Welche Fähigkeiten und Werkzeuge werden für das Omics-Datenmanagement und den Austausch in der translationalen Forschung benötigt?

Data quality is the key. One needs to be consider multiple factors while analyzing the data. Some examples are 1) Patient Data Accuracy: Inaccuracies in patient records can lead to misguided treatment decisions and compromise the integrity of translational research. 2) Standardizations across hospitals 3) Biomedical Imaging Quality: Imaging data, such as MRI or CT scans, must be of high resolution and accuracy for accurate diagnosis and effective translational research outcomes. 4) Biobanking and Sample Quality: In translational medicine, the quality of biospecimens (e.g., blood samples) is crucial for accurate molecular analyses and research outcomes.

> Proficiency in bioinformatics tools for processing omics data, such as NGS and microarray data analysis. > Familiarity with data management platforms and databases designed for omics data storage and retrieval and programming skills (R or Python). > Knowledge of statistical methods and machine learning algorithms relevant to omics data analysis. > Understanding of ethical considerations and compliance with data sharing standards in translational research. > Expertise in data visualization techniques to effectively communicate complex findings to diverse audiences. > Ensuring compliance with open science principles. > Ability to integrate and interpret multi-omics datasets for comprehensive translational insights.

While considering data storage, one of the key aspects to remember would be, omics data is often large. So an appropriate storage option should be explored along with the cost component. Data residency requirement is equally important.

Data analysis is also an important part. Statistical methods need to be supported by p-values and Confidence intervals. Research questions need to be clear for example: Biomarker vs. mechanistics study. If the biomarker is, then a new validation cohort is mandatory. Validation analysis with proper modeling (Multivariate and also Univariate analysis) and reporting ROC, Sensitivity, Specificity etc. wll be critical. Then visualization of the results for example : PCA (or UMAP/t-SNE) score plot to show differences in the cohort.

Integrating Omics with other data modalities (e.g. Image, EMR) Integrating data at scale Avoid Data Silos Establish effective Data Relationships Have Templates, Ontologies and Controlled Vocabularies in place

Data Sharing needs to happen with proper agreement with metadata information. All the information needs to be well documented and shared. Scripts need to be shared using Github or Gitlab.

Making Omics data FAIR: Findable: Data should be easily discoverable Accessible: Data should be easily accessible and retrievable Interoperable: Data should be able to be integrated and combined with other data Reusable: Data should be reusable for future research Adhering to FAIR Data principles enhances collaboration, data sharing, and reproducibility in Multi-Omics research Narrow view of individual projects/phases -> Query across diverse data and time scales