Quantitative modeling of transcription factor binding specificities using DNA shape
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE59845 | GEO | 2014/11/04
SECONDARY ACCESSION(S): PRJNA256346
REPOSITORIES: GEO
Similar Datasets
Project description:Accurate predictions of the DNA binding specificities of transcription factors (TFs) are necessary for understanding gene regulatory mechanisms. Traditionally, predictive models are built based on nucleotide sequence features. Here, we employed three- dimensional DNA shape information obtained on a high-throughput basis to integrate intuitive DNA structural features into the modeling of TF binding specificities using support vector regression. We performed quantitative predictions of DNA binding specificities, using the DREAM5 dataset for 65 mouse TFs and genomic-context protein binding microarray data for three human basic helix-loop-helix TFs. DNA shape-augmented models compared favorably with sequence-based models for these predictions. Although both k-mer and DNA shape features encoded the interdependencies between nucleotide positions of the binding site, using DNA shape features reduced the dimensionality of the feature space compared to k-mer use. Finally, analyzing the weights of DNA shape-augmented models uncovered TF family- specific structural readout mechanisms that were not obvious from the nucleotide sequence. Three genomic-context protein binding microarray (gcPBM) experiments of human transcription factors were performed. Briefly, the gcPBMs involved binding his-tagged transcription factors c-Myc, Max, and Mad1(Mxd1) to double-stranded 180K Agilent microarrays in order to determine their binding specificity for putative DNA binding sites in native genomic context. Briefly, we represent three categories of 36-bp sequences: 1) bound probes, 2) unbound probes (or negative controls), and 3) test probes. Bound probes corresponded to genomic regions bound in vivo by c-Myc, Max, or Mad2 (ChIP-seq P < 10^(-10) in HeLaS3 or K562 celld (ENCODE)) that contain at least two consecutive 8-mers with universal PBM E-score > 0.4 (Munteanu and Gordan, LNCS 2013). All putative binding sites occur at the same position within the probes on the array. M-bM-^@M-^\UnboundM-bM-^@M-^] probes corresponded to genomic regions with ChIP-seq P < 10^(-10) and a maximum 8-mer E-score < 0.2. We also designed test probes that contain, within constant flanking regions, all nnCACGTGnn 10-mers and 18 nnnCACGTGnnn 12-mers (where n = A, C, G, or T). Each DNA sequence represented on the array is present in 6 replicate spots. We report the gcPBM signal intensity for each spot. The PBM protocol is described in Berger et al., Nature Biotechnology 2006 (PMID 16998473).
2014-11-04 | E-GEOD-59845 | biostudies-arrayexpress
Project description:Development of the human malaria parasite, Plasmodium falciparum is regulated by a limited number of sequence-specific transcription factors (TFs). However, the mechanisms by which these TFs recognize genome-wide binding sites to regulate target genes is still largely unknown. To address TF target specificity, we investigated the binding of two TF subsets that either bind CACACA or GTGCAC and further characterized PfAP2-G and PfAP2-EXP which bind unique DNA motifs (GTAC and TGCATGCA). We interrogated the impact of DNA sequence context and the chromatin landscape on P. falciparum TF binding using high-throughput in vitro and in vivo binding assays, DNA shape predictions, epigenetic post-translational modifications, and chromatin accessibility. We determined that DNA sequence context does not greatly impact binding site selection for CACACA-binding TFs, while chromatin accessibility, epigenetic patterns, co-factor recruitment, and dimerization contribute to differential binding. In contrast, GTGCAC-binding TFs prefer different sequence contexts, DNA shape profiles, and chromatin dynamics. Finally, we find that TFs that preferentially bind divergent DNA motifs may bind overlapping genomic regions in vivo due to low-affinity binding to other sequence motifs. Our results demonstrate that TF binding site selection relies on a combination of DNA sequence and chromatin features, thereby contributing to the complexity of P. falciparum gene regulatory mechanisms.
2023-12-31 | GSE227873 | GEO
Project description:DNA sequence is a major determinant of the binding specificity of transcription factors (TFs) for their genomic targets. However, eukaryotic cells often express, at the same time, TFs with highly similar DNA binding motifs but distinct in vivo targets. Currently, it is not well understood how TFs with seemingly identical DNA motifs achieve unique specificities in vivo. Here, we used custom protein binding microarrays to analyze TF specificity for putative binding sites in their genomic sequence context. Using yeast TFs Cbf1 and Tye7 as our case study, we found that binding sites of these bHLH TFs (i.e., E-boxes) are bound differently in vitro and in vivo, depending on their genomic context. Computational analyses suggest that nucleotides outside E-box binding sites contribute to specificity by influencing the 3D structure of DNA binding sites. Thus, local shape of target sites might play a widespread role in achieving regulatory specificity within TF families. Two protein binding microarray (PBM) experiments of Saccharomyces cerevisiae transcription factors were performed. Briefly, the PBMs involved binding GST-tagged yeast transcription factors Cbf1 and Tye7 to double-stranded 44K Agilent microarrays in order to determine the accuracy of our regression models for TF-DNA binding specificity. Briefly, this array contains 30-bp genomic sequences from our initial custom array (Gordan et al 2013, submitted), with 0 through 4 mutations designed at various positions in the genomic sequences. Each sequence in represented in 6 replicate spots. We report the PBM signal intensity for each spot. The PBM protocol is described in Berger et al., Nature Biotechnology 2006 (PMID 16998473).
2013-03-29 | E-GEOD-44437 | biostudies-arrayexpress
Project description:In mammalian cells transcription factors (TFs) preferentially bind sites contained in regions of high nucleosomal occupancy, as determined by nucleotide-dependent computational analysis. This observation suggests that nucleosomes may act as gatekeepers of TF binding sites. We hypothesized that in mammalian genomes the information controlling nucleosome assembly may partially coincide with the information that enables TFs to recognize cognate sites in cis-regulatory elements while ignoring the myriad of non-functional, randomly occurring consensus binding sites. This way, nucleosome-mediated masking would be coupled to TF binding site functionality. The hematopoietic master regulator Pu.1 maintained nucleosome depletion at macrophage-specific enhancers that were otherwise occupied by nucleosomes in other cell types and in reconstituted chromatin. We identified a minimal set of DNA sequence and shape features that predicted Pu.1 binding with 78% accuracy. The same features predicted nucleosome occupancy in cells where Pu.1 was not expressed with higher accuracy than specifically designed models. Control of nucleosome deposition by DNA sequence and shape features that also specify TF consensus site functionality may allow maintaining the gatekeeper function of nucleosomes during evolution of cis-regulatory elements.
2014-05-08 | GSE50762 | GEO
Project description:This dataset uses DNase-seq to profile the genome-wide DNase I hypersensitivity of mES and mES-derived cells along an early pancreatic lineage and provides the locations of putative Transcription Factor (TF) binding sites using the PIQ algorithm. DNase-seq takes advantage of the preferential cutting of DNase I in open chromatin and steric blockage of of DNase I by tightly bound TFs that protect associated genomic DNA sequences. After deep sequencing of DNase IM-bM-^@M-^Sdigested genomic DNA from intact nuclei, genome-wide data on chromatin accessibility as well as TF-specific DNase I protection profiles that reveal the genomic binding locations of a majority of TFs are obtained. Such TF signature M-bM-^@M-^XDNase profilesM-bM-^@M-^Y reflect the effect of the TF on DNA shape and local chromatin architecture, extending hundreds of base pairs from a TF binding site, and these profiles are centered on M-bM-^@M-^XDNase footprintsM-bM-^@M-^Y at the binding motif itself, which reflects the biophysics of protein-DNA binding. An algorithm, PIQ, is then applied that models the specific profile of each factor, and in combination with sequence information predicts the likely binding locations of over 700 TFs genome wide. This dataset includes DNase-seq hypersensitivity data from 6 mES-derived cell types: mESC, Mesendoderm, Mesoderm, Endoderm, Intestinal Endoderm, and Prepancreatic Endoderm. For each cell type, TF binding site predictions are made based on the identification TF-specific DNase-seq profiles over any of 1331 possible binding motifs. After significance thesholding, genome-wide binding site predictions for <700 TFs are included.
2014-01-19 | E-GEOD-53776 | biostudies-arrayexpress
Project description:Binding of transcription factors to DNA is mediated by the recognition of the chemical signatures of the DNA bases and the three-dimensional shape of the DNA molecule. The direct contribution of DNA shape to DNA-binding specificity has been difficult to assess, as DNA shape is a consequence of its sequence. Here, we teased apart these two modes of recognition in the context of Hox-DNA binding. We made a series of mutations in Hox residues that, in a co-crystal structure, only recognize DNA shape, and tested the effect on DNA binding preferences using SELEX-seq. Analysis of shape features of selected sequences revealed that these residues are both necessary and sufficient for selection of sequences with distinct shape features. We used statistical machine learning to show that the accuracy of binding specificity predictions improves by adding shape features to a model that only depends on sequence. We conclude that shape readout is a direct and critical component of binding site selection by Hox proteins.
2015-01-21 | GSE65073 | GEO
Project description:Binding of transcription factors to DNA is mediated by the recognition of the chemical signatures of the DNA bases and the three-dimensional shape of the DNA molecule. The direct contribution of DNA shape to DNA-binding specificity has been difficult to assess, as DNA shape is a consequence of its sequence. Here, we teased apart these two modes of recognition in the context of Hox-DNA binding. We made a series of mutations in Hox residues that, in a co-crystal structure, only recognize DNA shape, and tested the effect on DNA binding preferences using SELEX-seq. Analysis of shape features of selected sequences revealed that these residues are both necessary and sufficient for selection of sequences with distinct shape features. We used statistical machine learning to show that the accuracy of binding specificity predictions improves by adding shape features to a model that only depends on sequence. We conclude that shape readout is a direct and critical component of binding site selection by Hox proteins. Three rounds of SELEX were performed on a series of Hox mutants as described in Slattery et al, Cell, 2011 (PMID 22153072) and Riley et al, Methods in molecular Biology, 2014 (PMID 25151169). Briefly, His-tagged Scr and Antp mutant proteins were incubated with a randomized 16mer oligonucleotide library, and bound DNA was amplified and sequenced as described (PMID 22153072, PMID 25151169).
2015-01-21 | E-GEOD-65073 | biostudies-arrayexpress
Project description:DNA sequence is a major determinant of the binding specificity of transcription factors (TFs) for their genomic targets. However, eukaryotic cells often express, at the same time, TFs with highly similar DNA binding motifs but distinct in vivo targets. Currently, it is not well understood how TFs with seemingly identical DNA motifs achieve unique specificities in vivo. Here, we used custom protein binding microarrays to analyze TF specificity for putative binding sites in their genomic sequence context. Using yeast TFs Cbf1 and Tye7 as our case study, we found that binding sites of these bHLH TFs (i.e., E-boxes) are bound differently in vitro and in vivo, depending on their genomic context. Computational analyses suggest that nucleotides outside E-box binding sites contribute to specificity by influencing the 3D structure of DNA binding sites. Thus, local shape of target sites might play a widespread role in achieving regulatory specificity within TF families.
2013-03-29 | GSE44437 | GEO
Project description:DNA sequence is a major determinant of the binding specificity of transcription factors (TFs) for their genomic targets. However, eukaryotic cells often express, at the same time, TFs with highly similar DNA binding motifs but distinct in vivo targets. Currently, it is not well understood how TFs with seemingly identical DNA motifs achieve unique specificities in vivo. Here, we used custom protein binding microarrays to analyze TF specificity for putative binding sites in their genomic sequence context. Using yeast TFs Cbf1 and Tye7 as our case study, we found that binding sites of these bHLH TFs (i.e., E-boxes) are bound differently in vitro and in vivo, depending on their genomic context. Computational analyses suggest that nucleotides outside E-box binding sites contribute to specificity by influencing the 3D structure of DNA binding sites. Thus, local shape of target sites might play a widespread role in achieving regulatory specificity within TF families.
2013-03-29 | GSE44436 | GEO
Project description:Motivation: The DNA binding specificity of a transcription factor (TF) is typically represented using a position weight matrix (PWM) model, which implicitly assumes that individual bases in a TF binding site contribute independently to the binding affinity, an assumption that does not always hold. For this reason, more complex models of binding specificity have been developed. However, these models have their own caveats: they typically have a large number of parameters, which makes them hard to learn and interpret. Results: We propose novel regression-based models of TF-DNA binding specificity, trained using high resolution in vitro data from custom protein binding microarray (PBM) experiments. Our PBMs are specifically designed to cover a large number of putative DNA binding sites for the TFs of interest (yeast TFs Cbf1 and Tye7, and human TFs c-Myc, Max, and Mad2) in their native genomic context. These high-throughput, quantitative data are well suited for training complex models that take into account not only independent contributions from individual bases, but also contributions from di- and trinucleotides at various positions within or near the binding sites. To ensure that our models remain interpretable, we use feature selection to identify a small number of sequence features that accurately predict TF-DNA binding specificity. To further illustrate the accuracy of our regression models, we show that even in the case of paralogous TF with highly similar PWMs, our new models can distinguish the specificities of individual factors. Thus, our work represents an important step towards better sequence-based models of individual TF-DNA binding specificity. Four protein binding microarray (PBM) experiments of human transcription factors were performed. Briefly, the PBMs involved binding GST-tagged transcription factors c-Myc, Max, and Mad2(Mxi1) to double-stranded 180K Agilent microarrays in order to determine their binding specificity for putative DNA binding sites in native genomic context. Briefly, we represent three categories of 36-bp sequences: 1) bound probes, 2) unbound probes (or negative controls), and 3) test probes. Bound probes corresponded to genomic regions bound in vivo by c-Myc, Max, or Mad2 (ChIP-seq P < 10^(-10) in HeLaS3 or K562 celld (ENCODE)) that contain at least two consecutive 8-mers with universal PBM E-score > 0.4 (Munteanu and Gordan, LNCS 2013). All putative binding sites occurr at the same position within the probes on the array. M-bM-^@M-^\UnboundM-bM-^@M-^] probes corresponded to genomic regions with ChIP-seq P < 10^(-10) and a maximum 8-mer E-score < 0.2. We also designed test probes that contain, within constant flanking regions, all nnCACGTGnn 10-mers and 18 nnnCACGTGnnn 12-mers (where n = A, C, G, or T). Each DNA sequence represented on the array is present in 6 replicate spots. We report the PBM signal intensity for each spot. The PBM protocol is described in Berger et al., Nature Biotechnology 2006 (PMID 16998473).
2013-05-17 | E-GEOD-47026 | biostudies-arrayexpress