A synergistic DNA logic predicts genome-wide chromatin accessibility

Hashimoto, Tatsunori; Sherwood, Richard I.; Kang, Daniel; Rajagopal, Nisha; Barkal, Amira; Zeng, Haoyang; Emons, Bart J M; Srinivasan, Sharanya; Jaakkola, Tommi S.; Gifford, David K.

doi:10.1101/gr.199778.115

Cited by 19 publications

(32 citation statements)

References 37 publications

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“…An alternative is to perform de novo motif discovery on accessible regions using machine learning frameworks that identify sequence features that are predictive of accessibility or ChIP-seq data. In particular, SeqGL [70] and gkm-SVM [71, 72] use a binary classification framework to discriminate peak from non-peak or flanking regions using k -mer features, while the Synergistic Chromatin Model (SCM) [73] performs L1-regularized Poisson regression to predict quantitative accessibility signal. These approaches can identify sequence specific motifs based on the selected k -mers.…”

Section: Identification Of Regulatory Sequence Elements and Their Genmentioning

confidence: 99%

Inference of cell type specific regulatory networks on mammalian lineages

Chasman

Roy

2017

Current Opinion in Systems Biology

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Transcriptional regulatory networks are at the core of establishing cell type specific gene expression programs. In mammalian systems, such regulatory networks are determined by multiple levels of regulation, including by transcription factors, chromatin environment, and three-dimensional organization of the genome. Recent efforts to measure diverse regulatory genomic datasets across multiple cell types and tissues offer unprecedented opportunities to examine the context-specificity and dynamics of regulatory networks at a greater resolution and scale than before. In parallel, numerous computational approaches to analyze these data have emerged that serve as important tools for understanding mammalian cell type specific regulation. In this article, we review recent computational approaches to predict the expression and sequence-based regulators of a gene’s expression level and examine long-range gene regulation. We highlight promising approaches, insights gained, and open challenges that need to be overcome to build a comprehensive picture of cell type specific transcriptional regulatory networks.

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Section: Identification Of Regulatory Sequence Elements and Their Genmentioning

confidence: 99%

Inference of cell type specific regulatory networks on mammalian lineages

Chasman

Roy

2017

Current Opinion in Systems Biology

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“…In particular, we aimed to identify DNA sequences that could predict cell-type-specific effects of regulatory variants. We investigated the use of machine learning models to predict the chromatin activity of regulatory elements across our three cell types using DNA sequence only (Zhou and Troyanskaya 2015;Hashimoto et al 2016;Kelley et al 2016;Zeng et al 2016). We developed a four-layered neural network architecture, OrbWeaver, to predict cell-type-specific chromatin accessibility of 500-bp windows centered at a regulatory locus ( Fig.…”

Section: Sequence-based Model For Chromatin Activity Explains the Regmentioning

confidence: 99%

Impact of regulatory variation across human iPSCs and differentiated cells

et al. 2017

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Induced pluripotent stem cells (iPSCs) are an essential tool for studying cellular differentiation and cell types that are otherwise difficult to access. We investigated the use of iPSCs and iPSC-derived cells to study the impact of genetic variation on gene regulation across different cell types and as models for studies of complex disease. To do so, we established a panel of iPSCs from 58 well-studied Yoruba lymphoblastoid cell lines (LCLs); 14 of these lines were further differentiated into cardiomyocytes. We characterized regulatory variation across individuals and cell types by measuring gene expression levels, chromatin accessibility, and DNA methylation. Our analysis focused on a comparison of inter-individual regulatory variation across cell types. While most cell-type-specific regulatory quantitative trait loci (QTLs) lie in chromatin that is open only in the affected cell types, we found that 20% of cell-type-specific regulatory QTLs are in shared open chromatin. This observation motivated us to develop a deep neural network to predict open chromatin regions from DNA sequence alone. Using this approach, we were able to use the sequences of segregating haplotypes to predict the effects of common SNPs on cell-type-specific chromatin accessibility.

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“…Briefly, a library of synthetic oligos containing a 99-bp variable region (phrase) flanked by short constant sequences used as primers is integrated into specific genomic locations in mouse embryonic stem cells (mESCs) by CRISPR/Cas9 based homology-directed repair. Previous work has shown that 20-40% of alleles will have site-specific integration of one of the variable phrases [Hashimoto et al, 2016. Binding of Tcf7l2 to each integrated phrase is measured by DamID [Vogel et al, 2007] using doxycycline-inducible ectopic expression of a genomically integrated fusion protein of Tcf7l2 and the N126A mutant of Dam, which we have recently shown to allow accurate measurement of Tcf7l2 binding genome-wide with reduced off-target methylation as compared to the wild-type Dam enzyme [Szczesnik et al, 2019].…”

Section: Resultsmentioning

confidence: 99%

A high-throughput genome-integrated assay reveals spatial dependencies governing Tcf7l2 binding

Szczesnik

Chu

et al. 2020

Preprint

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Predicting where transcription factors bind in the genome from their in-vitro DNA binding affinity is confounded by the large number of possible interactions with nearby transcription factors. To characterise the binding logic for the Wnt effector transcription factor Tcf7l2, we have developed a high-throughput screening platform in which thousands of 99-bp synthesised DNA sequences are inserted into a specific genomic locus through CRISPR/Cas9-based homology-directed repair, followed by measurement of Tcf7l2 binding by DamID. Using this platform at two genomic loci in mouse embryonic stem cells, we show that while the binding of Tcf7l2 closely follows the in-vitro motif binding strength and is influenced by local chromatin accessibility, it is also strongly affected by the surrounding 99-bp of sequence. The presence of nearby Oct4 and Klf4 motifs promote Tcf7l2 binding, particularly in the adjacent ∼20 to 50-bp nearby and oscillating with a 10.8-bp phasing relative to these cofactor motifs, which matches the turn of a DNA helix. This novel high-throughput DamID assay provides a powerful platform to determine local DNA sequence grammars that causally influence transcription factor binding in controlled genomic contexts.3

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A synergistic DNA logic predicts genome-wide chromatin accessibility

Cited by 19 publications

References 37 publications

Inference of cell type specific regulatory networks on mammalian lineages

Inference of cell type specific regulatory networks on mammalian lineages

Impact of regulatory variation across human iPSCs and differentiated cells

A high-throughput genome-integrated assay reveals spatial dependencies governing Tcf7l2 binding

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