Exploiting genetic variation to uncover rules of transcription factor binding and chromatin accessibility

Behera, Vivek; Evans, Perry; Face, Carolyne; Hamagami, Nicole; Sankaranarayanan, Laavanya; Keller, Cheryl A.; Giardine, Belinda; Tan, Kai; Hardison, Ross C.; Shi, Junwei; Blobel, Gerd A.

doi:10.1038/s41467-018-03082-6

Cited by 40 publications

(30 citation statements)

References 82 publications

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“…However, interestingly, we observed some strong longrange interactions between motifs as far as 70 bp apart (Fig. 3B), an observation corroborated by a recent analysis of SNP effects on TAL1 ChIP-seq signal in erythroid cells that found that GATA1 motif mutations impact TAL1 binding at distances as great as 75 bp 13 . The interactions were also symmetric, such that mutating TAL1 demonstrated a distribution of on GATA1 (SFig.…”

Section: Comparison Of Dfim To Shap Interaction Scores and Pairwise Isupporting

confidence: 89%

Discovering epistatic feature interactions from neural network models of regulatory DNA sequences

Greenside

Shimko

Fordyce

et al. 2018

Preprint

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Transcription factors bind complex regulatory DNA sequence patterns in a combinatorial manner to modulate gene expression. Deep neural networks (DNNs) can learn these cis-regulatory grammars encoded in regulatory DNA sequences associated with transcription factor binding and chromatin accessibility. Several feature attribution methods have been developed for estimating the predictive importance of individual features (nucleotides or motifs) in any input DNA sequence to its associated output prediction from a DNN model. However, these methods do not reveal higher-order, epistatic feature interactions encoded by the models. We present a new method called Deep Feature Interaction Maps (DFIM) to efficiently estimate interactions between all pairs of features in any input DNA sequence. DFIM accurately identifies ground truth motif interactions embedded in simulated regulatory DNA sequences. DFIM identifies synergistic interactions between GATA1 and TAL1 motifs from in vivo TF binding models. DFIM reveals epistatic interactions involving nucleotides flanking the core motif of the Cbf1 TF in yeast from in vitro TF binding models. We also apply DFIM to regulatory sequence models of in vivo chromatin accessibility to reveal interactions between regulatory genetic variants and proximal motifs of target TFs as validated by TF binding quantitative trait loci. Our approach makes significant strides in improving the interpretability of deep learning models for genomics.

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Section: Comparison Of Dfim To Shap Interaction Scores and Pairwise Isupporting

confidence: 89%

Discovering epistatic feature interactions from neural network models of regulatory DNA sequences

Greenside

Shimko

Fordyce

et al. 2018

Preprint

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“…This complex binds to the regulatory regions (i.e., active H3K27ac + enhancers) of genes involved in HSPC biology . In HSPCs, GATA2 marks a subset of bivalent H3K27me3 + H3K4me3 + regulatory regions that are bound by GATA1 upon erythroid differentiation and tend to be located close to erythroid‐specific genes—suggesting that GATA2 is at least partially involved in lineage priming . Lastly, upon activation of the BMP and Wnt signaling pathways in HSPCs, SMAD and TCF co‐occupy GATA2‐bound enhancers associated with actively transcribed genes and enhance transcriptional activation by GATA2 …”

Section: Gata2mentioning

confidence: 99%

“…24,25,37,38 However, GATA1 and KLF1 were shown to bind conjointly to several erythroid enhancers and genes and thus cooperate in the gene induction process. 15,24,25,32,[39][40][41] The transcriptional repressor GFI1B and GATA1 co-bind to some gene loci that are repressed upon erythroid development-suggesting that the two factors cooperate to repress gene expression in erythroid cells. 9,27 The transcriptional co-factor FOG1 is required for GATA1 activating or repressing activity at many loci in erythroid and megakaryocytic cells, [33][34][35]42 although genomewide studies of FOG1 occupancy have not yet been performed.…”

Section: Gata1mentioning

confidence: 99%

“…[71][72][73] In HSPCs, GATA2 marks a subset of bivalent H3K27me3 + H3K4me3 + regulatory regions that are bound by GATA1 upon erythroid differentiation and tend to be located close to erythroid-specific genes-suggesting that GATA2 is at least partially involved in lineage priming. 16,41,51,71,72 Lastly, upon activation of the BMP and Wnt signaling pathways in HSPCs, SMAD and TCF co-occupy GATA2-bound enhancers associated with actively transcribed genes and enhance transcriptional activation by GATA2. 36 In megakaryocytes, GATA2-occupied regions are generally enriched in the activating H3K4me3 mark, although some of these regions map to H3K27me3 domains.…”

Section: Gata2mentioning

confidence: 99%

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GATA factor transcriptional activity: Insights from genome‐wide binding profiles

Romano

Miccio

2019

IUBMB Life

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The members of the GATA family of transcription factors have homologous zinc fingers and bind to similar sequence motifs. Recent advances in genome-wide technologies and the integration of bioinformatics data have led to a better understanding of how GATA factors regulate gene expression; GATA-factor-induced transcriptional and epigenetic changes have now been analyzed at unprecedented levels of detail.Here, we review the results of genome-wide studies of GATA factor occupancy in human and murine cell lines and primary cells (as determined by chromatin immunoprecipitation sequencing), and then discuss the molecular mechanisms underlying the mediation of transcriptional and epigenetic regulation by GATA factors. K E Y W O R D S

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“…In addition, some CTCF loops are not constitutive, but rather cell type‐specific. This “dynamic” subset might involve several and non‐mutually exclusive mechanisms, such as: (i) tissue‐specific binding of CTCF mediated by cell type‐specific epigenetic modifications or transcription factors (Wang et al , ; Behera et al , ) and (ii) constitutively bound CTCF sites engaging into tissue‐specific interactions due to the action of additional “looping” co‐factors (Phillips‐Cremins et al , ; Huang et al , ).…”

Section: Introductionmentioning

confidence: 99%

Forces driving the three‐dimensional folding of eukaryotic genomes

Rada-Iglesias

Grosveld

Papantonis

2018

Molecular Systems Biology

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The last decade has radically renewed our understanding of higher order chromatin folding in the eukaryotic nucleus. As a result, most current models are in support of a mostly hierarchical and relatively stable folding of chromosomes dividing chromosomal territories into A‐ (active) and B‐ (inactive) compartments, which are then further partitioned into topologically associating domains (TADs), each of which is made up from multiple loops stabilized mainly by the CTCF and cohesin chromatin‐binding complexes. Nonetheless, the structure‐to‐function relationship of eukaryotic genomes is still not well understood. Here, we focus on recent work highlighting the biophysical and regulatory forces that contribute to the spatial organization of genomes, and we propose that the various conformations that chromatin assumes are not so much the result of a linear hierarchy, but rather of both converging and conflicting dynamic forces that act on it.

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Exploiting genetic variation to uncover rules of transcription factor binding and chromatin accessibility

Cited by 40 publications

References 82 publications

Discovering epistatic feature interactions from neural network models of regulatory DNA sequences

Discovering epistatic feature interactions from neural network models of regulatory DNA sequences

GATA factor transcriptional activity: Insights from genome‐wide binding profiles

Forces driving the three‐dimensional folding of eukaryotic genomes

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