Similar Datasets
Project description:We utilized a bacterial selection system to isolate Engrailed variants from a randomized library that are compatible with each of the 64 possible 3’ triplet sites (i.e. TAANNN). The DNA-binding specificity of 151 representative HD variants from these populations was subsequently characterized. Many of these variants contain novel combinations of specificity determinants that are uncommon or absent in extant HDs. These novel determinants, when grafted into different HD backbones, produce a corresponding alteration in their specificity. The identified determinates expand our understanding of HD recognition. Homeodomains where selected against each of the 64 possible 3' triplet sites (ie: TAANNN). The DNA-binding specificity of 151 representative HD variants from these populations was subsequently characterized. 7 novel determinants, when grafted into 3 different HD backbones. 5' speicifity was changed for 3 novel determinants.
2012-02-15 | E-GEOD-35806 | biostudies-arrayexpress
Project description:Desulfuromonas sp. TF strain:TF Genome sequencing and assembly
| PRJNA175252 | ENA
Project description:Homeodomains (HDs) are the second largest class of DNA binding domains (DBDs) in eukaryotic sequence-specific transcription factors (TFs) and are the TF structural class with the largest number of disease mutations in the Human Gene Mutation Database (HGMD). Despite numerous structural studies and large-scale analyses of HD DNA binding specificity, HD-DNA recognition is still not fully understood. Here, we analyzed 92 human HD mutants, including disease-associated variants and variants of unknown significance (VUS), for their effects on DNA binding activity. Many of the variants altered DNA binding affinity and/or specificity. Structural analysis identified 14 novel specificity-determining positions, 5 of which do not contact DNA. The same missense substitution at analogous positions within different HDs exhibited different effects on DNA binding activity. Variant effect prediction tools perform moderately well in distinguishing variants with altered DNA binding affinity, but poorly in identifying those with altered binding specificity. Our results highlight the need for biochemical assays of TF coding variants and promote dozens of variants for further investigations into their pathogenicity and the development of clinical diagnostics and precision therapies.
2023-12-05 | GSE233827 | GEO
Project description:Direct GR binding sites potentiate clusters of TF binding across the human genome
| PRJNA315836 | ENA
Project description:We utilized a bacterial selection system to isolate Engrailed variants from a randomized library that are compatible with each of the 64 possible 3’ triplet sites (i.e. TAANNN). The DNA-binding specificity of 151 representative HD variants from these populations was subsequently characterized. Many of these variants contain novel combinations of specificity determinants that are uncommon or absent in extant HDs. These novel determinants, when grafted into different HD backbones, produce a corresponding alteration in their specificity. The identified determinates expand our understanding of HD recognition.
2012-02-15 | GSE35806 | GEO
Project description:Mapping ONECUT1 TF binding with ChIP-exo
| PRJEB4933 | ENA
Project description: Not available
| S-EPMC5046229 | biostudies-literature
Project description:Monitoring the location of transcription factors (TFs) binding to DNA is key to understanding transcriptional regulation. The main tool for mapping TF binding is ChIP-seq and its variants. However, current ChIP-based methods are hampered by at least one of the following limitations: large input requirements, low spatial resolution, and limited compatibility with high-throughput automation. Here, we describe SLIM-ChIP (Short fragment enriched, Low input, Indexed, MNase ChIP), which overcomes these challenges by combining enzymatic fragmentation of chromatin and on-bead indexing of immobilized TF-DNA complexes. We show that SLIM-ChIP reproduces high resolution binding map of yeast Reb1 similarly to the high-resolution TF mapping methods ChIP-exo and ORGANIC. Yet, SLIM-ChIP requires substantially less input material, and is fully compatible with high-throughput procedures. We further demonstrate the robustness and flexibility of SLIM-ChIP by probing Abf1 and Rap1 in yeast and CTCF in mouse embryonic stem cells. Finally, we show that the unique combination of high resolution and preservation of DNA protection patterns by SLIM-ChIP provide an additional layer of information on the chromatin landscape surrounding the bound TF. We used this information to identify a class of Reb1 sites in which the proximal -1 nucleosome tightly interacts with Reb1 and unlike in most Reb1 sites is refractory to remodeling by the RSC complex. Importantly, the interaction of Reb1 with the -1 nucleosome prevents transcription initiation and can serve as a more general mechanism for maintaining unidirectional transcription. Altogether, SLIM-ChIP is an attractive solution for mapping DNA binding proteins in a more informative context regarding their surrounding chromatin occupancy landscape at a single cell level.
2018-03-06 | GSE108948 | GEO
Project description:A high-confidence map of the direct, functional targets of each transcription factor (TF) requires convergent evidence from independent sources. Two significant sources of evidence are TF binding locations and the transcriptional responses to direct TF perturbations. Systematic data sets of both types exist for yeast and human. Standard analysis of the genes whose regulatory DNA is bound by a TF, assayed by ChIP-chip/seq, and the genes that respond to a perturbation of that TF, shows that these two data sources rarely converge on a common set of direct, functional targets. Even taking the few genes that are both bound and responsive as direct functional targets is not safe -- when there are many non-functional binding sites and many indirect targets, non-functional sites are expected to occur in the cis-regulatory DNA of indirect targets by chance. To address this problem, we introduce Dual Threshold Optimization, a new method for setting significance thresholds on binding and response data, and show that it improves convergence. It also enables comparison of binding data to perturbation-response data that has been processed by network inference algorithms, which further improves convergence. Next, we analyze a comprehensive new data set measuring the transcriptional response shortly after inducing overexpression of a yeast TF. We also present a new yeast binding location data set obtained by transposon calling cards and compare it to recent ChIP-exo data. The combination of dual threshold optimization and network inference greatly expands the high-confidence TF network map in both yeast and human. In yeast, measuring the response shortly after inducing TF overexpression and measuring binding locations by using transposon calling cards or ChIP-exo improve the network synergistically.
2020-02-04 | GSE144657 | GEO
Project description:Non-coding genetic variants/mutations can play functional roles in the cell by disrupting regulatory interactions between transcription factors (TFs) and their genomic target sites. For most human TFs, a myriad of DNA-binding models are available and could be used to predict the effects of DNA mutations on TF binding. However, information on the quality of these models is scarce, making it hard to evaluate the statistical significance of predicted binding changes. Here, we present QBiC-Pred, a web server for predicting quantitative TF binding changes due to nucleotide variants. QBiC-Pred uses regression models of TF binding specificity trained on high-throughput in vitro data. The training is done using ordinary least squares (OLS), and we leverage distributional results associated with OLS estimation to compute, for each predicted change in TF binding, a p-value reflecting our confidence in the predicted effect. We show that OLS models are accurate in predicting the effects of mutations on TF binding in vitro and in vivo, outperforming widely-used PWM models as well as recently developed deep learning models of specificity. QBiC-Pred takes as input mutation data sets in several formats, and it allows post-processing of the results through a user- friendly web interface. QBiC-Pred is freely available at http://qbic.genome.duke.edu.
2019-05-08 | GSE130832 | GEO