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RNA Sequencing of Formalin-Fixed, Paraffin-Embedded Specimens for Gene Expression Quantification and Data Mining

Background. Proper rRNA depletion is crucial for the successful utilization of FFPE specimens when studying gene expression. We performed a study to evaluate two major rRNA depletion methods: Ribo-Zero and RNase H. RNAs extracted from 4 samples were treated with the two rRNA depletion methods in dup...

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Autores principales: Guo, Yan, Wu, Jie, Zhao, Shilin, Ye, Fei, Su, Yinghao, Clark, Travis, Sheng, Quanhu, Lehmann, Brian, Shu, Xiao-ou, Cai, Qiuyin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5059559/
https://www.ncbi.nlm.nih.gov/pubmed/27774452
http://dx.doi.org/10.1155/2016/9837310
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author Guo, Yan
Wu, Jie
Zhao, Shilin
Ye, Fei
Su, Yinghao
Clark, Travis
Sheng, Quanhu
Lehmann, Brian
Shu, Xiao-ou
Cai, Qiuyin
author_facet Guo, Yan
Wu, Jie
Zhao, Shilin
Ye, Fei
Su, Yinghao
Clark, Travis
Sheng, Quanhu
Lehmann, Brian
Shu, Xiao-ou
Cai, Qiuyin
author_sort Guo, Yan
collection PubMed
description Background. Proper rRNA depletion is crucial for the successful utilization of FFPE specimens when studying gene expression. We performed a study to evaluate two major rRNA depletion methods: Ribo-Zero and RNase H. RNAs extracted from 4 samples were treated with the two rRNA depletion methods in duplicate and sequenced (N = 16). We evaluated their reducibility, ability to detect RNA, and ability to molecularly subtype these triple negative breast cancer specimens. Results. Both rRNA depletion methods produced consistent data between the technical replicates. We found that the RNase H method produced higher quality RNAseq data as compared to the Ribo-Zero method. In addition, we evaluated the RNAseq data generated from the FFPE tissue samples for noncoding RNA, including lncRNA, enhancer/super enhancer RNA, and single nucleotide variation (SNV). We found that the RNase H is more suitable for detecting high-quality, noncoding RNAs as compared to the Ribo-Zero and provided more consistent molecular subtype identification between replicates. Unfortunately, neither method produced reliable SNV data. Conclusions. In conclusion, for FFPE specimens, the RNase H rRNA depletion method performed better than the Ribo-Zero. Neither method generates data sufficient for SNV detection.
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spelling pubmed-50595592016-10-23 RNA Sequencing of Formalin-Fixed, Paraffin-Embedded Specimens for Gene Expression Quantification and Data Mining Guo, Yan Wu, Jie Zhao, Shilin Ye, Fei Su, Yinghao Clark, Travis Sheng, Quanhu Lehmann, Brian Shu, Xiao-ou Cai, Qiuyin Int J Genomics Research Article Background. Proper rRNA depletion is crucial for the successful utilization of FFPE specimens when studying gene expression. We performed a study to evaluate two major rRNA depletion methods: Ribo-Zero and RNase H. RNAs extracted from 4 samples were treated with the two rRNA depletion methods in duplicate and sequenced (N = 16). We evaluated their reducibility, ability to detect RNA, and ability to molecularly subtype these triple negative breast cancer specimens. Results. Both rRNA depletion methods produced consistent data between the technical replicates. We found that the RNase H method produced higher quality RNAseq data as compared to the Ribo-Zero method. In addition, we evaluated the RNAseq data generated from the FFPE tissue samples for noncoding RNA, including lncRNA, enhancer/super enhancer RNA, and single nucleotide variation (SNV). We found that the RNase H is more suitable for detecting high-quality, noncoding RNAs as compared to the Ribo-Zero and provided more consistent molecular subtype identification between replicates. Unfortunately, neither method produced reliable SNV data. Conclusions. In conclusion, for FFPE specimens, the RNase H rRNA depletion method performed better than the Ribo-Zero. Neither method generates data sufficient for SNV detection. Hindawi Publishing Corporation 2016 2016-09-28 /pmc/articles/PMC5059559/ /pubmed/27774452 http://dx.doi.org/10.1155/2016/9837310 Text en Copyright © 2016 Yan Guo et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Guo, Yan
Wu, Jie
Zhao, Shilin
Ye, Fei
Su, Yinghao
Clark, Travis
Sheng, Quanhu
Lehmann, Brian
Shu, Xiao-ou
Cai, Qiuyin
RNA Sequencing of Formalin-Fixed, Paraffin-Embedded Specimens for Gene Expression Quantification and Data Mining
title RNA Sequencing of Formalin-Fixed, Paraffin-Embedded Specimens for Gene Expression Quantification and Data Mining
title_full RNA Sequencing of Formalin-Fixed, Paraffin-Embedded Specimens for Gene Expression Quantification and Data Mining
title_fullStr RNA Sequencing of Formalin-Fixed, Paraffin-Embedded Specimens for Gene Expression Quantification and Data Mining
title_full_unstemmed RNA Sequencing of Formalin-Fixed, Paraffin-Embedded Specimens for Gene Expression Quantification and Data Mining
title_short RNA Sequencing of Formalin-Fixed, Paraffin-Embedded Specimens for Gene Expression Quantification and Data Mining
title_sort rna sequencing of formalin-fixed, paraffin-embedded specimens for gene expression quantification and data mining
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5059559/
https://www.ncbi.nlm.nih.gov/pubmed/27774452
http://dx.doi.org/10.1155/2016/9837310
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