Deep sequencing-based expression analysis shows major advances in robustness, resolution and inter-lab portability over five microarray platforms

Abstract

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.

Applications of next generation sequencing in molecular ecology of non-model organisms

“…An attractive alternative to microarrays for gene expression studies in non-model organisms is sequencing-based expression profiling, or digital transcriptomics (Murray et al, 2007). The rationale behind this approach is that the representation of specific sequences derived from deep cDNA sequencing is proportional to the amount of RNA from the gene in the original sample (t Hoen et al, 2008). Basically, there are two different versions of this kind of analysis.…”

MicroRNA Classifier and Nomogram for Metastasis Prediction in Colon Cancer

“…The tissue-specific (16) and cell type-specific (17) nature of miRNA expression, combined with the high degree of heterogeneity of colon tumors with regard to the amount of stroma and immune infiltrate, requires selection of target cells by microdissection (18), like laser capture microdissection (LCM). To be able to investigate expression of the whole micronome, and the possibility of detecting novel miRNAs, a next-generation sequencing (NGS) approach is recommended (19). Previous studies reporting on miRNA classifier profiles for metastasis prediction in colon or colorectal cancer (20)(21)(22)(23)(24) did not use the optimal approach of identification of miRNAs by comparison of their expression patterns between patients with and without metachronous metastasis using NGS in microdissected tumors.…”

Assessing Differential Expression Measurements by Highly Parallel Pyrosequencing and DNA Microarrays: A Comparative Study

“…Recent articles have compared short-read technologies to microarray references, finding a good correspondence when a high number of reads is available (Bloom et al, 2009;t'Hoen et al, 2008). Here we extend, for the first time, this comparison to long reads to check to what extent the results of differential gene expression comparisons obtained by pyrosequencing are consistent with those ones obtained with gene expression microarrays.…”