Regardless of the pace of application of personal genome sequencing in clinical practice, researchers should bear in mind that additional tools are needed to supplement personal genomes so that insights can be made into the molecular biology of complex diseases. Among such tools, genome-wide transcriptomic profiling seems to be a powerful yet straightforward, affordable and easily validated molecular biology technology for discovering new diagnostic biomarkers and drug targets [4, 5]. For example, expression profiling currently costs under US$400 per sample using commercial microarrays. Comparisons of healthy and diseased tissues from the same individual, or the same tissue (in particular, white blood cells) from an individual over time, such as before and following drug treatment, may yield knowledge not extractable from personal genome sequences. The complexity of the interrelationship between DNA sequences and cell biology is likely to be far higher than is currently understood. For example, a new level of complexity linking the genome to the proteome has recently been introduced: it is well established that gene expression is regulated by short (22 to 23 nucleotides long) non-coding RNA sequences termed microRNAs. Now, an additional level of complexity has been discovered: circular non-coding RNAs, which modulate the action of microRNAs on gene expression [6]. Further surprises are likely to be in store for gene expression regulation by non-coding genome sequences, even though these sequences are part of already published - but little understood-personal genomes.
Another key advantage of expression profiling studies is that they also inform about the consequences of epigenomic modifications, as transcriptomes reflect not merely the output of DNA sequences, but also their interplay with non-genetic modifiers of gene expression. Moreover, transcriptomic studies can inform about alternative splicing events - in particular when RNA sequencing is applied - whereas, at our current level of knowledge, personal genome sequences do not have this capacity.
Expression profiling data can thus be far more informative than personal genomes for deciphering cellular networks and disease biomarkers, and indicating drug targets. Certainly, having both personal genomes and longitudinal gene expression profiles from the same study participants has clear advantages. Indeed, an integrative 'personal'omics profile that combines genomic, transcriptomic, proteomic, metabolomic and autoantibody profiles from a single individual over 14 months was recently presented [7]. This is undeniably the best way forward for genomic medicine projects when adequate funding is available. However, in the clinical setting, as well as for most academic research groups, costs for such comprehensive projects - in particular for large cohorts - are prohibitive. In lieu of such funding levels, expression profiling seems to offer the most promising and cost-effective approach for genome-wide searches for disease and drug-response biomarkers.