cross-links are assumed to be normally distributed with standard deviation σ. Tags are shown as small black rectangles at the 5′ end of fragments. The distance from a cross-link to a tag location is assumed to be exponentially distributed with mean β. When tags are mapped to a reference genome, then tags are projected onto the corresponding genomic locations. Blue arrows represent “left” tags mapped on the forward strand; red arrows, “right” tags mapped on the backward strand. The tag distribution is the NEXT-peak under the previous assumptions.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 7: ChIP-seq experiment with NEXT-peak model parameters. A genomic location of the center of the TF is denoted as μ. Green bi-directional arrows represent cross-links between a TF and a genomic sequence. Cross-links are assumed to be normally distributed with standard deviation σ. Tags are shown as small black rectangles at the 5′ end of fragments. The distance from a cross-link to a tag location is assumed to be exponentially distributed with mean β. When tags are mapped to a reference genome, then tags are projected onto the corresponding genomic locations. Blue arrows represent “left” tags mapped on the forward strand; red arrows, “right” tags mapped on the backward strand. The tag distribution is the NEXT-peak under the previous assumptions.
Mentions: The above distribution is a marginal distribution of the normal-exponential joint density, which we call a “normal-exponential” distribution in short. Figure 7 shows a schematic representation for the role of parameters σ and β in a ChIP-seq experiment. Figure 1a shows a normal-exponential density for both left and right tags (as discussed in Results).

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