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Series GSE210745 Query DataSets for GSE210745
Status Public on Apr 18, 2023
Title SCENIC+: identification of enhancers and gene regulatory networks using single-cell multiomics (cell lines)
Organism Homo sapiens
Experiment type Genome binding/occupancy profiling by high throughput sequencing
Summary Joint profiling of chromatin accessibility and gene expression of individual cells provides an opportunity to decipher enhancer-driven gene regulatory networks (eGRN). Here we present a new method for the inference of eGRNs, called SCENIC+. SCENIC+ predicts genomic enhancers along with candidate upstream transcription factors (TF) and links these enhancers to candidate target genes. Specific TFs for each cell type or cell state are predicted based on the concordance of TF binding site accessibility, TF expression, and target gene expression. To improve both recall and precision of TF identification, we curated and clustered more than 40,000 position weight matrices that we could associate with ~1,500 human TFs. We validated and benchmarked each of the SCENIC+ components on diverse data sets from different species, including human peripheral blood mononuclear cell types, human ENCODE cell lines, human melanoma cell states, and Drosophila retinal development. Next, we exploit SCENIC+ predictions to study conserved TFs, enhancers, and GRNs between human and mouse cell types in the cerebral cortex. Finally, we provide new capabilities that exploit the inferred eGRNs to study the dynamics of gene regulation along differentiation trajectories; to map regulatory activities onto tissues using spatial omics data; and to predict the effect of TF perturbations on cell state. SCENIC+ provides critical insight into gene regulation, starting from multi-ome atlases of scATAC-seq and scRNA-seq. The SCENIC+ suite is available as a set of Python modules at scenicplus.readthedocs.io.
 
Overall design A mix of 11 wild type melanoma cell lines (MM050, MM099, MM116, MM001, MM011, MM057, MM087, MM031, MM047 and MM029) were analysed using 10x scATAC-seq experiments
 
Contributor(s) González-Blas CB, De Winter S, Aerts S
Citation(s) 37443338
Submission date Aug 08, 2022
Last update date Jul 18, 2023
Contact name Gert Hulselmans
E-mail(s) [email protected]
Organization name VIB
Department Center for Brain and Disease Research
Lab Laboratory of Computational Biology
Street address Herestraat 49 PO Box 602
City Leuven
ZIP/Postal code 3000
Country Belgium
 
Platforms (2)
GPL24676 Illumina NovaSeq 6000 (Homo sapiens)
GPL30173 NextSeq 2000 (Homo sapiens)
Samples (2)
GSM6436448 10x scATAC-seq on mix of MM031, MM074, MM047 and MM029
GSM6436449 10x scATAC-seq on mix of MM050, MM099, MM116, MM001, MM011, MM057 and MM087
This SubSeries is part of SuperSeries:
GSE210749 SCENIC+: identification of enhancers and gene regulatory networks using single-cell multiomics
Relations
BioProject PRJNA867358

Download family Format
SOFT formatted family file(s) SOFTHelp
MINiML formatted family file(s) MINiMLHelp
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Supplementary file Size Download File type/resource
GSE210745_CRM_34940b_fragments.tsv.gz 764.8 Mb (ftp)(http) TSV
GSE210745_NML_a56c4b_fragments.tsv.gz 549.1 Mb (ftp)(http) TSV
GSE210745_mm_lines_cell_metadata.tsv.gz 1.9 Kb (ftp)(http) TSV
GSE210745_mm_lines_fragment_counts.tsv.gz 71.0 Mb (ftp)(http) TSV
GSE210745_mm_lines_gene_expression_matrix.tsv.gz 8.0 Mb (ftp)(http) TSV
GSE210745_mm_lines_region_accessibility_matrix.tsv.gz 83.7 Mb (ftp)(http) TSV
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