Dual threshold optimization and network inference reveal convergent evidence from TF binding locations and TF perturbation responses
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PROVIDER: PRJNA604506 | ENA |
REPOSITORIES: ENA
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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
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Project description:Transcriptional regulatory networks (TRNs) provide insight into cellular behavior by describing interactions between transcription factors (TFs) and their gene targets. The Assay for Transposase Accessible Chromatin (ATAC)-seq, coupled with transcription-factor motif analysis, provides indirect evidence of chromatin binding for hundreds of TFs genome-wide. Here, we propose methods for TRN inference in a mammalian setting, using ATAC-seq data to influence gene expression modeling. We rigorously test our methods in the context of T Helper Cell Type 17 (Th17) differentiation, generating new ATAC-seq data to complement existing Th17 genomic resources (plentiful gene expression data, TF knock-outs and ChIP-seq experiments). In this resource-rich mammalian setting our extensive benchmarking provides quantitative, genome-scale evaluation of TRN inference combining ATAC-seq and RNA-seq data. We refine and extend our previous Th17 TRN, using our new TRN inference methods to integrate all Th17 data (gene expression, ATAC-seq, TF KO, ChIP-seq). We highlight new roles for individual TFs and groups of TFs (“TF-TF modules”) in Th17 gene regulation. Given the popularity of ATAC-seq (a widely adapted protocol with high resolution and low sample input requirements), we anticipate that application of our methods will improve TRN inference in new mammalian systems and be of particular use for rare, uncharacterized cell types.
2018-12-16 | GSE113720 | GEO
Project description:Transcriptional regulatory networks (TRNs) provide insight into cellular behavior by describing interactions between transcription factors (TFs) and their gene targets. The Assay for Transposase Accessible Chromatin (ATAC)-seq, coupled with transcription-factor motif analysis, provides indirect evidence of chromatin binding for hundreds of TFs genome-wide. Here, we propose methods for TRN inference in a mammalian setting, using ATAC-seq data to influence gene expression modeling. We rigorously test our methods in the context of T Helper Cell Type 17 (Th17) differentiation, generating new ATAC-seq data to complement existing Th17 genomic resources (plentiful gene expression data, TF knock-outs and ChIP-seq experiments). In this resource-rich mammalian setting our extensive benchmarking provides quantitative, genome-scale evaluation of TRN inference combining ATAC-seq and RNA-seq data. We refine and extend our previous Th17 TRN, using our new TRN inference methods to integrate all Th17 data (gene expression, ATAC-seq, TF KO, ChIP-seq). We highlight new roles for individual TFs and groups of TFs (“TF-TF modules”) in Th17 gene regulation. Given the popularity of ATAC-seq (a widely adapted protocol with high resolution and low sample input requirements), we anticipate that application of our methods will improve TRN inference in new mammalian systems and be of particular use for rare, uncharacterized cell types.
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Project description:Desulfuromonas sp. TF strain:TF Genome sequencing and assembly
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Project description:This dataset uses DNase-seq to profile the genome-wide DNase I hypersensitivity of mES and mES-derived cells along an early pancreatic lineage and provides the locations of putative Transcription Factor (TF) binding sites using the PIQ algorithm. DNase-seq takes advantage of the preferential cutting of DNase I in open chromatin and steric blockage of of DNase I by tightly bound TFs that protect associated genomic DNA sequences. After deep sequencing of DNase IM-bM-^@M-^Sdigested genomic DNA from intact nuclei, genome-wide data on chromatin accessibility as well as TF-specific DNase I protection profiles that reveal the genomic binding locations of a majority of TFs are obtained. Such TF signature M-bM-^@M-^XDNase profilesM-bM-^@M-^Y reflect the effect of the TF on DNA shape and local chromatin architecture, extending hundreds of base pairs from a TF binding site, and these profiles are centered on M-bM-^@M-^XDNase footprintsM-bM-^@M-^Y at the binding motif itself, which reflects the biophysics of protein-DNA binding. An algorithm, PIQ, is then applied that models the specific profile of each factor, and in combination with sequence information predicts the likely binding locations of over 700 TFs genome wide. This dataset includes DNase-seq hypersensitivity data from 6 mES-derived cell types: mESC, Mesendoderm, Mesoderm, Endoderm, Intestinal Endoderm, and Prepancreatic Endoderm. For each cell type, TF binding site predictions are made based on the identification TF-specific DNase-seq profiles over any of 1331 possible binding motifs. After significance thesholding, genome-wide binding site predictions for <700 TFs are included.
2014-01-19 | E-GEOD-53776 | biostudies-arrayexpress
Project description: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 Protein binding microarray (PBM) experiments were performed for a set of 1048 diverse eukaryotic 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.
2014-08-01 | E-GEOD-53348 | biostudies-arrayexpress
Project description:Genome control is operated by transcription factors (TF) controlling their target genes by binding to promoters and enhancers. Conceptually, the interactions between TFs, their binding sites, and their functional targets are represented by gene regulatory networks (GRN). Deciphering in vivo GRNs underlying organ development in an unbiased genome-wide setting involves identifying both functional TF-gene interactions and physical TF-DNA interactions. To reverse-engineer the GRN of eye development in Drosophila, we performed RNA-seq across 72 genetic perturbations and sorted cell types, and inferred a co-expression network. Next, we derived direct TF-DNA interactions using computational motif inference, ultimately connecting 241 TFs to 5632 direct target genes through 24926 enhancers. Using this network we found network motifs, cis-regulatory codes, and new regulators of eye development. We validate the predicted target regions of Grainyhead by ChIP-seq and identify this factor as a general co-factor in the eye network, being bound to thousands of nucleosome-free regions.
2014-12-01 | GSE62557 | GEO
Project description:Genome control is operated by transcription factors (TF) controlling their target genes by binding to promoters and enhancers. Conceptually, the interactions between TFs, their binding sites, and their functional targets are represented by gene regulatory networks (GRN). Deciphering in vivo GRNs underlying organ development in an unbiased genome-wide setting involves identifying both functional TF-gene interactions and physical TF-DNA interactions. To reverse-engineer the GRN of eye development in Drosophila, we performed RNA-seq across 72 genetic perturbations and sorted cell types, and inferred a co-expression network. Next, we derived direct TF-DNA interactions using computational motif inference, ultimately connecting 241 TFs to 5632 direct target genes through 24926 enhancers. Using this network we found network motifs, cis-regulatory codes, and new regulators of eye development. We validate the predicted target regions of Grainyhead by ChIP-seq and identify this factor as a general co-factor in the eye network, being bound to thousands of nucleosome-free regions.
2014-12-01 | GSE59059 | GEO