Deep sequencing-based expression analysis shows major advances in robustness, resolution and inter-lab portability over five microarray platforms
By P. '. ’t Hoen, Y. Ariyurek, Helene H. Thygesen, E. Vreugdenhil, R. Vossen, Renée X de Menezes, Judith M. Boer, G. V. van Ommen, J. D. den Dunnen. Nucleic Acids Research, 2008
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The hippocampal expression profiles of wild-type mice and mice transgenic for δC-doublecortin-like kinase were compared with Solexa/Illumina deep sequencing technology and five different microarray platforms. With Illumina's digital gene expression assay, we obtained ∼2.4 million sequence tags per sample, their abundance spanning four orders of magnitude. Results were highly reproducible, even across laboratories. With a dedicated Bayesian model, we found differential expression of 3179 transcripts with an estimated false-discovery rate of 8.5%. This is a much higher figure than found for microarrays. The overlap in differentially expressed transcripts found with deep sequencing and microarrays was most significant for Affymetrix. The changes in expression observed by deep sequencing were larger than observed by microarrays or quantitative PCR. Relevant processes such as calmodulin-dependent protein kinase activity and vesicle transport along microtubules were found affected by deep sequencing but not by microarrays. While undetectable by microarrays, antisense transcription was found for 51% of all genes and alternative polyadenylation for 47%. We conclude that deep sequencing provides a major advance in robustness, comparability and richness of expression profiling data and is expected to boost collaborative, comparative and integrative genomics studies.
Subfields: Molecular Biology
View Paper: https://www.semanticscholar.org/paper/15d63d21db8364fd35fdba15e99839ad54c49db8
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Citations in the corpus
- Applications of next generation sequencing in molecular ecology of non-model organisms (2011, methodology): “In recent years, NGS has been utilized to characterize DNA methylation patterns genome-wide, using an application known as ultra-deep bisulphite DNA sequencing (BSSeq) (Taylor et al., 2007; Cokus et al., 2008).”
- Next generation sequencing technology: Advances and applications. (2014, background): “cost-directed, SAGE-like approaches were popular [22], but dropping costs now drives analysis towards full RNA sequencing (RNAseq) [23], requiring much deeper sequencing yet through analysis of the entire transcript revealing the complete picture incl.”
- Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments (2010, methodology): “literature include t-statistics with square root-transformed standard errors and Bayesian statistics based on the Beta-Binomial distribution (Hoen et al., 2008).”
- Correlation of mRNA and protein in complex biological samples (2009, positive): “A comparison with five array platforms demonstrates a better resolution, reproducibility and robustness of a deep sequencing based method [75].”
- Finding consistent patterns: A nonparametric approach for identifying differential expression in RNA-Seq data (2013, methodology): “We compare PoissonSeq, edgeR, DESeq, and SAMseq on three real sequencing data sets: an RNA-Seq data set from [16], a Tag-Seq data set from [12], and an miRNA-Seq data set from [35].”
- From RNA-seq reads to differential expression results (2010, methodology): “These include proof-of-principle demonstrations of the sequencing platform [2,31,32], dedicated comparison studies [34,75-77] and analysis methodology development [10].”
- Translational Genomics in Agriculture: Some Examples in Grain Legumes (2015, background): “, 2011), gene expression analysis (Hoen et al., 2008; Wang et al., 2012), and epigenetics (Jackson et al.”