The Omics revolution has provided the researcher with tools and methodologies for qualitative and quantitative assessment of a wide spectrum of molecular players spanning from the genome to the meta-bolome level. As a consequence, explorative analysis (in contrast to purely hypothesis driven research procedures) has become applicable. However, numerous issues have to be considered for deriving meaningful results from Omics, and bioinformatics has to respect these in data analysis and interpretation. Aspects include sample type and quality, concise definition of the (clinical) question, and selection of samples ideally coming from thoroughly defined sample and data repositories. Omics suffers from a principal shortcoming, namely unbalanced sample-to-feature matrix denoted as "curse of dimensionality", where a feature refers to a specific gene or protein among the many thousands assayed in parallel in an Omics experiment. This setting makes the identification of relevant features with respect to a phenotype under analysis error prone from a statistical perspective. From this sample size calculation for screening studies and for verification of results from Omics, bioinformatics is essential. Here we present key elements to be considered for embedding Omics bioinformatics in a quality controlled workflow for Omics screening, feature identification, and validation. Relevant items include sample and clinical data management, minimum sample quality requirements, sample size estimates, and statistical procedures for computing the significance of findings from Omics bioinformatics in validation studies.
Cross-Omics studies aimed at characterizing a specific phenotype on multiple levels are entering the -scientific literature, and merging e.g. transcriptomics and proteomics data clearly promises to improve Omics data interpretation. Also for Systems Biology the integration of multi-level Omics profiles (also across species) is considered as central element. Due to the complexity of each specific Omics technique, specialization of experimental and bioinformatics research groups have become necessary, in turn demanding collaborative efforts for effectively implementing cross-Omics. This setting imposes specific emphasis on data sharing platforms for Omics data integration and cross-Omics data analysis and interpretation. Here we describe a software concept and methodology fostering Omics data sharing in a distributed team setting which next to the data management component also provides hypothesis generation via inference, semantic search, and community functions. Investigators are supported in data workflow management and interpretation, supporting the transition from a collection of heterogeneous Omics profiles into an integrated body of knowledge.
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