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A computational model of quantitative chromatin immunoprecipitation (ChIP) analysis.

Xie J, Crooke PS, McKinney BA, Soltman J, Brandt SJ - Cancer Inform (2008)

Bottom Line: We developed a computational model of quantitative ChIP analysis to elucidate the factors contributing to the method's resolution.The most important variables identified by the model were, in order of importance, the spacing of the PCR primers, the mean length of the chromatin fragments, and, unexpectedly, the type of fragment width distribution, with very small DNA fragments and smaller amplicons providing the best resolution of TF binding.One of the major predictions of the model was also validated experimentally.

View Article: PubMed Central - PubMed

Affiliation: Department of Medicine, Vanderbilt University, Nashville, Tennessee 37232, USA.

ABSTRACT
Chromatin immunoprecipitation (ChIP) analysis is widely used to identify the locations in genomes occupied by transcription factors (TFs). The approach involves chemical cross-linking of DNA with associated proteins, fragmentation of chromatin by sonication or enzymatic digestion, immunoprecipitation of the fragments containing the protein of interest, and then PCR or hybridization analysis to characterize and quantify the genomic sequences enriched. We developed a computational model of quantitative ChIP analysis to elucidate the factors contributing to the method's resolution. The most important variables identified by the model were, in order of importance, the spacing of the PCR primers, the mean length of the chromatin fragments, and, unexpectedly, the type of fragment width distribution, with very small DNA fragments and smaller amplicons providing the best resolution of TF binding. One of the major predictions of the model was also validated experimentally.

No MeSH data available.


Related in: MedlinePlus

Results of computational simulation of quantitative ChIP analysis: exponential distributionTF binding, quantified using a scale from 0–1, is plotted as a function of position along a 1.5 kb linear DNA molecule. A single TF binding site (marked by green line) was assigned to a position 530 bp from its 5′ end. The amount of binding was determined for amplicons of 50, 100, 150, 200, and 250 bp and a mean DNA fragment size of 140–750 bp as indicated. An exponential distribution of fragment widths was used for these simulations.
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f1-cin-6-0137: Results of computational simulation of quantitative ChIP analysis: exponential distributionTF binding, quantified using a scale from 0–1, is plotted as a function of position along a 1.5 kb linear DNA molecule. A single TF binding site (marked by green line) was assigned to a position 530 bp from its 5′ end. The amount of binding was determined for amplicons of 50, 100, 150, 200, and 250 bp and a mean DNA fragment size of 140–750 bp as indicated. An exponential distribution of fragment widths was used for these simulations.

Mentions: As expected, the model showed that the location giving the greatest signal in the simulated PCR analysis coincided with the assigned TF binding site (Fig. 1). Although extent rather than probability of binding was analyzed, the width and inflection point of the resulting binding isotherm would determine the precision with which the TF binding site could be localized and represent two measures of assay resolution. As shown in Figure 1, the model shows that the shorter the distance between PCR primers for given sized DNA fragments, the more narrowly the binding distribution encompassed the TF binding site. With increasing amplicon size, the PCR signal declined, ultimately falling to zero when the size of the amplification product exceeded DNA fragment length. When the length of the amplicon was fixed and the DNA fragment size varied, the binding isotherm narrowed with decreasing mean DNA fragment size. Thus, the model shows that small DNA fragments and short amplicons provide the greatest precision in localizing TF binding, although the binding isotherms for many DNA fragment sizes and amplicon lengths possess distinct peaks (Fig. 1) and a number of combinations could potentially be informative.


A computational model of quantitative chromatin immunoprecipitation (ChIP) analysis.

Xie J, Crooke PS, McKinney BA, Soltman J, Brandt SJ - Cancer Inform (2008)

Results of computational simulation of quantitative ChIP analysis: exponential distributionTF binding, quantified using a scale from 0–1, is plotted as a function of position along a 1.5 kb linear DNA molecule. A single TF binding site (marked by green line) was assigned to a position 530 bp from its 5′ end. The amount of binding was determined for amplicons of 50, 100, 150, 200, and 250 bp and a mean DNA fragment size of 140–750 bp as indicated. An exponential distribution of fragment widths was used for these simulations.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC2367313&req=5

f1-cin-6-0137: Results of computational simulation of quantitative ChIP analysis: exponential distributionTF binding, quantified using a scale from 0–1, is plotted as a function of position along a 1.5 kb linear DNA molecule. A single TF binding site (marked by green line) was assigned to a position 530 bp from its 5′ end. The amount of binding was determined for amplicons of 50, 100, 150, 200, and 250 bp and a mean DNA fragment size of 140–750 bp as indicated. An exponential distribution of fragment widths was used for these simulations.
Mentions: As expected, the model showed that the location giving the greatest signal in the simulated PCR analysis coincided with the assigned TF binding site (Fig. 1). Although extent rather than probability of binding was analyzed, the width and inflection point of the resulting binding isotherm would determine the precision with which the TF binding site could be localized and represent two measures of assay resolution. As shown in Figure 1, the model shows that the shorter the distance between PCR primers for given sized DNA fragments, the more narrowly the binding distribution encompassed the TF binding site. With increasing amplicon size, the PCR signal declined, ultimately falling to zero when the size of the amplification product exceeded DNA fragment length. When the length of the amplicon was fixed and the DNA fragment size varied, the binding isotherm narrowed with decreasing mean DNA fragment size. Thus, the model shows that small DNA fragments and short amplicons provide the greatest precision in localizing TF binding, although the binding isotherms for many DNA fragment sizes and amplicon lengths possess distinct peaks (Fig. 1) and a number of combinations could potentially be informative.

Bottom Line: We developed a computational model of quantitative ChIP analysis to elucidate the factors contributing to the method's resolution.The most important variables identified by the model were, in order of importance, the spacing of the PCR primers, the mean length of the chromatin fragments, and, unexpectedly, the type of fragment width distribution, with very small DNA fragments and smaller amplicons providing the best resolution of TF binding.One of the major predictions of the model was also validated experimentally.

View Article: PubMed Central - PubMed

Affiliation: Department of Medicine, Vanderbilt University, Nashville, Tennessee 37232, USA.

ABSTRACT
Chromatin immunoprecipitation (ChIP) analysis is widely used to identify the locations in genomes occupied by transcription factors (TFs). The approach involves chemical cross-linking of DNA with associated proteins, fragmentation of chromatin by sonication or enzymatic digestion, immunoprecipitation of the fragments containing the protein of interest, and then PCR or hybridization analysis to characterize and quantify the genomic sequences enriched. We developed a computational model of quantitative ChIP analysis to elucidate the factors contributing to the method's resolution. The most important variables identified by the model were, in order of importance, the spacing of the PCR primers, the mean length of the chromatin fragments, and, unexpectedly, the type of fragment width distribution, with very small DNA fragments and smaller amplicons providing the best resolution of TF binding. One of the major predictions of the model was also validated experimentally.

No MeSH data available.


Related in: MedlinePlus