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Determination and Inference of Eukaryotic Transcription Factor Sequence Specificity

ABSTRACT: The DNA sequence preferences of the vast majority of eukaryotic transcription factors (TFs) are unknown. Using an approach designed to broadly sample both DNA-binding domain types and eukaryotic clades, we have determined DNA-binding motifs for 1,033 TFs from 131 diverse eukaryotes, encompassing 54 domain types. Closely related orthologs and paralogs typically have very similar sequence preferences; this property allows inference of motifs for roughly one third of the 166,851 known or predicted eukaryotic TFs. While the origins of most motifs can be dated to hundreds of millions of years ago, we also characterize more recent TF expansions. Sequences matching the motifs are enriched upstream of TSS in most eukaryotic lineages, and at informative eQTL SNPs in Arabidopsis promoters, demonstrating their utility in mapping transcriptional networks. The motifs are housed at http://cisbp.ccbr.utoronto.ca

ORGANISM(S): synthetic construct

PROVIDER: GSE53348 | GEO | 2014/08/01

SECONDARY ACCESSION(S): PRJNA232033

REPOSITORIES: GEO

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Determination and Inference of Eukaryotic Transcription Factor Sequence Specificity

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2014-08-01 | E-GEOD-53348 | biostudies-arrayexpress

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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

Evaluation of methods for modeling transcription factor sequence specificity

2012-12-13 | GSE42864 | GEO

Joint sequence and chromatin neural networks characterize the differential abilities of Forkhead transcription factors to engage inaccessible chromatin (ChIP-exo)

Project description:The DNA-binding activities of transcription factors (TFs) are influenced by both intrinsic sequence preferences and extrinsic interactions with cell-specific chromatin landscapes and other regulatory proteins. Disentangling the roles of these determinants in TF-DNA binding remains challenging. For instance, the FoxA subfamily of Forkhead domain TFs are known pioneer factors, yet their binding varies across cell types, pointing to a combination of intrinsic and extrinsic forces guiding their binding. How such sequence and chromatin influences vary across related Forkhead domain TFs remains mostly uncharacterized. Here, we present a principled approach to compare the relative contributions of intrinsic DNA sequence preference and cell-specific chromatin environments to a TF’s DNA-binding activities. We over-express a selection of Fox TFs in mouse embryonic stem (mES) cells, which offer a platform to contrast each TF's binding activity within the same preexisting chromatin background. By developing and applying a neural network that jointly models sequence and chromatin data, we can evaluate how sequence and preexisting chromatin features contribute to induced TF binding, both at individual sites and genome-wide. We demonstrate that Fox TFs bind different DNA targets, and drive differential gene expression patterns, even when induced in identical chromatin settings. Differential Fox binding activities can be attributed to distinct DNA-binding preferences coupled with differential abilities to engage relatively inaccessible chromatin. We propose that varying preferences for preexisting chromatin states enables the functional diversification of paralogous TFs.

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Joint sequence and chromatin neural networks characterize the differential abilities of Forkhead transcription factors to engage inaccessible chromatin (ATAC-seq)

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Joint sequence and chromatin neural networks characterize the differential abilities of Forkhead transcription factors to engage inaccessible chromatin (RNA-seq)

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Mapping and Analysis of Caenorhabditis elegans Transcription Factor Binding Specificities

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

Diverse CGG binding proteins across eukaryotes produced by independent domestications of hAT transposons

Project description:The human transcription factor (TF) CGGBP1 (“CGG Binding Protein”) is conserved only in amniotes, and is believed to derive from the zf-BED and Hermes transposase DNA-binding domains (DBDs) of a hAT DNA transposon. Here, we examine the DNA binding preferences of a wide variety of metazoan CGGBP1-like TFs with this bipartite domain using PBM experiments. The derived motifs are available at ...

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ATAC-seq of frozen H1 Human Embryonic Stem Cells (hESC)

Project description:We performed Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) to profile genome-wide chromatin accessibility in the human H1 embryonic stem cell (ESC) line. We used this data to train a deep learning model called ChromBPNet which can accurately predict base-resolution accessibility profiles as a function of DNA sequence, while accounting for and correcting biases due the sequence preferences of the Tn5 transposase used in ATAC-seq. We interpreted the models to identify globally predictive transcription factor (TF) motifs, individual predictive motif instances in all accessible regions and Tn5-bias corrected canonical footprints of TFs at these predictive motifs.

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