Sequence and chromatin determinants of cell-type–specific transcription factor binding

By Aaron Arvey, Phaedra Agius, William Stafford Noble, Christina S. Leslie. 2012

229 citations recorded by the source corpus.

Gene regulatory programs in distinct cell types are maintained in large part through the cell-type–specific binding of transcription factors (TFs). The determinants of TF binding include direct DNA sequence preferences, DNA sequence preferences of cofactors, and the local cell-dependent chromatin context. To explore the contribution of DNA sequence signal, histone modifications, and DNase accessibility to cell-type–specific binding, we analyzed 286 ChIP-seq experiments performed by the ENCODE Consortium. This analysis included experiments for 67 transcriptional regulators, 15 of which were profiled in both the GM12878 (lymphoblastoid) and K562 (erythroleukemic) human hematopoietic cell lines. To model TF-bound regions, we trained support vector machines (SVMs) that use flexible k-mer patterns to capture DNA sequence signals more accurately than traditional motif approaches. In addition, we trained SVM spatial chromatin signatures to model local histone modifications and DNase accessibility, obtaining significantly more accurate TF occupancy predictions than simpler approaches. Consistent with previous studies, we find that DNase accessibility can explain cell-line–specific binding for many factors. However, we also find that of the 10 factors with prominent cell-type–specific binding patterns, four display distinct cell-type–specific DNA sequence preferences according to our models. Moreover, for two factors we identify cell-specific binding sites that are accessible in both cell types but bound only in one. For these sites, cell-type–specific sequence models, rather than DNase accessibility, are better able to explain differential binding. Our results suggest that using a single motif for each TF and filtering for chromatin accessible loci is not always sufficient to accurately account for cell-type–specific binding profiles.

Subfields: Molecular Biology

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Citations in the corpus

Learning from mistakes: Accurate prediction of cell type-specific transcription factor binding (2017, methodology): “…like Factorbook (Wang et al. , 2012), the ENCODE-71 motif collection (Kheradpour and Kellis, 2014), or Homer (Heinz et al. , 2010), while some perform de-novo motif discovery based on k-mers (Arvey et al. , 2012) or as part of convolutional neural networks (Quang and Xie, 2017; Qin and Feng, 2017).”
SeqGL Identifies Context-Dependent Binding Signals in Genome-Wide Regulatory Element Maps (2015, methodology): “In our previous di-mismatch work [14], we showed that for some TFs, discriminative k-mer models indeed capture cell-type specific TF binding preferences, which we were able to interpret as variants in the binding model of the ChIP-ed TF.”
Enhanced Regulatory Sequence Prediction Using Gapped k-mer Features (2014, methodology): “Mismatch kernel [8], Wildcard kernel [9], and Di-mismatch kernel [10,11], using the mouse forebrain EP300 data set.”
Transfer String Kernel for Cross-Context DNA-Protein Binding Prediction (2016, background): “contexts should have similar gene regulation processes and similar TFBS sequence patterns [6].”
Discovery of cell-type specific DNA motif grammar in cis-regulatory elements using random Forest (2018, background): “Several published studies have used a similar machine learning approach to explore cell-type specificity, including a SVM classification model using k-mer sequences in TF ChIP-seq peaks, histone modifications and DNase accessibility data as features to train and predict cell-type specific TF binding”