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Atlas of RNA sequencing profiles for normal human tissues
Comprehensive analysis of molecular pathology requires a collection of reference samples representing normal tissues from healthy donors. For the available limited collections of normal tissues from postmortal donors, there is a problem of data incompatibility, as different datasets generated using...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6478850/ https://www.ncbi.nlm.nih.gov/pubmed/31015567 http://dx.doi.org/10.1038/s41597-019-0043-4 |
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author | Suntsova, Maria Gaifullin, Nurshat Allina, Daria Reshetun, Alexey Li, Xinmin Mendeleeva, Larisa Surin, Vadim Sergeeva, Anna Spirin, Pavel Prassolov, Vladimir Morgan, Alexander Garazha, Andrew Sorokin, Maxim Buzdin, Anton |
author_facet | Suntsova, Maria Gaifullin, Nurshat Allina, Daria Reshetun, Alexey Li, Xinmin Mendeleeva, Larisa Surin, Vadim Sergeeva, Anna Spirin, Pavel Prassolov, Vladimir Morgan, Alexander Garazha, Andrew Sorokin, Maxim Buzdin, Anton |
author_sort | Suntsova, Maria |
collection | PubMed |
description | Comprehensive analysis of molecular pathology requires a collection of reference samples representing normal tissues from healthy donors. For the available limited collections of normal tissues from postmortal donors, there is a problem of data incompatibility, as different datasets generated using different experimental platforms often cannot be merged in a single panel. Here, we constructed and deposited the gene expression database of normal human tissues based on uniformly screened original sequencing data. In total, 142 solid tissue samples representing 20 organs were taken from post-mortal human healthy donors of different age killed in road accidents no later than 36 hours after death. Blood samples were taken from 17 healthy volunteers. We then compared them with the 758 transcriptomic profiles taken from the other databases. We found that overall 463 biosamples showed tissue-specific rather than platform- or database-specific clustering and could be aggregated in a single database termed Oncobox Atlas of Normal Tissue Expression (ANTE). Our data will be useful to all those working with the analysis of human gene expression. |
format | Online Article Text |
id | pubmed-6478850 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-64788502019-04-24 Atlas of RNA sequencing profiles for normal human tissues Suntsova, Maria Gaifullin, Nurshat Allina, Daria Reshetun, Alexey Li, Xinmin Mendeleeva, Larisa Surin, Vadim Sergeeva, Anna Spirin, Pavel Prassolov, Vladimir Morgan, Alexander Garazha, Andrew Sorokin, Maxim Buzdin, Anton Sci Data Data Descriptor Comprehensive analysis of molecular pathology requires a collection of reference samples representing normal tissues from healthy donors. For the available limited collections of normal tissues from postmortal donors, there is a problem of data incompatibility, as different datasets generated using different experimental platforms often cannot be merged in a single panel. Here, we constructed and deposited the gene expression database of normal human tissues based on uniformly screened original sequencing data. In total, 142 solid tissue samples representing 20 organs were taken from post-mortal human healthy donors of different age killed in road accidents no later than 36 hours after death. Blood samples were taken from 17 healthy volunteers. We then compared them with the 758 transcriptomic profiles taken from the other databases. We found that overall 463 biosamples showed tissue-specific rather than platform- or database-specific clustering and could be aggregated in a single database termed Oncobox Atlas of Normal Tissue Expression (ANTE). Our data will be useful to all those working with the analysis of human gene expression. Nature Publishing Group UK 2019-04-23 /pmc/articles/PMC6478850/ /pubmed/31015567 http://dx.doi.org/10.1038/s41597-019-0043-4 Text en © The Author(s) 2019 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 Suntsova, Maria Gaifullin, Nurshat Allina, Daria Reshetun, Alexey Li, Xinmin Mendeleeva, Larisa Surin, Vadim Sergeeva, Anna Spirin, Pavel Prassolov, Vladimir Morgan, Alexander Garazha, Andrew Sorokin, Maxim Buzdin, Anton Atlas of RNA sequencing profiles for normal human tissues |
title | Atlas of RNA sequencing profiles for normal human tissues |
title_full | Atlas of RNA sequencing profiles for normal human tissues |
title_fullStr | Atlas of RNA sequencing profiles for normal human tissues |
title_full_unstemmed | Atlas of RNA sequencing profiles for normal human tissues |
title_short | Atlas of RNA sequencing profiles for normal human tissues |
title_sort | atlas of rna sequencing profiles for normal human tissues |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6478850/ https://www.ncbi.nlm.nih.gov/pubmed/31015567 http://dx.doi.org/10.1038/s41597-019-0043-4 |
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