1/18/2024 0 Comments Benchmark scenic construction![]() The SCENIC+ motif collection includes 34,524 unique motifs gathered from 29 motif collections, which were clustered with a two-step strategy. d, Workflow to create motif databases for SCENIC+. Promoter regions were excluded from the analysis. The AUC value is scaled by dividing by the maximum possible AUC at 10% of the ranking. c, Bar-plots showing the area under the recovery curve (AUC enhancer recovery) on the top 10% of the ranking based on STARR-seq signal, for the top 5,000 DARs identified by Signac, pycisTopic and ArchR and top 5,000 regions from the cell-line-specific topics identified by pycisTopic. b, Running-time comparison per topic model using cisTopic with Collapsed Gibbs Sampling or WarpLDA (blue) and pycisTopic with Collapsed Gibbs Sampling or MALLET (red) for parameter optimization. PWM, position weight matrix UCSC, University of California, Santa Cruz. SCENIC+ integrates region accessibility, TF and target gene expression and cistromes to infer eGRNs, in which TFs are linked to their target regions and these to their target genes. Topics and DARs inferred with pycisTopic are transformed into cistromes of directly bound regions by identifying modules that present significant enrichment of the regulator’s binding motif using pycisTarget. Here, we developed SCENIC+, a computational framework that combines single-cell chromatin accessibility and gene expression data with motif discovery to infer enhancer-driven GRNs (eGRNs).Ī, SCENIC+ workflow. In fact, genomic regions that are specifically accessible in a cell type often represent enhancers and are enriched for TFBS combinations 2, 14, 16, 17, 18. With single-cell chromatin-accessibility data, the accuracy of TFBS predictions can be improved substantially 15. For example, SCENIC combines single-cell RNA-sequencing (scRNA-seq) coexpression networks with TF motif discovery 11, 12, but it cannot identify the exact CRE targeted by the TF and it only uses a small proportion of a gene’s putative regulatory space 13, 14. Alternative approaches have recently been described that have increased cellular resolution (for example, single-cell CUT&Tag 6, nano-CT 7 and NTT-seq 8) or that rely on genetic tagging (for example, DamID 9 and nanoDam 10), yet such methods are still difficult to scale to all TFs.Ĭomputational modeling is an alternative for identifying TFBSs. In addition, for most TFs, high-quality antibodies are lacking. Nevertheless, for tissues with high cell-type diversity it remains challenging to map TFBSs because of the need for large amounts of homogenous cells. In-depth knowledge of GRNs is important for mechanistic understanding of biological aspects underlying development 1, 2, evolution 3, 4 and disease 5 however, knowledge of TF–target relationships at the cis-regulatory level is still limited.Įxperimental techniques, including chromatin immunoprecipitation and sequencing (ChIP-seq), have yielded a wealth of TF-binding datasets. ![]() CREs are often cell-type-specific and consist of specific TF-binding site (TFBS) combinations. Similar content being viewed by othersĬell identity is encoded by gene regulatory networks (GRNs), in which transcription factors (TFs) interact with sets of cis-regulatory elements (CREs) to control transcription of target genes. Finally, we use SCENIC+ to study the dynamics of gene regulation along differentiation trajectories and the effect of TF perturbations on cell state. Next, we exploit SCENIC+ predictions to study conserved TFs, enhancers and GRNs between human and mouse cell types in the cerebral cortex. We benchmarked SCENIC+ on diverse datasets from different species, including human peripheral blood mononuclear cells, ENCODE cell lines, melanoma cell states and Drosophila retinal development. ![]() To improve both recall and precision of TF identification, we curated and clustered a motif collection with more than 30,000 motifs. SCENIC+ predicts genomic enhancers along with candidate upstream transcription factors (TFs) and links these enhancers to candidate target genes. Here we present a method for the inference of enhancer-driven GRNs, called SCENIC+. Joint profiling of chromatin accessibility and gene expression in individual cells provides an opportunity to decipher enhancer-driven gene regulatory networks (GRNs).
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