Ontology highlight
ABSTRACT:
PROVIDER: PRJNA723898 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR14662800_1.fastq.gz | Fastqsanger.gz | |||
| SRR14662800_2.fastq.gz | Fastqsanger.gz | |||
| SRR14662900_1.fastq.gz | Fastqsanger.gz | |||
| SRR14662900_2.fastq.gz | Fastqsanger.gz | |||
| SRR14662801_1.fastq.gz | Fastqsanger.gz |
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Project description:This model is from the article:
A regulatory role for repeated decoy transcription factor binding sites in target gene expression.
Lee TH, Maheshri N. Mol Syst Biol.
2012 Mar 27;8:576. 22453733
,
Abstract:
Tandem repeats of DNA that contain transcription factor (TF) binding sites could serve as decoys, competitively binding to TFs and affecting target gene expression. Using a synthetic system in budding yeast, we demonstrate that repeated decoy sites inhibit gene expression by sequestering a transcriptional activator and converting the graded dose-response of target promoters to a sharper, sigmoidal-like response. On the basis of both modeling and chromatin immunoprecipitation measurements, we attribute the altered response to TF binding decoy sites more tightly than promoter binding sites. Tight TF binding to arrays of contiguous repeated decoy sites only occurs when the arrays are mostly unoccupied. Finally, we show that the altered sigmoidal-like response can convert the graded response of a transcriptional positive-feedback loop to a bimodal response. Together, these results show how changing numbers of repeated TF binding sites lead to qualitative changes in behavior and raise new questions about the stability of TF/promoter binding.
Note:
This model corresponds to the basic model for gene expression in the presence of decoys, described in the paper.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication
for more information.
In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Novère N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.
2005-01-01 | MODEL1202270000 | BioModels
Project description:This model is from the article:
A regulatory role for repeated decoy transcription factor binding sites in target gene expression.
Lee TH, Maheshri N. Mol Syst Biol.
2012 Mar 27;8:576. 22453733
,
Abstract:
Tandem repeats of DNA that contain transcription factor (TF) binding sites could serve as decoys, competitively binding to TFs and affecting target gene expression. Using a synthetic system in budding yeast, we demonstrate that repeated decoy sites inhibit gene expression by sequestering a transcriptional activator and converting the graded dose-response of target promoters to a sharper, sigmoidal-like response. On the basis of both modeling and chromatin immunoprecipitation measurements, we attribute the altered response to TF binding decoy sites more tightly than promoter binding sites. Tight TF binding to arrays of contiguous repeated decoy sites only occurs when the arrays are mostly unoccupied. Finally, we show that the altered sigmoidal-like response can convert the graded response of a transcriptional positive-feedback loop to a bimodal response. Together, these results show how changing numbers of repeated TF binding sites lead to qualitative changes in behavior and raise new questions about the stability of TF/promoter binding.
Note:
This model corresponds to the comprehensive model encompassing the basic model (MODEL1202270000) as well as the tTA/dox (tet-transcriptional activator/doxycycline) interaction, described in the paper.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication
for more information.
In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Novère N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.
2005-01-01 | MODEL1202270001 | BioModels
Project description:The binding and contribution of transcription factors (TF) to cell specific gene expression is often deduced from open-chromatin measurements to avoid cost and labour intensive TF ChIP-seq assays.It is important to develop reliable and fast computational methods for accurate TF binding prediction in open-chromatin regions (OCRs). Here, we report a novel segmentation-based method, TEPIC, to predict TF binding by combining sets of OCRs with position weight matrices.TEPIC can be applied to various open-chromatin data, e.g. DNaseI-seq and NOMe-seq, using either peaks or footprints as input.In addition to open-chromatin data, also Histone-Marks (HMs) can be used in TEPIC to identify candidate TF binding sites.TEPIC computes TF affinities and uses open-chromatin/HM signal intensity as quantitative measures of TF binding strength.Using machine learning techniques, we show that incorporating low affinity binding sites improves our ability to explain gene expression variability compared to the standard presence/absence classification of binding sites.Further, we show that both footprints and peaks capture essential TF binding events and lead to a good prediction performance.In our application, gene-based scores computed by TEPIC with one open-chromatin assay nearly reach the quality of several TF ChIP-seq datasets.Finally, we show that these scores correctly predict known transcriptional regulators as illustrated by the application to novel DNaseI-seq and NOMe-seq data for primary human hepatocytes and CD4+ T-cells, respectively.
| EGAS00001002073 | EGA
Project description:Transcription factors (TF) binding is key to understanding and characterizing the effect of genetic variability on phenotypic differences. Here, we used a novel scalable ChIP-seq approach to annotate the regulatory landscape of the maize genome with binding data from 104 leaf TFs. TF binding regions co-localized with open chromatin regions, with ~70% of TF binding nearby genes. TF binding sites are evolutionarily conserved and show enrichment for GWAS-hits, cis-expression QTLs. Furthermore, the regulatory network shows characteristics of real-word networks such as scale-free topology and larger modularity than random graphs. Finally, machine-learning analyses reveal that sequence preferences are alike within TF families, and that TF co-localization is key for TF binding specificity. Our comprehensive TF-DNA interaction approach provides the starting point to decipher the gene regulatory system in plant leaves.
2020-03-01 | GSE137972 | GEO
Project description:Understanding gene regulatory networks (GRNs) is essential for improving maize yield and quality through molecular breeding approaches. The lack of comprehensive transcription factor (TF)-DNA interaction data has hindered accurate GRN predictions, limiting our insight into the regulatory mechanisms. In this study, we performed large-scale profiling of maize TF binding sites. We obtained and collected reliable binding profiles for 513 TFs, identified 394,136 binding sites, and constructed an accuracy-enhanced maize GRN (mGRN+) by integrating chromatin accessibility and gene expression data. The mGRN+ comprises 397,699 regulatory relationships. We further divided the mGRN+ into multiple modules across six major tissues. Using machine-learning algorithms, we optimized the mGRN+ to improve the prediction accuracy of gene functions and key regulators. Through independent genetic validation experiments, we further confirmed the reliability of these predictions. This work provides the largest collection of experimental TF binding sites in maize and highly optimized regulatory networks, which serve as valuable resources for studying maize gene function and crop improvement.
2025-08-06 | GSE256146 | GEO
Project description:Transposable elements (TE) have been shown to contrain functional transcription factor (TF) binding sites for long, but the extent to which TEs contribute TF binding sites is not well know. Here, we comprehensively mapped binding sites for 26 pairs of orthologous TFs, in two pairs of human and mouse cell lines (i.e., leukemia, and lymphoblast), along with epigenomic profiles representing DNA methylation and six histone modifications. We found that on average, 20% of TF binding sites were embedded in TEs. We further identified 710 TF-TE relationships in which certain TE subfamilies enriched for TF binidng sites. TE-derived TF binding peaks were also strongly associated with decreased DNA methylation and increased enhancer-associated histone marks. Most of the TE-derived TF binding sites were species-specific, but we also identified conserved binding sites. Additionally, 66% of TE-derived TF binding events were cell-type specific, associated with cell-type specific epigenetic landscape. For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf To evaluate the contribution of transposable elements (TE) to transcription factor (TF) binding landscapes, we profiled ChIP-seq datasets for 26 TFs in two cell lines in human and mouse, generated by the ENCODE and MouseENCODE consortia. The epigenomic profiles were evaluated from six histone modification in each of the cell lines, also generated by the consortia. We added DNA methylation to the epigenomic profiles, using two complementary techniques, MeDIP-seq and MRE-seq. The mouse data related to this study are available through GSE57230: http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE57230
2014-05-09 | E-GEOD-56774 | biostudies-arrayexpress
Project description:Transposable elements (TE) have been shown to contrain functional transcription factor (TF) binding sites for long, but the extent to which TEs contribute TF binding sites is not well know. Here, we comprehensively mapped binding sites for 26 pairs of orthologous TFs, in two pairs of human and mouse cell lines (i.e., leukemia, and lymphoblast), along with epigenomic profiles representing DNA methylation and six histone modifications. We found that on average, 20% of TF binding sites were embedded in TEs. We further identified 710 TF-TE relationships in which certain TE subfamilies enriched for TF binidng sites. TE-derived TF binding peaks were also strongly associated with decreased DNA methylation and increased enhancer-associated histone marks. Most of the TE-derived TF binding sites were species-specific, but we also identified conserved binding sites. Additionally, 66% of TE-derived TF binding events were cell-type specific, associated with cell-type specific epigenetic landscape. For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf To evaluate the contribution of transposable elements (TE) to transcription factor (TF) binding landscapes, we profiled ChIP-seq datasets for 26 TFs in two cell lines in human and mouse, generated by the ENCODE and MouseENCODE consortia. The epigenomic profiles were evaluated from six histone modification in each of the cell lines, also generated by the consortia. We added DNA methylation to the epigenomic profiles, using two complementary techniques, MeDIP-seq and MRE-seq. The human data related to this study are available through GSE56774: http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE56774
2014-05-09 | E-GEOD-57230 | biostudies-arrayexpress
Project description:Plant development is controlled by transcription factors (TFs) which form complex gene-regulatory networks. Genome-wide TF DNA-binding studies revealed that these TFs have several thousands of binding sites in the Arabidopsis genome, and may regulate the expression of many genes directly. Given the importance of natural variation in plant developmental programs, there is a need to understand the molecular basis of this variation at the level of developmental gene regulation. However, until now, the evolutionary turnover and dynamics of TF binding sites among plant species has not yet experimentally determined. Here, we performed comparative ChIP-seq studies of the MADS-box TF SEPALLATA3 (SEP3) in inflorescences of two Arabidopsis species: A. thaliana and A. lyrata. Comparative RNA-seq analysis shows that the loss/gain of BSs is often followed by a change in gene expression.
2015-10-11 | GSE63463 | GEO
Project description:A core task to understand the consequences of non-coding single nucleotide polymorphisms (SNP) is to identify their genotype specific binding of transcription factor (TF). Here, we generate a large-scale TF-SNP interaction map for a selection of 116 colorectal cancer (CRC) risk loci and validated TF binding to 10 putatively functional SNPs. Our data further revealed TF binding complexity adjacent to the 116 risk loci, adding an additional layer of understanding to regulatory networks associated with CRC relevant loci.
2017-03-30 | MSV000080810 | MassIVE
Project description:Detecting in vivo transcription factor (TF) binding is important for understanding gene regulatory circuitries. ChIP-seq is a powerful technique to empirically define TF binding in vivo. However, the multitude of distinct TFs makes genome-wide profiling for them all labor-intensive and costly. Algorithms for in silico prediction of TF binding have been developed, based mostly on histone modification or DNase I hypersensitivity data in conjunction with DNA motif and other genomic features. However, technical limitations of these methods prevent them from being applied broadly, especially in clinical settings. We conducted a comprehensive survey involving multiple cell lines, TFs, and methylation types and found that there are intimate relationships between TF binding and methylation level changes around the binding sites. Exploiting the connection between DNA methylation and TF binding, we proposed a novel supervised learning approach to predict TF-DNA interaction using data from base-resolution whole-genome methylation sequencing experiments. We devised beta-binomial models to characterize methylation data around TF binding sites and the background. Along with other static genomic features, we adopted a random forest framework to predict TF-DNA interaction. After conducting comprehensive tests, we saw that the proposed method accurately predicts TF binding and performs favorably versus competing methods. Examine Oct4 genome-wide binding in mouse embryonic stem cells (E14)
2015-03-02 | E-GEOD-65093 | biostudies-arrayexpress