Comparison of RNA-Seq and microarray in transcriptome profiling of activated T cells.

To demonstrate the benefits of RNA-Seq over microarray in transcriptome profiling, both RNA-Seq and microarray analyses were performed on RNA samples from a human T cell activation experiment. In contrast to other reports, our analyses focused on the difference, rather than similarity, between RNA-S...

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Autores principales:	Shanrong Zhao, Wai-Ping Fung-Leung, Anton Bittner, Karen Ngo, Xuejun Liu
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/a7f05e3ab5884884aa9e0ad8adb8671b
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spelling	oai:doaj.org-article:a7f05e3ab5884884aa9e0ad8adb8671b2021-11-18T08:37:34ZComparison of RNA-Seq and microarray in transcriptome profiling of activated T cells.1932-620310.1371/journal.pone.0078644https://doaj.org/article/a7f05e3ab5884884aa9e0ad8adb8671b2014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24454679/?tool=EBIhttps://doaj.org/toc/1932-6203To demonstrate the benefits of RNA-Seq over microarray in transcriptome profiling, both RNA-Seq and microarray analyses were performed on RNA samples from a human T cell activation experiment. In contrast to other reports, our analyses focused on the difference, rather than similarity, between RNA-Seq and microarray technologies in transcriptome profiling. A comparison of data sets derived from RNA-Seq and Affymetrix platforms using the same set of samples showed a high correlation between gene expression profiles generated by the two platforms. However, it also demonstrated that RNA-Seq was superior in detecting low abundance transcripts, differentiating biologically critical isoforms, and allowing the identification of genetic variants. RNA-Seq also demonstrated a broader dynamic range than microarray, which allowed for the detection of more differentially expressed genes with higher fold-change. Analysis of the two datasets also showed the benefit derived from avoidance of technical issues inherent to microarray probe performance such as cross-hybridization, non-specific hybridization and limited detection range of individual probes. Because RNA-Seq does not rely on a pre-designed complement sequence detection probe, it is devoid of issues associated with probe redundancy and annotation, which simplified interpretation of the data. Despite the superior benefits of RNA-Seq, microarrays are still the more common choice of researchers when conducting transcriptional profiling experiments. This is likely because RNA-Seq sequencing technology is new to most researchers, more expensive than microarray, data storage is more challenging and analysis is more complex. We expect that once these barriers are overcome, the RNA-Seq platform will become the predominant tool for transcriptome analysis.Shanrong ZhaoWai-Ping Fung-LeungAnton BittnerKaren NgoXuejun LiuPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 1, p e78644 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Shanrong Zhao Wai-Ping Fung-Leung Anton Bittner Karen Ngo Xuejun Liu Comparison of RNA-Seq and microarray in transcriptome profiling of activated T cells.
description	To demonstrate the benefits of RNA-Seq over microarray in transcriptome profiling, both RNA-Seq and microarray analyses were performed on RNA samples from a human T cell activation experiment. In contrast to other reports, our analyses focused on the difference, rather than similarity, between RNA-Seq and microarray technologies in transcriptome profiling. A comparison of data sets derived from RNA-Seq and Affymetrix platforms using the same set of samples showed a high correlation between gene expression profiles generated by the two platforms. However, it also demonstrated that RNA-Seq was superior in detecting low abundance transcripts, differentiating biologically critical isoforms, and allowing the identification of genetic variants. RNA-Seq also demonstrated a broader dynamic range than microarray, which allowed for the detection of more differentially expressed genes with higher fold-change. Analysis of the two datasets also showed the benefit derived from avoidance of technical issues inherent to microarray probe performance such as cross-hybridization, non-specific hybridization and limited detection range of individual probes. Because RNA-Seq does not rely on a pre-designed complement sequence detection probe, it is devoid of issues associated with probe redundancy and annotation, which simplified interpretation of the data. Despite the superior benefits of RNA-Seq, microarrays are still the more common choice of researchers when conducting transcriptional profiling experiments. This is likely because RNA-Seq sequencing technology is new to most researchers, more expensive than microarray, data storage is more challenging and analysis is more complex. We expect that once these barriers are overcome, the RNA-Seq platform will become the predominant tool for transcriptome analysis.
format	article
author	Shanrong Zhao Wai-Ping Fung-Leung Anton Bittner Karen Ngo Xuejun Liu
author_facet	Shanrong Zhao Wai-Ping Fung-Leung Anton Bittner Karen Ngo Xuejun Liu
author_sort	Shanrong Zhao
title	Comparison of RNA-Seq and microarray in transcriptome profiling of activated T cells.
title_short	Comparison of RNA-Seq and microarray in transcriptome profiling of activated T cells.
title_full	Comparison of RNA-Seq and microarray in transcriptome profiling of activated T cells.
title_fullStr	Comparison of RNA-Seq and microarray in transcriptome profiling of activated T cells.
title_full_unstemmed	Comparison of RNA-Seq and microarray in transcriptome profiling of activated T cells.
title_sort	comparison of rna-seq and microarray in transcriptome profiling of activated t cells.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/a7f05e3ab5884884aa9e0ad8adb8671b
work_keys_str_mv	AT shanrongzhao comparisonofrnaseqandmicroarrayintranscriptomeprofilingofactivatedtcells AT waipingfungleung comparisonofrnaseqandmicroarrayintranscriptomeprofilingofactivatedtcells AT antonbittner comparisonofrnaseqandmicroarrayintranscriptomeprofilingofactivatedtcells AT karenngo comparisonofrnaseqandmicroarrayintranscriptomeprofilingofactivatedtcells AT xuejunliu comparisonofrnaseqandmicroarrayintranscriptomeprofilingofactivatedtcells
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Comparison of RNA-Seq and microarray in transcriptome profiling of activated T cells.

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