Dataset Information

On the identification of differentially-active transcription factors from ATAC-seq data.

ABSTRACT: ATAC-seq has emerged as a rich epigenome profiling technique, and is commonly used to identify Transcription Factors (TFs) underlying given phenomena. A number of methods can be used to identify differentially-active TFs through the accessibility of their DNA-binding motif, however little is known on the best approaches for doing so. Here we benchmark several such methods using a combination of curated datasets with various forms of short-term perturbations on known TFs, as well as semi-simulations. We include both methods specifically designed for this type of data as well as some that can be repurposed for it. We also investigate variations to these methods, and identify three particularly promising approaches (chromVAR-limma with critical adjustments, monaLisa and a combination of GC smooth quantile normalization and multivariate modeling). We further investigate the specific use of nucleosome-free fragments, the combination of top methods, and the impact of technical variation. Finally, we illustrate the use of the top methods on a novel dataset to characterize the impact on DNA accessibility of TRAnscription Factor TArgeting Chimeras (TRAFTAC), which can deplete TFs - in our case NFkB - at the protein level.

SUBMITTER: Gerbaldo F

PROVIDER: S-EPMC10942475 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature

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On the identification of differentially-active transcription factors from ATAC-seq data.

Gerbaldo Felix Ezequiel FE Sonder Emanuel E Fischer Vincent V Frei Selina S Wang Jiayi J Gapp Katharina K Robinson Mark D MD Germain Pierre-Luc PL

bioRxiv : the preprint server for biology 20240820

ATAC-seq has emerged as a rich epigenome profiling technique, and is commonly used to identify Transcription Factors (TFs) underlying given phenomena. A number of methods can be used to identify differentially-active TFs through the accessibility of their DNA-binding motif, however little is known on the best approaches for doing so. Here we benchmark several such methods using a combination of curated datasets with various forms of short-term perturbations on known TFs, as well as semi-simulati ...[more]

PMID: 38496482

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Project description:Expression quantitative trait loci (eQTL) analysis is useful for identifying genetic variants correlated with gene expression, however, it cannot distinguish between causal and nearby non-functional variants. Because the majority of disease-associated SNPs are located in regulatory regions, they can impact allele-specific binding (ASB) of transcription factors and result in differential expression of the target gene alleles. In this study, our aim was to identify functional single-nucleotide polymorphisms (SNPs) that alter transcriptional regulation and thus, potentially impact cellular function. Here, we present regSNPs-ASB, a generalized linear model-based approach to identify regulatory SNPs that are located in transcription factor binding sites. The input for this model includes ATAC-seq (assay for transposase-accessible chromatin with high-throughput sequencing) raw read counts from heterozygous loci, where differential transposase-cleavage patterns between two alleles indicate preferential transcription factor binding to one of the alleles. Using regSNPs-ASB, we identified 53 regulatory SNPs in human MCF-7 breast cancer cells and 125 regulatory SNPs in human mesenchymal stem cells (MSC). By integrating the regSNPs-ASB output with RNA-seq experimental data and publicly available chromatin interaction data from MCF-7 cells, we found that these 53 regulatory SNPs were associated with 74 potential target genes and that 32 (43%) of these genes showed significant allele-specific expression. By comparing all of the MCF-7 and MSC regulatory SNPs to the eQTLs in the Genome-Tissue Expression (GTEx) Project database, we found that 30% (16/53) of the regulatory SNPs in MCF-7 and 43% (52/122) of the regulatory SNPs in MSC were also in eQTL regions. The enrichment of regulatory SNPs in eQTLs indicated that many of them are likely responsible for allelic differences in gene expression (chi-square test, p-value < 0.01). In summary, we conclude that regSNPs-ASB is a useful tool for identifying causal variants from ATAC-seq data. This new computational tool will enable efficient prioritization of genetic variants identified as eQTL for further studies to validate their causal regulatory function. Ultimately, identifying causal genetic variants will further our understanding of the underlying molecular mechanisms of disease and the eventual development of potential therapeutic targets.

Project description:Genomic enhancers regulate spatio-temporal gene expression by recruiting specific combinations of transcription factors (TFs). When TFs are bound to active regulatory regions, they displace canonical nucleosomes, making these regions biochemically detectable as nucleosome-depleted regions or accessible/open chromatin. Here we ask whether open chromatin profiling can be used to identify the entire repertoire of active promoters and enhancers underlying tissue-specific gene expression during normal development and oncogenesis in vivo. To this end, we first compare two different approaches to detect open chromatin in vivo using the Drosophila eye primordium as a model system: FAIRE-seq, based on physical separation of open versus closed chromatin; and ATAC-seq, based on preferential integration of a transposon into open chromatin. We find that both methods reproducibly capture the tissue-specific chromatin activity of regulatory regions, including promoters, enhancers, and insulators. Using both techniques, we screened for regulatory regions that become ectopically active during Ras-dependent oncogenesis, and identified 3778 regions that become (over-)activated during tumor development. Next, we applied motif discovery to search for candidate transcription factors that could bind these regions and identified AP-1 and Stat92E as key regulators. We validated the importance of Stat92E in the development of the tumors by introducing a loss of function Stat92E mutant, which was sufficient to rescue the tumor phenotype. Additionally we tested if the predicted Stat92E responsive regulatory regions are genuine, using ectopic induction of JAK/STAT signaling in developing eye discs, and observed that similar chromatin changes indeed occurred. Finally, we determine that these are functionally significant regulatory changes, as nearby target genes are up- or down-regulated. In conclusion, we show that FAIRE-seq and ATAC-seq based open chromatin profiling, combined with motif discovery, is a straightforward approach to identify functional genomic regulatory regions, master regulators, and gene regulatory networks controlling complex in vivo processes.

Dataset Information

On the identification of differentially-active transcription factors from ATAC-seq data.

Publications

On the identification of differentially-active transcription factors from ATAC-seq data.

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