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DIRECT-NET: An efficient method to discover cis-regulatory elements and construct regulatory networks from single-cell multiomics data

The emergence of single-cell multiomics data provides unprecedented opportunities to scrutinize the transcriptional regulatory mechanisms controlling cell identity. However, how to use those datasets to dissect the cis-regulatory element (CRE)–to–gene relationships at a single-cell level remains a m...

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Detalles Bibliográficos
Autores principales: Zhang, Lihua, Zhang, Jing, Nie, Qing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Association for the Advancement of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9159696/
https://www.ncbi.nlm.nih.gov/pubmed/35648859
http://dx.doi.org/10.1126/sciadv.abl7393
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author Zhang, Lihua
Zhang, Jing
Nie, Qing
author_facet Zhang, Lihua
Zhang, Jing
Nie, Qing
author_sort Zhang, Lihua
collection PubMed
description The emergence of single-cell multiomics data provides unprecedented opportunities to scrutinize the transcriptional regulatory mechanisms controlling cell identity. However, how to use those datasets to dissect the cis-regulatory element (CRE)–to–gene relationships at a single-cell level remains a major challenge. Here, we present DIRECT-NET, a machine-learning method based on gradient boosting, to identify genome-wide CREs and their relationship to target genes, either from parallel single-cell gene expression and chromatin accessibility data or from single-cell chromatin accessibility data alone. By extensively evaluating and characterizing DIRECT-NET’s predicted CREs using independent functional genomics data, we find that DIRECT-NET substantially improves the accuracy of inferring CRE-to-gene relationships in comparison to existing methods. DIRECT-NET is also capable of revealing cell subpopulation–specific and dynamic regulatory linkages. Overall, DIRECT-NET provides an efficient tool for predicting transcriptional regulation codes from single-cell multiomics data.
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spelling pubmed-91596962022-06-16 DIRECT-NET: An efficient method to discover cis-regulatory elements and construct regulatory networks from single-cell multiomics data Zhang, Lihua Zhang, Jing Nie, Qing Sci Adv Biomedicine and Life Sciences The emergence of single-cell multiomics data provides unprecedented opportunities to scrutinize the transcriptional regulatory mechanisms controlling cell identity. However, how to use those datasets to dissect the cis-regulatory element (CRE)–to–gene relationships at a single-cell level remains a major challenge. Here, we present DIRECT-NET, a machine-learning method based on gradient boosting, to identify genome-wide CREs and their relationship to target genes, either from parallel single-cell gene expression and chromatin accessibility data or from single-cell chromatin accessibility data alone. By extensively evaluating and characterizing DIRECT-NET’s predicted CREs using independent functional genomics data, we find that DIRECT-NET substantially improves the accuracy of inferring CRE-to-gene relationships in comparison to existing methods. DIRECT-NET is also capable of revealing cell subpopulation–specific and dynamic regulatory linkages. Overall, DIRECT-NET provides an efficient tool for predicting transcriptional regulation codes from single-cell multiomics data. American Association for the Advancement of Science 2022-06-01 /pmc/articles/PMC9159696/ /pubmed/35648859 http://dx.doi.org/10.1126/sciadv.abl7393 Text en Copyright © 2022 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC). https://creativecommons.org/licenses/by-nc/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license (https://creativecommons.org/licenses/by-nc/4.0/) , which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.
spellingShingle Biomedicine and Life Sciences
Zhang, Lihua
Zhang, Jing
Nie, Qing
DIRECT-NET: An efficient method to discover cis-regulatory elements and construct regulatory networks from single-cell multiomics data
title DIRECT-NET: An efficient method to discover cis-regulatory elements and construct regulatory networks from single-cell multiomics data
title_full DIRECT-NET: An efficient method to discover cis-regulatory elements and construct regulatory networks from single-cell multiomics data
title_fullStr DIRECT-NET: An efficient method to discover cis-regulatory elements and construct regulatory networks from single-cell multiomics data
title_full_unstemmed DIRECT-NET: An efficient method to discover cis-regulatory elements and construct regulatory networks from single-cell multiomics data
title_short DIRECT-NET: An efficient method to discover cis-regulatory elements and construct regulatory networks from single-cell multiomics data
title_sort direct-net: an efficient method to discover cis-regulatory elements and construct regulatory networks from single-cell multiomics data
topic Biomedicine and Life Sciences
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9159696/
https://www.ncbi.nlm.nih.gov/pubmed/35648859
http://dx.doi.org/10.1126/sciadv.abl7393
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