Potential applications include identifying new drug targets, finding new biomarkers for the treatment of diseases and developing better crops. However, because of the complexity underlying some of the fundamental biological questions in these areas, it is crucial that "omics" technologies are applied in the most effective fashion to leverage the full benefit of these disciplines. The CPGR applies "omics" in the following manner:
| Common Issues | CPGR Approach |
|---|---|
| Poor quality data, in particular in the less mature technologies (e.g. Mass Spectrometry) | Rigorous validation of workflows; stringent QA/QC systems including quality gates in workflows; proficiency testing; and inter laboratory comparisons |
| Disconnect of data-generation and data-analysis | Strong links between data generating platforms and computational biology; a multi-disciplinary team of experts can assist with design, execution, and analysis of omics projects in one place |
| Disconnect of biology and "omics" | In-depth understanding and handling of biological systems, "omics" technologies and bio-computational solutions in one place enhances value generation and the innovation potential in R&D programs |
| "Value chain" thinking, sequential problem tackling, "engineering fallacy" | "Value systems" approach: iterative, adaptive, learning- and solutions-focused strategies |
| More data than questions to ask | Solutions designed to generate problem-specific outputs |
