Dual threshold optimization and network inference reveal convergent evidence from TF binding locations and TF perturbation responses
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ABSTRACT:
PROVIDER: PRJNA604506 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR11003600_1.fastq.gz | Fastqsanger.gz | |||
| SRR11003600_2.fastq.gz | Fastqsanger.gz | |||
| SRR11003601_1.fastq.gz | Fastqsanger.gz | |||
| SRR11003601_2.fastq.gz | Fastqsanger.gz | |||
| SRR11003594_1.fastq.gz | Fastqsanger.gz |
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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
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, but they rarely converge on a common set of direct, functional targets for a TF. Even the few genes that are both bound and responsive may not be direct functional targets. Our analysis shows that when there are many nonfunctional binding sites and many indirect targets, nonfunctional sites are expected to occur in the cis-regulatory DNA of indirect targets by chance. To address this problem, we introduce dual threshold optimization (DTO), a new method for setting significance thresholds on binding and perturbation-response data, and show that it improves convergence. It also enables comparison of binding data to perturbation-response data that have been processed by network inference algorithms, which further improves convergence. The combination of dual threshold optimization and network inference greatly expands the high-confidence TF network map in both yeast and human. 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. These new data sets improve convergence and expand the high-confidence network synergistically.
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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.
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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.
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