• RNA EXPRESSION
• LIVER
• LIVER BIOPSY
• HEPATITIS C

‘But, where’s the beef?’, a colleague and friend asked me at a conference, right after a student from my group had presented our first transcriptional data set on T cells in viral hepatitis. I knew exactly what she meant. As scientists, we are accustomed to expect clear and definitive answers to specific questions that step by step prove or disprove a defined hypothesis developed on rigorous previous data. However, comprehensive and unbiased omics studies, like the analysis of genome-wide gene expression in liver tissue with HCV infection by Marchi et al in Gut,1 are in most instances neither aimed nor suited to deliver easy-to-grasp answers. This is especially true for cross-sectional analyses in humans with their inherent heterogeneity in mutable and immutable traits. The conversation mentioned previously happened almost 10 years ago, and studies using omics approaches in human cohorts have become commonplace in the meantime, but most of us still feel not completely at ease assessing the merits of omics-based work. This challenge is aggravated by the rapid speed with which techniques for omics data generation as well as the computational tools for their analysis are evolving, making one feel always one or two steps below the required level of expertise. Without a doubt, coming generations of scientists will deal with large-scale omics data much more naturally, but for now the challenge of evaluating and understanding complex omics studies is a reality for many of us.

The transcriptomics study by Marchi et al serves as an excellent example to discuss criteria for identifying those omics studies that are highly informative now and that can be expected to have significant impact down the road. Key aspects are the significance and potential of the analysed material, …