The AltraBio's team implements and develops state-of-the-art and innovative methods for all kinds of omics studies (genomics, transcriptomics, proteomics,...) with a very high expertise in transcriptomics.
The AltraBio's Added Value
- Time-proven and cutting-edge data analysis methodologies: building on the BioConductor software packages.
- Extensive quality controls: applying its expertise to a wide variety of large-scale biological datasets, AltraBio performs extensive quality controls to identify potential outliers and check for consistency with the experimental design, thus ensuring the relevance and quality of downstream analyses.
- Preprocessing and statistical analysis: AltraBio then provides the complete range of preprocessing and statistical analysis services relevant to all current omics technologies (e.g., identification of differentially expressed genes, SNP associations).
- Biological pathway approach: in the classical approach, omics analysis typically yields a list of differentially expressed molecules. AltraBio has expanded its statistical models to include analysis based on biological concepts, in which groups of molecules (rather than individual ones) that are informative for the experiment are identified. Besides including the pre-defined categories and annotations from resources such as Gene Ontology (GO), AltraBio has been building a collection of novel sets based on publicly available gene expression data and associated literature.
- Biological interpretation: AltraBio's involvement in a project does not end with the generation of a list. Using the integrative, biological pathway-oriented approach, AltraBio analysts interpret the lists in the context of the biological problem the experiment addresses.
- Comprehensive report: The final result of AltraBio's work is a comprehensive report summarizing the experimental observations, discussing the biological mechanisms underlying the observed effects and, where appropriate, suggesting follow-up experiments to further elaborate on the results.
Transcriptomics, a genome-wide measurement of mRNA expression levels based on DNA microarray ("gene chip") or RNAseq technologies, has a prominent role among these techniques. Determination of the transcript levels of practically all annotated protein-coding genes not only provides a comprehensive gene expression profile but, crucially, allows powerful statistical analysis and mining of the generated datasets. Identification of groups of genes expressed in a coordinated manner, combined with the access to an annotated and searchable biological knowledge base, enables the analyst to gain an insight about the mechanisms of action at the level of gene expression. This is of a substantial value in the drug discovery and development setting (e.g., target validation, toxicity prediction).