Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Perspectives on Codebook: sequence specificity of uncharacterized human transcription factors

ABSTRACT: We describe an effort (“Codebook”) to determine the sequence specificity of 332 putative and largely uncharacterized human transcription factors (TFs), as well as 61 control TFs. Nearly 5,000 independent experiments, including in vitro and in vivo assays, produced motifs for most of the uncharacterized TFs analyzed (180, or 53%), the vast majority of which are unique to a single TF. The data highlight the extensive contribution of transposable elements to TF evolution, both in cis and trans, and identify tens of thousands of conserved, base-level binding sites in the human genome. The use of multiple platforms provides an unprecedented opportunity to benchmark and analyze TF sequence specificity, function, and evolution, as further explored in accompanying manuscripts. Over 1,421 human TFs are now associated with a DNA binding motif. Extrapolation from the Codebook benchmarking suggests that many of the binding motifs for well-studied TFs may inaccurately describe the TF’s true sequence preferences.

ORGANISM(S): synthetic construct Homo sapiens

PROVIDER: GSE275577 | GEO | 2024/11/11

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Genomic regions flanking E-box sites influence DNA binding specificity of bHLH transcription factors through DNA shape (validation)

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

Evaluation of methods for modeling transcription factor sequence specificity

Project description:Genomic analyses often involve scanning for potential transcription-factor (TF) binding sites using models of the sequence specificity of DNA binding proteins. Many approaches have been developed to model and learn a protein’s binding specificity by representing sequence motifs, including the gaps and dependencies between binding-site residues, but these methods have not been systematically compared. Here we applied 26 such approaches to in vitro protein binding microarray data for 66 mouse TFs belonging to various families. For 9 TFs, we also scored the resulting motif models on in vivo data, and found that the best in vitro–derived motifs performed similarly to motifs derived from in vivo data. Our results indicate that simple models based on mononucleotide position weight matrices learned by the best methods perform similarly to more complex models for most TFs examined, but fall short in specific cases. In addition, the best-performing motifs typically have relatively low information content, consistent with widespread degeneracy in eukaryotic TF sequence preferences. Protein binding microarray (PBM) experiments were performed for a set of 86 mouse transcription factors. Briefly, the PBMs involved binding GST-tagged DNA-binding proteins to two double-stranded 44K Agilent microarrays, each containing a different DeBruijn sequence design, in order to determine their sequence preferences. Details of the PBM protocol are described in Berger et al., Nature Biotechnology 2006.

2012-12-13 | E-GEOD-42864 | biostudies-arrayexpress

Project description:Transcription factor (TF) binding specificities (motifs) are essential to the analysis of noncoding DNA and gene regulation. Accurate prediction of TF sequence specificities is critical, because the hundreds of sequenced eukaryotic genomes encompass hundreds of thousands of TFs, and assaying each is currently infeasible. There is ongoing controversy regarding the efficacy of motif prediction methods, as well as the degree of motif diversification among related species. Here, we describe Similarity Regression (SR), a significantly improved method for predicting motifs. We have updated and expanded the Cis-BP database using SR, and validate its predictive capacity with new data from diverse eukaryotic TFs. SR inherently quantifies TF motif evolution, and we show that previous claims of near-complete conservation of motifs between human and Drosophila are grossly inflated, with nearly half the motifs in each species absent from the other. We conclude that diversification in DNA binding motifs is pervasive, and present a new tool and updated resource to study TF diversity and gene regulation across eukaryotes.

2019-03-08 | GSE121420 | GEO

Genomic regions flanking E-box sites influence DNA binding specificity of bHLH transcription factors through DNA shape (validation)

2013-03-29 | GSE44437 | GEO

Genomic regions flanking E-box sites influence DNA binding specificity of bHLH transcription factors through DNA shape (different concentrations)

2013-03-29 | GSE44436 | GEO

Evaluation of methods for modeling transcription factor sequence specificity

2012-12-13 | GSE42864 | GEO

P. falciparum-specific Genomic Context Protein-Binding Microarray

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

RNA-seq of GFP positive and negative fractions of zebrafish transgenic cell lines of sea sponge enhancers

Project description:To investigate the activity of sponge enhancers in vertebrates transgenic experiments was performed where sponge enhancers were inserted into zebrafish embryos and stable lines generated abstract: Transcription factors (TFs) bind DNA enhancer sequences to regulate gene transcription in animals. Unlike TFs, the evolution of enhancers has been difficult to trace because of their fast evolution. Here, we take enhancers in the sponge Amphimedon queenslandica and test their activity in zebrafish and mouse. Of the five sponge enhancers assessed, three were located in conserved syntenic gene regions that are unique to animals (Islet–Scaper, Ccne1–Uri, Tdrd3–Diaph3). Despite diverging over 700 million years ago and a dearth of sequence identity, sponge enhancers are able to drive cell type-specific reporter gene expression in vertebrates. Analysis of the type and frequency of TF binding motifs in the sponge Islet enhancer allowed for the identification of homologous enhancers in human and mouse, which show remarkably similar reporter expression patterns to the sponge enhancer. These findings uncover an unexpected deep conservation of enhancers and suggest that enhancers established early in metazoan evolution can remain functional through retention of combinations of transcription factor binding motifs despite substantial sequence divergence.

2019-11-30 | E-MTAB-7846 | biostudies-arrayexpress

Mapping and Analysis of Caenorhabditis elegans Transcription Factor Binding Specificities

Project description:The nematode Caenorhabditis elegans is a powerful model for studying gene regulation, as it has a compact genome and a wealth of genomic tools. However, identification of regulatory elements has been hampered by the fact that DNA binding motifs are known for only 71 (9%) of the estimated 763 high-confidence sequence-specific transcription factors (TFs). To address this problem, we performed protein binding microarray (PBM) experiments on representatives of canonical TF families in the C. elegans TF repertoire, obtaining motifs for 129 distinct TFs. Moreover, we can infer motifs for 97 additional TFs that have DNA binding domains that are very similar to those already characterized, resulting in a total coverage of binding specificities for almost 40% of the C. elegans TF repertoire. These data highlight the diversification of binding motifs for the nuclear hormone receptor (NHR) and C2H2 zinc finger families, and reveal unexpected diversity of motifs for others, including the T-box and DM families. Enrichment of motifs in the promoters of functionally related genes is consistent with known biology in many cases, and also identifies putative new regulatory roles for poorly characterized TFs. The motifs are available at http:// http://cisbp.ccbr.utoronto.ca. Protein binding microarray (PBM) experiments were performed for a set of 129 diverse C. elegans transcription factors. Briefly, the PBMs involved binding GST-tagged DNA-binding proteins to two double-stranded 44K Agilent microarrays, each containing a different DeBruijn sequence design, in order to determine their sequence preferences. Details of the PBM protocol are described in Berger et al., Nature Biotechnology 2006.

2015-04-23 | E-GEOD-65719 | biostudies-arrayexpress

Systematic discovery of uncharacterized transcription factors in Escherichia coli K-12 MG1655 (RNA-seq data set)

Project description:Transcriptional regulation enables cells to respond to environmental changes. Yet, among the estimated 304 candidate transcription factors (TFs) in Escherichia coli K-12 MG1655, only 185 have been experimentally characterized. Here we developed an integrated workflow that contains the prediction of TFs using machine learning and comprehensive experimental validation using a suite of genome-wide experiments. Applying this workflow we: 1) computationally identified 16 candidate genes encoding uncharacterized TFs; 2) confirmed that ten of these 16 are TF candidates that showed 255 DNA binding sites; 3) found high-confidence TF-binding sequence motifs for six of the ten TFs; 4) reconstructed the regulons of the ten TFs by determining gene expression change upon deletion of each TF; and 5) further determined the regulatory roles of three TFs (YdcI, YeiE and YiaJ) to be regulating acetate metabolism, iron homeostasis under iron limited condition, and utilization of L-ascorbate, respectively. Together, these results demonstrate how the integrated workflow can be used to discover, characterize, and elucidate regulatory functions of uncharacterized TFs. This workflow can be applied to less studied bacteria for systematic discovery and characterization of their transcriptional regulatory networks.

2018-08-08 | GSE111094 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data