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SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks
Joint profiling of chromatin accessibility and gene expression in individual cells provides an opportunity to decipher enhancer-driven gene regulatory networks (GRNs). Here we present a method for the inference of enhancer-driven GRNs, called SCENIC+. SCENIC+ predicts genomic enhancers along with ca...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group US
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10482700/ https://www.ncbi.nlm.nih.gov/pubmed/37443338 http://dx.doi.org/10.1038/s41592-023-01938-4 |
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author | Bravo González-Blas, Carmen De Winter, Seppe Hulselmans, Gert Hecker, Nikolai Matetovici, Irina Christiaens, Valerie Poovathingal, Suresh Wouters, Jasper Aibar, Sara Aerts, Stein |
author_facet | Bravo González-Blas, Carmen De Winter, Seppe Hulselmans, Gert Hecker, Nikolai Matetovici, Irina Christiaens, Valerie Poovathingal, Suresh Wouters, Jasper Aibar, Sara Aerts, Stein |
author_sort | Bravo González-Blas, Carmen |
collection | PubMed |
description | Joint profiling of chromatin accessibility and gene expression in individual cells provides an opportunity to decipher enhancer-driven gene regulatory networks (GRNs). Here we present a method for the inference of enhancer-driven GRNs, called SCENIC+. SCENIC+ predicts genomic enhancers along with candidate upstream transcription factors (TFs) and links these enhancers to candidate target genes. To improve both recall and precision of TF identification, we curated and clustered a motif collection with more than 30,000 motifs. 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. Next, we exploit SCENIC+ predictions to study conserved TFs, enhancers and GRNs between human and mouse cell types in the cerebral cortex. Finally, we use SCENIC+ to study the dynamics of gene regulation along differentiation trajectories and the effect of TF perturbations on cell state. SCENIC+ is available at scenicplus.readthedocs.io. |
format | Online Article Text |
id | pubmed-10482700 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group US |
record_format | MEDLINE/PubMed |
spelling | pubmed-104827002023-09-08 SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks Bravo González-Blas, Carmen De Winter, Seppe Hulselmans, Gert Hecker, Nikolai Matetovici, Irina Christiaens, Valerie Poovathingal, Suresh Wouters, Jasper Aibar, Sara Aerts, Stein Nat Methods Article Joint profiling of chromatin accessibility and gene expression in individual cells provides an opportunity to decipher enhancer-driven gene regulatory networks (GRNs). Here we present a method for the inference of enhancer-driven GRNs, called SCENIC+. SCENIC+ predicts genomic enhancers along with candidate upstream transcription factors (TFs) and links these enhancers to candidate target genes. To improve both recall and precision of TF identification, we curated and clustered a motif collection with more than 30,000 motifs. 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. Next, we exploit SCENIC+ predictions to study conserved TFs, enhancers and GRNs between human and mouse cell types in the cerebral cortex. Finally, we use SCENIC+ to study the dynamics of gene regulation along differentiation trajectories and the effect of TF perturbations on cell state. SCENIC+ is available at scenicplus.readthedocs.io. Nature Publishing Group US 2023-07-13 2023 /pmc/articles/PMC10482700/ /pubmed/37443338 http://dx.doi.org/10.1038/s41592-023-01938-4 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Bravo González-Blas, Carmen De Winter, Seppe Hulselmans, Gert Hecker, Nikolai Matetovici, Irina Christiaens, Valerie Poovathingal, Suresh Wouters, Jasper Aibar, Sara Aerts, Stein SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks |
title | SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks |
title_full | SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks |
title_fullStr | SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks |
title_full_unstemmed | SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks |
title_short | SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks |
title_sort | scenic+: single-cell multiomic inference of enhancers and gene regulatory networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10482700/ https://www.ncbi.nlm.nih.gov/pubmed/37443338 http://dx.doi.org/10.1038/s41592-023-01938-4 |
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