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High-throughput chromatin information enables accurate tissue-specific prediction of transcription factor binding sites.

Whitington T, Perkins AC, Bailey TL - Nucleic Acids Res. (2008)

Bottom Line: This improvement is superior to the improvement gained by equivalent use of either transcription start site proximity or phylogenetic conservation information.Importantly, predictions made with the use of chromatin structure information are tissue specific.This result supports the biological hypothesis that chromatin modulates TF binding to produce tissue-specific binding profiles in higher eukaryotes, and suggests that the use of chromatin modification information can lead to accurate tissue-specific transcriptional regulatory network elucidation.

View Article: PubMed Central - PubMed

Affiliation: Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia.

ABSTRACT
In silico prediction of transcription factor binding sites (TFBSs) is central to the task of gene regulatory network elucidation. Genomic DNA sequence information provides a basis for these predictions, due to the sequence specificity of TF-binding events. However, DNA sequence alone is an impoverished source of information for the task of TFBS prediction in eukaryotes, as additional factors, such as chromatin structure regulate binding events. We show that incorporating high-throughput chromatin modification estimates can greatly improve the accuracy of in silico prediction of in vivo binding for a wide range of TFs in human and mouse. This improvement is superior to the improvement gained by equivalent use of either transcription start site proximity or phylogenetic conservation information. Importantly, predictions made with the use of chromatin structure information are tissue specific. This result supports the biological hypothesis that chromatin modulates TF binding to produce tissue-specific binding profiles in higher eukaryotes, and suggests that the use of chromatin modification information can lead to accurate tissue-specific transcriptional regulatory network elucidation.

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Filter performance in mouse ES cells at sensitivity 20%. The best relative FP rate (as defined in the Methods section) of each filter type has been plotted for the 18 mouse gold-standard TFBS datasets. Multiple gold-standard datasets were available for Klf4, Oct4 and Nanog, and the first author of the corresponding gold-standard dataset has been indicated. PhastCons filtering failed to yield a positive relative FP rate improvement for any of the 18 gold-standard datasets at this sensitivity level, and so has been omitted. Error bars indicate standard error. Barplot mean and standard errors smaller than −1 have been truncated to −1, to allow clearer visualization of relative FP improvement values between 0 and 1.
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Figure 5: Filter performance in mouse ES cells at sensitivity 20%. The best relative FP rate (as defined in the Methods section) of each filter type has been plotted for the 18 mouse gold-standard TFBS datasets. Multiple gold-standard datasets were available for Klf4, Oct4 and Nanog, and the first author of the corresponding gold-standard dataset has been indicated. PhastCons filtering failed to yield a positive relative FP rate improvement for any of the 18 gold-standard datasets at this sensitivity level, and so has been omitted. Error bars indicate standard error. Barplot mean and standard errors smaller than −1 have been truncated to −1, to allow clearer visualization of relative FP improvement values between 0 and 1.

Mentions: In Figures 5 and 6, we illustrate the tissue specificity and superiority of ES H3K4me3 filtering in mouse ES cells over all TFs considered. The TFBS gold-standard datasets for these TFs are derived from mouse ES cells.Figure 5.


High-throughput chromatin information enables accurate tissue-specific prediction of transcription factor binding sites.

Whitington T, Perkins AC, Bailey TL - Nucleic Acids Res. (2008)

Filter performance in mouse ES cells at sensitivity 20%. The best relative FP rate (as defined in the Methods section) of each filter type has been plotted for the 18 mouse gold-standard TFBS datasets. Multiple gold-standard datasets were available for Klf4, Oct4 and Nanog, and the first author of the corresponding gold-standard dataset has been indicated. PhastCons filtering failed to yield a positive relative FP rate improvement for any of the 18 gold-standard datasets at this sensitivity level, and so has been omitted. Error bars indicate standard error. Barplot mean and standard errors smaller than −1 have been truncated to −1, to allow clearer visualization of relative FP improvement values between 0 and 1.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2662491&req=5

Figure 5: Filter performance in mouse ES cells at sensitivity 20%. The best relative FP rate (as defined in the Methods section) of each filter type has been plotted for the 18 mouse gold-standard TFBS datasets. Multiple gold-standard datasets were available for Klf4, Oct4 and Nanog, and the first author of the corresponding gold-standard dataset has been indicated. PhastCons filtering failed to yield a positive relative FP rate improvement for any of the 18 gold-standard datasets at this sensitivity level, and so has been omitted. Error bars indicate standard error. Barplot mean and standard errors smaller than −1 have been truncated to −1, to allow clearer visualization of relative FP improvement values between 0 and 1.
Mentions: In Figures 5 and 6, we illustrate the tissue specificity and superiority of ES H3K4me3 filtering in mouse ES cells over all TFs considered. The TFBS gold-standard datasets for these TFs are derived from mouse ES cells.Figure 5.

Bottom Line: This improvement is superior to the improvement gained by equivalent use of either transcription start site proximity or phylogenetic conservation information.Importantly, predictions made with the use of chromatin structure information are tissue specific.This result supports the biological hypothesis that chromatin modulates TF binding to produce tissue-specific binding profiles in higher eukaryotes, and suggests that the use of chromatin modification information can lead to accurate tissue-specific transcriptional regulatory network elucidation.

View Article: PubMed Central - PubMed

Affiliation: Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia.

ABSTRACT
In silico prediction of transcription factor binding sites (TFBSs) is central to the task of gene regulatory network elucidation. Genomic DNA sequence information provides a basis for these predictions, due to the sequence specificity of TF-binding events. However, DNA sequence alone is an impoverished source of information for the task of TFBS prediction in eukaryotes, as additional factors, such as chromatin structure regulate binding events. We show that incorporating high-throughput chromatin modification estimates can greatly improve the accuracy of in silico prediction of in vivo binding for a wide range of TFs in human and mouse. This improvement is superior to the improvement gained by equivalent use of either transcription start site proximity or phylogenetic conservation information. Importantly, predictions made with the use of chromatin structure information are tissue specific. This result supports the biological hypothesis that chromatin modulates TF binding to produce tissue-specific binding profiles in higher eukaryotes, and suggests that the use of chromatin modification information can lead to accurate tissue-specific transcriptional regulatory network elucidation.

Show MeSH