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

TLDR

The study compared hippocampal gene expression in wild‑type and δC‑DKI mice using Illumina deep sequencing alongside five microarray platforms. Deep sequencing yielded ~2.4 million tags per sample, identified 3,179 differentially expressed transcripts (8.5 % FDR) with larger fold changes than microarrays, revealed antisense transcription in 51 % of genes and alternative polyadenylation in 47 %, and demonstrated higher robustness and richer biological insights than 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.