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Assessing differential expression measurements by highly parallel pyrosequencing and DNA microarrays: a comparative study.

ABSTRACT: To explore the feasibility of pyrosequencing for quantitative differential gene expression analysis we have performed a comparative study of the results of the sequencing experiments to those obtained by a conventional DNA microarray platform. A conclusion from our analysis is that, over a threshold of 35 normalized reads per gene, the measurements of gene expression display a good correlation with the references. The observed concordance between pyrosequencing and DNA microarray platforms beyond the threshold was of 0.8, measured as a Pearson's correlation coefficient. In differential gene expression the initial aim is the quantification the differences among transcripts when comparing experimental conditions. Thus, even in a scenario of low coverage the concordance in the measurements is quite acceptable. On the other hand, the comparatively longer read size obtained by pyrosequencing allows detecting unconventional splicing forms.

SUBMITTER: Arino J

PROVIDER: S-EPMC3545353 | biostudies-literature | 2013 Jan

REPOSITORIES: biostudies-literature

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Assessing differential expression measurements by highly parallel pyrosequencing and DNA microarrays: a comparative study.

Ariño Joaquín J Casamayor Antonio A Pérez Julián Perez JP Pedrola Laia L Álvarez-Tejado Miguel M Marbà Martina M Santoyo Javier J Dopazo Joaquín J

Omics : a journal of integrative biology 20110915 1

To explore the feasibility of pyrosequencing for quantitative differential gene expression analysis we have performed a comparative study of the results of the sequencing experiments to those obtained by a conventional DNA microarray platform. A conclusion from our analysis is that, over a threshold of 35 normalized reads per gene, the measurements of gene expression display a good correlation with the references. The observed concordance between pyrosequencing and DNA microarray platforms beyon ...[more]

PMID: 21919703

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Project description:BackgroundMicroRNAs (miRNAs) are a new class of small RNAs of approximately 22 nucleotides in length that control eukaryotic gene expression by fine tuning mRNA translation. They regulate a wide variety of biological processes, namely developmental timing, cell differentiation, cell proliferation, immune response and infection. For this reason, their identification is essential to understand eukaryotic biology. Their small size, low abundance and high instability complicated early identification, however cloning/Sanger sequencing and new generation genome sequencing approaches overcame most technical hurdles and are being used for rapid miRNA identification in many eukaryotes.ResultsWe have applied 454 DNA pyrosequencing technology to miRNA discovery in zebrafish (Danio rerio). For this, a series of cDNA libraries were prepared from miRNAs isolated at different embryonic time points and from fully developed organs. Each cDNA library was tagged with specific sequences and was sequenced using the Roche FLX genome sequencer. This approach retrieved 90% of the 192 miRNAs previously identified by cloning/Sanger sequencing and bioinformatics. Twenty five novel miRNAs were predicted, 107 miRNA star sequences and also 41 candidate miRNA targets were identified. A miRNA expression profile built on the basis of pyrosequencing read numbers showed high expression of most miRNAs throughout zebrafish development and identified tissue specific miRNAs.ConclusionThis study increases the number of zebrafish miRNAs from 192 to 217 and demonstrates that a single DNA mini-chip pyrosequencing run is effective in miRNA identification in zebrafish. This methodology also produced sufficient information to elucidate miRNA expression patterns during development and in differentiated organs. Moreover, some zebrafish miRNA star sequences were more abundant than their corresponding miRNAs, suggesting a functional role for the former in gene expression control in this vertebrate model organism.

Project description:Microarrays are widely used for examining differential gene expression, identifying single nucleotide polymorphisms, and detecting methylation loci. Multiple testing methods in microarray data analysis aim at controlling both Type I and Type II error rates; however, real microarray data do not always fit their distribution assumptions. Smyth's ubiquitous parametric method, for example, inadequately accommodates violations of normality assumptions, resulting in inflated Type I error rates. The Significance Analysis of Microarrays, another widely used microarray data analysis method, is based on a permutation test and is robust to non-normally distributed data; however, the Significance Analysis of Microarrays method fold change criteria are problematic, and can critically alter the conclusion of a study, as a result of compositional changes of the control data set in the analysis. We propose a novel approach, combining resampling with empirical Bayes methods: the Resampling-based empirical Bayes Methods. This approach not only reduces false discovery rates for non-normally distributed microarray data, but it is also impervious to fold change threshold since no control data set selection is needed. Through simulation studies, sensitivities, specificities, total rejections, and false discovery rates are compared across the Smyth's parametric method, the Significance Analysis of Microarrays, and the Resampling-based empirical Bayes Methods. Differences in false discovery rates controls between each approach are illustrated through a preterm delivery methylation study. The results show that the Resampling-based empirical Bayes Methods offer significantly higher specificity and lower false discovery rates compared to Smyth's parametric method when data are not normally distributed. The Resampling-based empirical Bayes Methods also offers higher statistical power than the Significance Analysis of Microarrays method when the proportion of significantly differentially expressed genes is large for both normally and non-normally distributed data. Finally, the Resampling-based empirical Bayes Methods are generalizable to next generation sequencing RNA-seq data analysis.

Project description:BackgroundDNA microarray technology allows the analysis of genome structure and dynamics at genome-wide scale. Expression microarrays (EMA) contain probes for annotated open reading frames (ORF) and are widely used for the analysis of differential gene expression. By contrast, tiling microarrays (TMA) have a much higher probe density and provide unbiased genome-wide coverage. The purpose of this study was to develop a protocol to exploit the high resolution of TMAs for quantitative measurement of DNA strand-specific differential expression of annotated and non-annotated transcripts.ResultsWe extensively filtered probes present in Affymetrix Genechip Yeast Genome 2.0 expression and GeneChip S. pombe 1.0FR tiling microarrays to generate custom Chip Description Files (CDF) in order to compare their efficiency. We experimentally tested the potential of our approach by measuring the differential expression of 4904 genes in the yeast Schizosaccharomyces pombe growing under conditions of oxidative stress. The results showed a Pearson correlation coefficient of 0.943 between both platforms, indicating that TMAs are as reliable as EMAs for quantitative expression analysis. A significant advantage of TMAs over EMAs is the possibility of detecting non-annotated transcripts generated only under specific physiological conditions. To take full advantage of this property, we have used a target-labelling protocol that preserves the original polarity of the transcripts and, therefore, allows the strand-specific differential expression of non-annotated transcripts to be determined. By using a segmentation algorithm prior to generating the corresponding custom CDFs, we identified and quantitatively measured the expression of 510 transcripts longer than 180 nucleotides and not overlapping previously annotated ORFs that were differentially expressed at least 2-fold under oxidative stress.ConclusionsWe show that the information derived from TMA hybridization can be processed simultaneously for high-resolution qualitative and quantitative analysis of the differential expression of well-characterized genes and of previously non-annotated and antisense transcripts. The consistency of the performance of TMA, their genome-wide coverage and adaptability to updated genome annotations, and the possibility of measuring strand-specific differential expression makes them a tool of choice for the analysis of gene expression in any organism for which TMA platforms are available.

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Assessing differential expression measurements by highly parallel pyrosequencing and DNA microarrays: a comparative study.

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Assessing differential expression measurements by highly parallel pyrosequencing and DNA microarrays: a comparative study.

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