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Cross-platform ultradeep transcriptomic profiling of human reference RNA samples by RNA-Seq
Whole-transcriptome sequencing (‘RNA-Seq’) has been drastically changing the scale and scope of genomic research. In order to fully understand the power and limitations of this technology, the US Food and Drug Administration (FDA) launched the third phase of the MicroArray Quality Control (MAQC-III)...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4322577/ https://www.ncbi.nlm.nih.gov/pubmed/25977777 http://dx.doi.org/10.1038/sdata.2014.20 |
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author | Xu, Joshua Su, Zhenqiang Hong, Huixiao Thierry-Mieg, Jean Thierry-Mieg, Danielle Kreil, David P. Mason, Christopher E. Tong, Weida Shi, Leming |
author_facet | Xu, Joshua Su, Zhenqiang Hong, Huixiao Thierry-Mieg, Jean Thierry-Mieg, Danielle Kreil, David P. Mason, Christopher E. Tong, Weida Shi, Leming |
author_sort | Xu, Joshua |
collection | PubMed |
description | Whole-transcriptome sequencing (‘RNA-Seq’) has been drastically changing the scale and scope of genomic research. In order to fully understand the power and limitations of this technology, the US Food and Drug Administration (FDA) launched the third phase of the MicroArray Quality Control (MAQC-III) project, also known as the SEquencing Quality Control (SEQC) project. Using two well-established human reference RNA samples from the first phase of the MAQC project, three sequencing platforms were tested across more than ten sites with built-in truths including spike-in of external RNA controls (ERCC), titration data and qPCR verification. The SEQC project generated over 30 billion sequence reads representing the largest RNA-Seq data ever generated by a single project on individual RNA samples. This extraordinarily ultradeep transcriptomic data set and the known truths built into the study design provide many opportunities for further research and development to advance the improvement and application of RNA-Seq. |
format | Online Article Text |
id | pubmed-4322577 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-43225772015-05-14 Cross-platform ultradeep transcriptomic profiling of human reference RNA samples by RNA-Seq Xu, Joshua Su, Zhenqiang Hong, Huixiao Thierry-Mieg, Jean Thierry-Mieg, Danielle Kreil, David P. Mason, Christopher E. Tong, Weida Shi, Leming Sci Data Data Descriptor Whole-transcriptome sequencing (‘RNA-Seq’) has been drastically changing the scale and scope of genomic research. In order to fully understand the power and limitations of this technology, the US Food and Drug Administration (FDA) launched the third phase of the MicroArray Quality Control (MAQC-III) project, also known as the SEquencing Quality Control (SEQC) project. Using two well-established human reference RNA samples from the first phase of the MAQC project, three sequencing platforms were tested across more than ten sites with built-in truths including spike-in of external RNA controls (ERCC), titration data and qPCR verification. The SEQC project generated over 30 billion sequence reads representing the largest RNA-Seq data ever generated by a single project on individual RNA samples. This extraordinarily ultradeep transcriptomic data set and the known truths built into the study design provide many opportunities for further research and development to advance the improvement and application of RNA-Seq. Nature Publishing Group 2014-08-26 /pmc/articles/PMC4322577/ /pubmed/25977777 http://dx.doi.org/10.1038/sdata.2014.20 Text en Copyright © 2014, Macmillan Publishers Limited http://creativecommons.org/licenses/by/4.0 This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0 Metadata associated with this Data Descriptor is available at http://www.nature.com/sdata/ and is released under the CC0 waiver to maximize reuse. |
spellingShingle | Data Descriptor Xu, Joshua Su, Zhenqiang Hong, Huixiao Thierry-Mieg, Jean Thierry-Mieg, Danielle Kreil, David P. Mason, Christopher E. Tong, Weida Shi, Leming Cross-platform ultradeep transcriptomic profiling of human reference RNA samples by RNA-Seq |
title | Cross-platform ultradeep transcriptomic profiling of human reference RNA samples by RNA-Seq |
title_full | Cross-platform ultradeep transcriptomic profiling of human reference RNA samples by RNA-Seq |
title_fullStr | Cross-platform ultradeep transcriptomic profiling of human reference RNA samples by RNA-Seq |
title_full_unstemmed | Cross-platform ultradeep transcriptomic profiling of human reference RNA samples by RNA-Seq |
title_short | Cross-platform ultradeep transcriptomic profiling of human reference RNA samples by RNA-Seq |
title_sort | cross-platform ultradeep transcriptomic profiling of human reference rna samples by rna-seq |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4322577/ https://www.ncbi.nlm.nih.gov/pubmed/25977777 http://dx.doi.org/10.1038/sdata.2014.20 |
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