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RNA proximity sequencing data and analysis pipeline from a human neuroblastoma nuclear transcriptome
We have previously developed and described a method for measuring RNA co-locations within cells, called Proximity RNA-seq, which promises insights into RNA expression, processing, storage and translation. Here, we describe transcriptome-wide proximity RNA-seq datasets obtained from human neuroblasto...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6987088/ https://www.ncbi.nlm.nih.gov/pubmed/31992717 http://dx.doi.org/10.1038/s41597-020-0372-3 |
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author | Wingett, Steven W. Andrews, Simon Fraser, Peter Morf, Jörg |
author_facet | Wingett, Steven W. Andrews, Simon Fraser, Peter Morf, Jörg |
author_sort | Wingett, Steven W. |
collection | PubMed |
description | We have previously developed and described a method for measuring RNA co-locations within cells, called Proximity RNA-seq, which promises insights into RNA expression, processing, storage and translation. Here, we describe transcriptome-wide proximity RNA-seq datasets obtained from human neuroblastoma SH-SY5Y cell nuclei. To aid future users of this method, we also describe and release our analysis pipeline, CloseCall, which maps cDNA to a custom transcript annotation and allocates cDNA-linked barcodes to barcode groups. CloseCall then performs Monte Carlo simulations on the data to identify pairs of transcripts, which are co-barcoded more frequently than expected by chance. Furthermore, derived co-barcoding frequencies for individual transcripts, dubbed valency, serve as proxies for RNA density or connectivity for that given transcript. We outline how this pipeline was applied to these sequencing datasets and openly share the processed data outputs and access to a virtual machine that runs CloseCall. The resulting data specify the spatial organization of RNAs and builds hypotheses for potential regulatory relationships between RNAs. |
format | Online Article Text |
id | pubmed-6987088 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-69870882020-02-05 RNA proximity sequencing data and analysis pipeline from a human neuroblastoma nuclear transcriptome Wingett, Steven W. Andrews, Simon Fraser, Peter Morf, Jörg Sci Data Data Descriptor We have previously developed and described a method for measuring RNA co-locations within cells, called Proximity RNA-seq, which promises insights into RNA expression, processing, storage and translation. Here, we describe transcriptome-wide proximity RNA-seq datasets obtained from human neuroblastoma SH-SY5Y cell nuclei. To aid future users of this method, we also describe and release our analysis pipeline, CloseCall, which maps cDNA to a custom transcript annotation and allocates cDNA-linked barcodes to barcode groups. CloseCall then performs Monte Carlo simulations on the data to identify pairs of transcripts, which are co-barcoded more frequently than expected by chance. Furthermore, derived co-barcoding frequencies for individual transcripts, dubbed valency, serve as proxies for RNA density or connectivity for that given transcript. We outline how this pipeline was applied to these sequencing datasets and openly share the processed data outputs and access to a virtual machine that runs CloseCall. The resulting data specify the spatial organization of RNAs and builds hypotheses for potential regulatory relationships between RNAs. Nature Publishing Group UK 2020-01-28 /pmc/articles/PMC6987088/ /pubmed/31992717 http://dx.doi.org/10.1038/s41597-020-0372-3 Text en © The Author(s) 2020 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/. The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files associated with this article. |
spellingShingle | Data Descriptor Wingett, Steven W. Andrews, Simon Fraser, Peter Morf, Jörg RNA proximity sequencing data and analysis pipeline from a human neuroblastoma nuclear transcriptome |
title | RNA proximity sequencing data and analysis pipeline from a human neuroblastoma nuclear transcriptome |
title_full | RNA proximity sequencing data and analysis pipeline from a human neuroblastoma nuclear transcriptome |
title_fullStr | RNA proximity sequencing data and analysis pipeline from a human neuroblastoma nuclear transcriptome |
title_full_unstemmed | RNA proximity sequencing data and analysis pipeline from a human neuroblastoma nuclear transcriptome |
title_short | RNA proximity sequencing data and analysis pipeline from a human neuroblastoma nuclear transcriptome |
title_sort | rna proximity sequencing data and analysis pipeline from a human neuroblastoma nuclear transcriptome |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6987088/ https://www.ncbi.nlm.nih.gov/pubmed/31992717 http://dx.doi.org/10.1038/s41597-020-0372-3 |
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