Limits...
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.

Show MeSH

Related in: MedlinePlus

A plot of mean distance between top peaks and motif. (a) STAT1. (b) NRSF. (c) ZNF143. Mean distances are average distances between motif sites and estimated sties, where estimated sites contain a motif site within 250 bp distance. (Small distances are desirable.)
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3672025&req=5

Figure 4: A plot of mean distance between top peaks and motif. (a) STAT1. (b) NRSF. (c) ZNF143. Mean distances are average distances between motif sites and estimated sties, where estimated sites contain a motif site within 250 bp distance. (Small distances are desirable.)

Mentions: FigureĀ 4 shows mean distances for TP peaks ranked up to 10,000. In all three datasets, for the most of the range up to rank 10,000, NEXT-peak had the smallest mean distances. Note that other programs did not show the same consistency among three datasets in terms of mean distances. For example, MTC was the second best in STAT1 but performed poorly for NRSF; QuEST was the second best in ZNF143 but performed poorly for STAT1.


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 distance between top peaks and motif. (a) STAT1. (b) NRSF. (c) ZNF143. Mean distances are average distances between motif sites and estimated sties, where estimated sites contain a motif site within 250 bp distance. (Small distances are desirable.)
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 4: A plot of mean distance between top peaks and motif. (a) STAT1. (b) NRSF. (c) ZNF143. Mean distances are average distances between motif sites and estimated sties, where estimated sites contain a motif site within 250 bp distance. (Small distances are desirable.)
Mentions: FigureĀ 4 shows mean distances for TP peaks ranked up to 10,000. In all three datasets, for the most of the range up to rank 10,000, NEXT-peak had the smallest mean distances. Note that other programs did not show the same consistency among three datasets in terms of mean distances. For example, MTC was the second best in STAT1 but performed poorly for NRSF; QuEST was the second best in ZNF143 but performed poorly for STAT1.

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