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RNA sequencing and transcriptome arrays analyses show opposing results for alternative splicing in patient derived samples
BACKGROUND: RNA sequencing (RNA-seq) and microarrays are two transcriptomics techniques aimed at the quantification of transcribed genes and their isoforms. Here we compare the latest Affymetrix HTA 2.0 microarray with Illumina 2000 RNA-seq for the analysis of patient samples - normal lung epitheliu...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5461714/ https://www.ncbi.nlm.nih.gov/pubmed/28587590 http://dx.doi.org/10.1186/s12864-017-3819-y |
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author | Nazarov, Petr V. Muller, Arnaud Kaoma, Tony Nicot, Nathalie Maximo, Cristina Birembaut, Philippe Tran, Nhan L. Dittmar, Gunnar Vallar, Laurent |
author_facet | Nazarov, Petr V. Muller, Arnaud Kaoma, Tony Nicot, Nathalie Maximo, Cristina Birembaut, Philippe Tran, Nhan L. Dittmar, Gunnar Vallar, Laurent |
author_sort | Nazarov, Petr V. |
collection | PubMed |
description | BACKGROUND: RNA sequencing (RNA-seq) and microarrays are two transcriptomics techniques aimed at the quantification of transcribed genes and their isoforms. Here we compare the latest Affymetrix HTA 2.0 microarray with Illumina 2000 RNA-seq for the analysis of patient samples - normal lung epithelium tissue and squamous cell carcinoma lung tumours. Protein coding mRNAs and long non-coding RNAs (lncRNAs) were included in the study. RESULTS: Both platforms performed equally well for protein-coding RNAs, however the stochastic variability was higher for the sequencing data than for microarrays. This reduced the number of differentially expressed genes and genes with predictive potential for RNA-seq compared to microarray data. Analysis of this variability revealed a lack of reads for short and low abundant genes; lncRNAs, being shorter and less abundant RNAs, were found especially susceptible to this issue. A major difference between the two platforms was uncovered by analysis of alternatively spliced genes. Investigation of differential exon abundance showed insufficient reads for many exons and exon junctions in RNA-seq while the detection on the array platform was more stable. Nevertheless, we identified 207 genes which undergo alternative splicing and were consistently detected by both techniques. CONCLUSIONS: Despite the fact that the results of gene expression analysis were highly consistent between Human Transcriptome Arrays and RNA-seq platforms, the analysis of alternative splicing produced discordant results. We concluded that modern microarrays can still outperform sequencing for standard analysis of gene expression in terms of reproducibility and cost. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12864-017-3819-y) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5461714 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-54617142017-06-07 RNA sequencing and transcriptome arrays analyses show opposing results for alternative splicing in patient derived samples Nazarov, Petr V. Muller, Arnaud Kaoma, Tony Nicot, Nathalie Maximo, Cristina Birembaut, Philippe Tran, Nhan L. Dittmar, Gunnar Vallar, Laurent BMC Genomics Research Article BACKGROUND: RNA sequencing (RNA-seq) and microarrays are two transcriptomics techniques aimed at the quantification of transcribed genes and their isoforms. Here we compare the latest Affymetrix HTA 2.0 microarray with Illumina 2000 RNA-seq for the analysis of patient samples - normal lung epithelium tissue and squamous cell carcinoma lung tumours. Protein coding mRNAs and long non-coding RNAs (lncRNAs) were included in the study. RESULTS: Both platforms performed equally well for protein-coding RNAs, however the stochastic variability was higher for the sequencing data than for microarrays. This reduced the number of differentially expressed genes and genes with predictive potential for RNA-seq compared to microarray data. Analysis of this variability revealed a lack of reads for short and low abundant genes; lncRNAs, being shorter and less abundant RNAs, were found especially susceptible to this issue. A major difference between the two platforms was uncovered by analysis of alternatively spliced genes. Investigation of differential exon abundance showed insufficient reads for many exons and exon junctions in RNA-seq while the detection on the array platform was more stable. Nevertheless, we identified 207 genes which undergo alternative splicing and were consistently detected by both techniques. CONCLUSIONS: Despite the fact that the results of gene expression analysis were highly consistent between Human Transcriptome Arrays and RNA-seq platforms, the analysis of alternative splicing produced discordant results. We concluded that modern microarrays can still outperform sequencing for standard analysis of gene expression in terms of reproducibility and cost. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12864-017-3819-y) contains supplementary material, which is available to authorized users. BioMed Central 2017-06-06 /pmc/articles/PMC5461714/ /pubmed/28587590 http://dx.doi.org/10.1186/s12864-017-3819-y Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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 Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Nazarov, Petr V. Muller, Arnaud Kaoma, Tony Nicot, Nathalie Maximo, Cristina Birembaut, Philippe Tran, Nhan L. Dittmar, Gunnar Vallar, Laurent RNA sequencing and transcriptome arrays analyses show opposing results for alternative splicing in patient derived samples |
title | RNA sequencing and transcriptome arrays analyses show opposing results for alternative splicing in patient derived samples |
title_full | RNA sequencing and transcriptome arrays analyses show opposing results for alternative splicing in patient derived samples |
title_fullStr | RNA sequencing and transcriptome arrays analyses show opposing results for alternative splicing in patient derived samples |
title_full_unstemmed | RNA sequencing and transcriptome arrays analyses show opposing results for alternative splicing in patient derived samples |
title_short | RNA sequencing and transcriptome arrays analyses show opposing results for alternative splicing in patient derived samples |
title_sort | rna sequencing and transcriptome arrays analyses show opposing results for alternative splicing in patient derived samples |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5461714/ https://www.ncbi.nlm.nih.gov/pubmed/28587590 http://dx.doi.org/10.1186/s12864-017-3819-y |
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