AltraBio data analysis package is build on a strong background in biostatistics combined with the interest in high throughput technologies. These technologies allow to measure a very large number of variables in the living systems. They complement more traditional techniques measuring one value at a time or in combinations as panels of a relatively small number of assays.
Why Transcriptomics?
Transcriptomics, a genome-wide measurement of mRNA expression levels based on DNA microarray ("gene chip") technology, 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 or toxicity prediction).
While the most functional information about the cellular state lies at the level of protein, in practice transcriptomics offers a crucial advantage over proteomics, namely the ability to amplify the RNA molecules leading to detection of even very rare transcripts, and the technical feasibility of transcript detection based on hybridization to complementary probes (defined by nucleic acid sequence). Thus, transcriptomics with its tool, microarray data analysis, is positioned between genomics (reflecting the relatively static information at the level of genome) and proteomics (reflecting the fully functional, though not easily obtainable information at the level of proteome).
Transcriptomics is one of the primary tools of pharmacogenomics.
AltraBio: The Added Value
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In the classical approach, transcriptomic analysis typically yields a list of differentially expressed genes. AltraBio has expanded its statistical models to include analysis based on biological concepts, in which groups of genes (rather than individual genes) 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. |
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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. 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 obtained by transcriptomics. |
Contact us to find out how AltraBio can address your preclinical data analysis and interpretation needs.