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A comprehensive genomic and transcriptomic dataset of triple-negative breast cancers

Molecular subtyping of triple-negative breast cancer (TNBC) is essential for understanding the mechanisms and discovering actionable targets of this highly heterogeneous type of breast cancer. We previously performed a large single-center and multiomics study consisting of genomics, transcriptomics,...

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Autores principales: Chen, Qingwang, Liu, Yaqing, Gao, Yuechen, Zhang, Ruolan, Hou, Wanwan, Cao, Zehui, Jiang, Yi-Zhou, Zheng, Yuanting, Shi, Leming, Ma, Ding, Yang, Jingcheng, Shao, Zhi-Ming, Yu, Ying
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9509351/
https://www.ncbi.nlm.nih.gov/pubmed/36153392
http://dx.doi.org/10.1038/s41597-022-01681-z
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author Chen, Qingwang
Liu, Yaqing
Gao, Yuechen
Zhang, Ruolan
Hou, Wanwan
Cao, Zehui
Jiang, Yi-Zhou
Zheng, Yuanting
Shi, Leming
Ma, Ding
Yang, Jingcheng
Shao, Zhi-Ming
Yu, Ying
author_facet Chen, Qingwang
Liu, Yaqing
Gao, Yuechen
Zhang, Ruolan
Hou, Wanwan
Cao, Zehui
Jiang, Yi-Zhou
Zheng, Yuanting
Shi, Leming
Ma, Ding
Yang, Jingcheng
Shao, Zhi-Ming
Yu, Ying
author_sort Chen, Qingwang
collection PubMed
description Molecular subtyping of triple-negative breast cancer (TNBC) is essential for understanding the mechanisms and discovering actionable targets of this highly heterogeneous type of breast cancer. We previously performed a large single-center and multiomics study consisting of genomics, transcriptomics, and clinical information from 465 patients with primary TNBC. To facilitate reusing this unique dataset, we provided a detailed description of the dataset with special attention to data quality in this study. The multiomics data were generally of high quality, but a few sequencing data had quality issues and should be noted in subsequent data reuse. Furthermore, we reconduct data analyses with updated pipelines and the updated version of the human reference genome from hg19 to hg38. The updated profiles were in good concordance with those previously published in terms of gene quantification, variant calling, and copy number alteration. Additionally, we developed a user-friendly web-based database for convenient access and interactive exploration of the dataset. Our work will facilitate reusing the dataset, maximize the values of data and further accelerate cancer research.
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spelling pubmed-95093512022-09-26 A comprehensive genomic and transcriptomic dataset of triple-negative breast cancers Chen, Qingwang Liu, Yaqing Gao, Yuechen Zhang, Ruolan Hou, Wanwan Cao, Zehui Jiang, Yi-Zhou Zheng, Yuanting Shi, Leming Ma, Ding Yang, Jingcheng Shao, Zhi-Ming Yu, Ying Sci Data Data Descriptor Molecular subtyping of triple-negative breast cancer (TNBC) is essential for understanding the mechanisms and discovering actionable targets of this highly heterogeneous type of breast cancer. We previously performed a large single-center and multiomics study consisting of genomics, transcriptomics, and clinical information from 465 patients with primary TNBC. To facilitate reusing this unique dataset, we provided a detailed description of the dataset with special attention to data quality in this study. The multiomics data were generally of high quality, but a few sequencing data had quality issues and should be noted in subsequent data reuse. Furthermore, we reconduct data analyses with updated pipelines and the updated version of the human reference genome from hg19 to hg38. The updated profiles were in good concordance with those previously published in terms of gene quantification, variant calling, and copy number alteration. Additionally, we developed a user-friendly web-based database for convenient access and interactive exploration of the dataset. Our work will facilitate reusing the dataset, maximize the values of data and further accelerate cancer research. Nature Publishing Group UK 2022-09-24 /pmc/articles/PMC9509351/ /pubmed/36153392 http://dx.doi.org/10.1038/s41597-022-01681-z Text en © The Author(s) 2022 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 Data Descriptor
Chen, Qingwang
Liu, Yaqing
Gao, Yuechen
Zhang, Ruolan
Hou, Wanwan
Cao, Zehui
Jiang, Yi-Zhou
Zheng, Yuanting
Shi, Leming
Ma, Ding
Yang, Jingcheng
Shao, Zhi-Ming
Yu, Ying
A comprehensive genomic and transcriptomic dataset of triple-negative breast cancers
title A comprehensive genomic and transcriptomic dataset of triple-negative breast cancers
title_full A comprehensive genomic and transcriptomic dataset of triple-negative breast cancers
title_fullStr A comprehensive genomic and transcriptomic dataset of triple-negative breast cancers
title_full_unstemmed A comprehensive genomic and transcriptomic dataset of triple-negative breast cancers
title_short A comprehensive genomic and transcriptomic dataset of triple-negative breast cancers
title_sort comprehensive genomic and transcriptomic dataset of triple-negative breast cancers
topic Data Descriptor
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9509351/
https://www.ncbi.nlm.nih.gov/pubmed/36153392
http://dx.doi.org/10.1038/s41597-022-01681-z
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