Quantitative modeling of transcription factor binding specificities using DNA shape
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE60200 | GEO | 2015/03/01
SECONDARY ACCESSION(S): PRJNA257744
REPOSITORIES: GEO
Similar Datasets
Project description:The SELEX-seq platform was used to generate DNA-binding affinity predictions for the human Max transcription factor. This experiment was performed as part of a cross-validation study comparing the accuracy of DNA shape-augmented TF binding specificity models across two different platforms (SELEX-seq and gcPBM) Two rounds of SELEX were performed on Max protein as described in Slattery et al, Cell, 2011 (PMID 22153072). Briefly, His-tagged Max was incubated with a randomized 16mer oligonucleotide library (GTTCAGAGTTCTACAGTCCGACGATCTGG[ACGT]{16}CCAGAACTCGTATGCCGTCTTCTGCTTG). Max bound DNA was amplified and sequenced as described (Slattery et al, 2011).
2015-03-01 | E-GEOD-60200 | biostudies-arrayexpress
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: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.
2014-11-04 | GSE59845 | 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: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:DNA binding specificities of transcription factors (TFs) are a key component of gene regulatory processes. Underlying mechanisms that explain the highly specific binding of TFs to their genomic target sites are poorly understood. A better understanding of TF-DNA binding requires the ability to quantitatively model TF binding to accessible DNA as its basic step, before additional in vivo components can be considered. Traditionally, these models were built based on nucleotide sequence. Here, we integrated 3D DNA shape information derived with a high-throughput approach into the modeling of TF binding specificities. Using support vector regression, we trained quantitative models of TF binding specificity based on protein binding microarray (PBM) data for 68 mammalian TFs. The evaluation of our models included cross-validation on specific PBM array designs, testing across different PBM array designs, and using PBM-trained models to predict relative binding affinities derived from in vitro selection combined with deep sequencing (SELEX-seq). Our results showed that shape-augmented models compared favorably to sequence-based models. Although both k-mer and DNA shape features can encode interdependencies between nucleotide positions of the binding site, using DNA shape features reduced the dimensionality of the feature space. In addition, analyzing the feature weights of DNA shape-augmented models uncovered TF family-specific structural readout mechanisms that were not revealed by the DNA sequence. As such, this work combines knowledge from structural biology and genomics, and suggests a new path toward understanding TF binding and genome function.
| S-EPMC4403198 | biostudies-literature
Project description:Quantifying the preferences of DNA binding proteins is an essential step in determining how transcription factors (TFs) interact with their targets in the genome. High-throughput in vitro binding assays have been used to identify the inherent DNA preferences of TFs in a controlled environment isolated from confounding factors such as genome accessibility, DNA methylation, and TF binding cooperativity. Unfortunately, the limited variable region or sequencing depth typically utilized by many of the available experimental approaches makes it difficult to measure the preferences of moderate- to low-affinity binding sites, and impossible to measure small-scale differences between closely related homologs. The Forkhead box (FOX) family of TFs is known to play a crucial role in regulating a variety of key processes from proliferation and development, to tumor suppression and aging. By using the high-sequencing depth SELEX-seq approach to study all four FOX homologs in Saccharomyces cerevisiae, we have been able to precisely quantify the contribution and importance of nucleotide positions flanking the core of the binding sites. Essential to this process was the alignment of our SELEX-seq reads to a set of candidate core sequences determined by two newly developed strategies of alignment and reprioritization applied to enriched k-mers.
2023-04-26 | GSE178811 | GEO
Project description:We performed a high-throughput systematic evolution of ligands by exponential enrichment (HT-SELEX) approach on all 371 annotated TFs in P. aeruginosa. This study provides a valuable resource for TF binding specificities in P. aeruginosa and demonstrates a novel and an integrative analysis for seeking the virulence-associated TFs and its target genes.
2021-03-26 | GSE151518 | GEO
Project description:The regulation of transcription is primarily exerted through transcription factors (TFs) binding to genomic DNA. Although molecular mechanisms of TFs have been studied individually for decades, a complete picture of binding profiles of all TFs and their precise targets in the genome are still lacking in the model pathogen Pseudomonas syringae. To this end, we performed a high-throughput systematic evolution of ligands by exponential enrichment (HT-SELEX) approach on all 301 annotated TFs in P. syringae. Robust enrichment of specific sequences was deduced to 118 SELEX motifs. We identified 12,464 interactions between 100 TFs and their target genes in the genome, for an individual TF ranging from 6 to 1481 sites. It showed that 90% TF binding was of dimeric site type, in which 85% with the head-to-head palindromic binding preference. To further explore the pathogenic mechanism of TFs in P. syringae, we mapped intricate networks of these TFs and their targets in the virulence-associated pathways, many of which were verified by orthologous methods such as ChIP-seq, EMSA, RT-qPCR and a reporter assay of promoter activity. By checking the enrichment of binding sites in pathways, we identified 25 virulence-associated master regulators of which 14 had never been characterized as TFs before. Overall, the present study provides a valuable resource for TF binding specificities in P. syringae and demonstrates a novel and an integrative analysis for seeking the virulence-associated TFs and its target genes. We expect that the results will significantly benefit future studies on the transcriptional regulation in P. syringae, and facilitate the design of drug targets to protect plants from attacks by relevant pathogens.
2020-08-18 | GSE146697 | GEO
Project description:A long-standing challenge in human regulatory genomics is that transcription factor (TF) DNA-binding motifs are short and degenerate, while the genome is large. Motif scans therefore produce many false-positive binding site predictions. By surveying TFs across 25 families using >1,500 cyclic in vitro selection experiments with fragmented, naked, and unmodified genomic DNA – a method we term GHT-SELEX (Genomic HT-SELEX) – we find that many human TFs possess much higher sequence specificity than anticipated. Moreover, genomic binding regions from GHT-SELEX are often surprisingly similar to those obtained in vivo (i.e. ChIP-seq peaks). We find that comparable specificity can also be obtained from motif scans, but performance is highly dependent on derivation and use of the motifs, including accounting for multiple local matches in the scans. We also observe alternative engagement of multiple DNA-binding domains within the same protein: long C2H2 zinc finger proteins often utilize modular DNA recognition, engaging different subsets of their DNA binding domain (DBD) arrays to recognize multiple types of distinct target sites, frequently evolving via internal duplication and divergence of one or more DBDs. Thus, contrary to conventional wisdom, it is common for TFs to possess sufficient intrinsic specificity to independently delineate cellular targets.
2024-11-11 | GSE278858 | GEO