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Cited by 116Genomics and Chromatin DynamicsGenomics and Phylogenetic StudiesChromatin Remodeling and Cancer

The DOI matches its Crossref record; 56 of the 58 references it cites were found in a registry or an index.

Genome-wide prediction of transcription factor binding sites using an integrated model

Open access
Won, Kyoung-Jae, Ren, Bing, Wang, Wei·2010·Genome Biology
DOI 10.1186/gb-2010-11-1-r7PMC PMC2847719
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Abstract

We present an integrated method called Chromia for the genome-wide identification of functional target loci of transcription factors. Designed to capture the characteristic patterns of transcription factor binding motif occurrences and the histone profiles associated with regulatory elements such as promoters and enhancers, Chromia significantly outperforms other methods in the identification of 13 transcription factor binding sites in mouse embryonic stem cells, evaluated by both binding (ChIP-seq) and functional (RNA interference knockdown) experiments.

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Crossref · 10.1186/gb-2010-11-1-r7

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Figures

Figure 1 Histone modification signals aligned at TSSs (promoters) and distal p300 binding sites (enhancers). Scaled signals of 100 promoter and enhancer regions with strong histone modification (sequencing read counts higher than an arbitrary cutoff) and the highest PSSM scores of Oct4 are plotted (see Materials and methods for how these regions were selected and Figure S1 in Additional file 1 for plots of other TFs). Individual histone marks are shown in a heatmap (upper panels) and the averaged signal is shown in the lower panels. TSSs are ordered by gene expression and the p300 binding sites are ordered using ChIP tag counts. Both are in descending order from the top to the bottom.
Figure 1 Histone modification signals aligned at TSSs (promoters) and distal p300 binding sites (enhancers). Scaled signals of 100 promoter and enhancer regions with strong histone modification (sequencing read counts higher than an arbitrary cutoff) and the highest PSSM scores of Oct4 are plotted (see Materials and methods for how these regions were selected and Figure S1 in Additional file 1 for plots of other TFs). Individual histone marks are shown in a heatmap (upper panels) and the averaged signal is shown in the lower panels. TSSs are ordered by gene expression and the p300 binding sites are ordered using ChIP tag counts. Both are in descending order from the top to the bottom.CC BY · PubMed Central
Figure 2 Histone modification signals centered at the top 500 ChIP-seq binding peaks of Oct4 (heatmap in upper panels and the average shown as a black line in the lower panels). The red line in the lower panels is the averaged histone modification signals centered at the bottom 500 Oct4 binding peaks. The histone modification signals centered at other TF binding peaks are shown in Figure S2 in Additional file 1.
Figure 2 Histone modification signals centered at the top 500 ChIP-seq binding peaks of Oct4 (heatmap in upper panels and the average shown as a black line in the lower panels). The red line in the lower panels is the averaged histone modification signals centered at the bottom 500 Oct4 binding peaks. The histone modification signals centered at other TF binding peaks are shown in Figure S2 in Additional file 1.CC BY · PubMed Central
Figure 3 Difference in histone mark signals associated with the binding strength of the 13 TFs. We calculated the Euclidean distance between the averages of the histone mark signals (read count) centered at the top 500 and the bottom 500 TF binding peaks shown in the lower panels of Figure 2.
Figure 3 Difference in histone mark signals associated with the binding strength of the 13 TFs. We calculated the Euclidean distance between the averages of the histone mark signals (read count) centered at the top 500 and the bottom 500 TF binding peaks shown in the lower panels of Figure 2.CC BY · PubMed Central
Figure 4 The framework of Chromia. (1) Data preparation. Chromia takes binned signals of PSSM scores and eight histone marks in the entire genome as input. (2) Training data. Regions centered at TSS and p300 binding sites were selected to train HMMs for promoters and enhancers, respectively. The entire chromosome 1 was used to train the background model. (3) Model training. Three HMMs with a left-right structure and a mixture of Gaussians were trained for promoters, enhancers and background, respectively. (4) Whole genome scanning. Two log-odd scores were calculated for each bin in the entire genome using the trained HMMs. (5) TFBS predictions. Log-odd scores of adjacent bins were averaged to smooth the curve. Bins with a log-odd score greater than other binding sites within ± 2,000 bp were predicted to contain the TFBSs. See Materials and methods for details.
Figure 4 The framework of Chromia. (1) Data preparation. Chromia takes binned signals of PSSM scores and eight histone marks in the entire genome as input. (2) Training data. Regions centered at TSS and p300 binding sites were selected to train HMMs for promoters and enhancers, respectively. The entire chromosome 1 was used to train the background model. (3) Model training. Three HMMs with a left-right structure and a mixture of Gaussians were trained for promoters, enhancers and background, respectively. (4) Whole genome scanning. Two log-odd scores were calculated for each bin in the entire genome using the trained HMMs. (5) TFBS predictions. Log-odd scores of adjacent bins were averaged to smooth the curve. Bins with a log-odd score greater than other binding sites within ± 2,000 bp were predicted to contain the TFBSs. See Materials and methods for details.CC BY · PubMed Central
Figure 5 ROC curves for TFBS identification methods in the leave-one-chromosome-out cross-validations. Sensitivity = TP/(TP + FN) and Specificity = TN/(TN + FP).
Figure 5 ROC curves for TFBS identification methods in the leave-one-chromosome-out cross-validations. Sensitivity = TP/(TP + FN) and Specificity = TN/(TN + FP).CC BY · PubMed Central
Figure 6 ROC curves for TFBS identification using Chromia and the baseline method in the leave-one-chromosome-out cross-validation. The baseline method used one histone mark read count (H3K3me3 for promoters and H3K4me1 for enhancers) multiplied by the PSSM score to rank all the sites.
Figure 6 ROC curves for TFBS identification using Chromia and the baseline method in the leave-one-chromosome-out cross-validation. The baseline method used one histone mark read count (H3K3me3 for promoters and H3K4me1 for enhancers) multiplied by the PSSM score to rank all the sites.CC BY · PubMed Central
Figure 7 ROC curves of TFBS identification using various methods for 20 genomic regions. Sensitivity = TP/(TP + FN) and Specificity = TN/(TN + FP). Dotted lines are using all the ChIP-seq binding peaks and solid lines are using the confident ChIP-seq binding peaks that have strong motif scores in Table S1 in Additional file 2.
Figure 7 ROC curves of TFBS identification using various methods for 20 genomic regions. Sensitivity = TP/(TP + FN) and Specificity = TN/(TN + FP). Dotted lines are using all the ChIP-seq binding peaks and solid lines are using the confident ChIP-seq binding peaks that have strong motif scores in Table S1 in Additional file 2.CC BY · PubMed Central
Figure 8 Mixture of Gaussians in the trained HMM. The x-axis is the signal intensity (sequencing read count) and the y-axis is the weighted sum of the three Gaussians in each state of the HMM. The first column shows the distribution of the mixture of Gaussians of the three states trained for H3K4me1 centered at the p300 binding sites (enhancers) with the strongest 100 Oct4 motif scores. Second column is for H3K3me3 in E2f1.
Figure 8 Mixture of Gaussians in the trained HMM. The x-axis is the signal intensity (sequencing read count) and the y-axis is the weighted sum of the three Gaussians in each state of the HMM. The first column shows the distribution of the mixture of Gaussians of the three states trained for H3K4me1 centered at the p300 binding sites (enhancers) with the strongest 100 Oct4 motif scores. Second column is for H3K3me3 in E2f1.CC BY · PubMed Central
Figure 9 HMM log-odds scores along with histone signatures and PSSM score around the TSS of gene Yipf6.
Figure 9 HMM log-odds scores along with histone signatures and PSSM score around the TSS of gene Yipf6.CC BY · PubMed Central

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Metadata

Authors
Won, K.-J., Ren, B., & Wang, W.
Affiliations
  • University of California San Diego (US)
  • Ludwig Cancer Research (BE)
Corresponding author
Wang, Wei
ORCID
Venue
Genome Biology
Publisher
Springer Science and Business Media LLC
Published
2010-01-22
Volume
11
Issue
1
Pages
R7-R7
ISSN
  • 1465-6906 (ISSN-L)
  • 1474-760X
DOI
10.1186/gb-2010-11-1-r7
PMC
PMC2847719
OpenAlex
W2078034709
Cited by
116
References
58
Citations by year
2026: 2 · 2024: 1 · 2023: 4 · 2022: 2 · 2021: 6 · 2020: 4 · 2019: 4 · 2018: 4 · 2017: 3 · 2016: 5 · 2015: 13 · 2014: 10 · 2013: 19 · 2012: 12
FWCI
7.56
Type
journal article
Language
en
Open access
Licence
Locations
  • Genome biology (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • eScholarship (California Digital Library) (submittedVersion)
Funders
  • National Institutes of Health
  • National Institute of General Medical Sciences (R01GM072856)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Chromatin Remodeling and Cancer
Keywords
transcription factor binding sites, chromia, regulatory elements, promoter, enhancers, mouse embryonic stem cells, ChIP-seq, RNAi knockdown, genomic prediction
MeSH terms
Animals, Binding Sites, Chromatin, Enhancer Elements, Genetic, Models, Genetic, Promoter Regions, Genetic, ROC Curve, Transcription Factors, Genome, Computational Biology, Amino Acid Motifs, RNA Interference, Chromatin Immunoprecipitation, Mice, Embryonic Stem Cells

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