Characterizing chromatin landscape from aggregate and single-cell genomic assays using flexible duration modeling
Most currently available statistical tools for the analysis of ATAC-seq data were repurposed from tools developed for other functional genomics data (e.g. ChIP-seq). Here, Gabitto et al develop ChromA, a Bayesian statistical approach for the analysis of both bulk and single-cell ATAC-seq data.
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2020-02-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-020-14497-5 |
| _version_ | 1818427279857942528 |
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| author | Mariano I. Gabitto Anders Rasmussen Orly Wapinski Kathryn Allaway Nicholas Carriero Gordon J. Fishell Richard Bonneau |
| author_facet | Mariano I. Gabitto Anders Rasmussen Orly Wapinski Kathryn Allaway Nicholas Carriero Gordon J. Fishell Richard Bonneau |
| author_sort | Mariano I. Gabitto |
| collection | DOAJ |
| description | Most currently available statistical tools for the analysis of ATAC-seq data were repurposed from tools developed for other functional genomics data (e.g. ChIP-seq). Here, Gabitto et al develop ChromA, a Bayesian statistical approach for the analysis of both bulk and single-cell ATAC-seq data. |
| first_indexed | 2024-12-14T14:43:13Z |
| format | Article |
| id | doaj.art-ff875af4ced749c59fcbbc72569eff52 |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-12-14T14:43:13Z |
| publishDate | 2020-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-ff875af4ced749c59fcbbc72569eff522022-12-21T22:57:21ZengNature PortfolioNature Communications2041-17232020-02-0111111010.1038/s41467-020-14497-5Characterizing chromatin landscape from aggregate and single-cell genomic assays using flexible duration modelingMariano I. Gabitto0Anders Rasmussen1Orly Wapinski2Kathryn Allaway3Nicholas Carriero4Gordon J. Fishell5Richard Bonneau6Center for Computational Biology, Flatiron Institute, Simons FoundationCenter for Computational Biology, Flatiron Institute, Simons FoundationNew York University, Neuroscience Institute and the Department of Neuroscience and Physiology, Smilow Research CenterNew York University, Neuroscience Institute and the Department of Neuroscience and Physiology, Smilow Research CenterCenter for Computational Biology, Flatiron Institute, Simons FoundationNew York University, Neuroscience Institute and the Department of Neuroscience and Physiology, Smilow Research CenterCenter for Computational Biology, Flatiron Institute, Simons FoundationMost currently available statistical tools for the analysis of ATAC-seq data were repurposed from tools developed for other functional genomics data (e.g. ChIP-seq). Here, Gabitto et al develop ChromA, a Bayesian statistical approach for the analysis of both bulk and single-cell ATAC-seq data.https://doi.org/10.1038/s41467-020-14497-5 |
| spellingShingle | Mariano I. Gabitto Anders Rasmussen Orly Wapinski Kathryn Allaway Nicholas Carriero Gordon J. Fishell Richard Bonneau Characterizing chromatin landscape from aggregate and single-cell genomic assays using flexible duration modeling Nature Communications |
| title | Characterizing chromatin landscape from aggregate and single-cell genomic assays using flexible duration modeling |
| title_full | Characterizing chromatin landscape from aggregate and single-cell genomic assays using flexible duration modeling |
| title_fullStr | Characterizing chromatin landscape from aggregate and single-cell genomic assays using flexible duration modeling |
| title_full_unstemmed | Characterizing chromatin landscape from aggregate and single-cell genomic assays using flexible duration modeling |
| title_short | Characterizing chromatin landscape from aggregate and single-cell genomic assays using flexible duration modeling |
| title_sort | characterizing chromatin landscape from aggregate and single cell genomic assays using flexible duration modeling |
| url | https://doi.org/10.1038/s41467-020-14497-5 |
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