Microarray-based gene-expression profiling technology hardly seems like it has been around long enough for a new approach to come along and challenge its market dominance. Yet, there is a growing sense that digital gene-expression profiling, a fully quantitative approach for gene-expression analysis, will replace microarrays in this application area.

Within this emerging market sector for digital RNA counting, the methodologies and technologies are evolving so rapidly that traditional sequencing-based serial analysis of gene-expression  approaches are being challenged by more direct RNASeq techniques, driven in large part by the advances in and declining cost of next-generation sequencing technologies.

Digital gene-expression (DGE) technologies are emerging that eliminate the need for restriction enzyme digestion of DNA samples, PCR-based genomic amplification, and ligation of sequence tags. These innovative strategies are not only making whole transcriptome analysis feasible and cost-efficient, they are also creating new commercial opportunities, including the analysis of newly discovered populations of small RNAs believed to have an important role in gene regulation, protein expression, and cell function.

DGE offers distinct advantages over array-based gene-expression analysis systems for transcriptomics, including better coverage, the ability to measure low-abundance genes and to find unknown transcripts, and minimal background noise for increased sensitivity.

A group of Dutch researchers compared deep sequencing-based gene-expression analysis using the Illumina whole genome sequencer to five microarray-based platforms. They concluded “that deep sequencing provides a major advance in robustness, comparability, and richness of expression profiling data.” The authors predicted that, “with the continuously increasing number of reads at reduced costs, RNASeq will become affordable for standard differential gene-expression analysis.”