Transcriptomic Analyzes (RNA-Seq)

BioDecision Analytics offers intelligent solutions that allow transforming transcriptomic data from RNA sequencing (RNA-Seq) into information that helps in the interpretation of in vitro, preclinical, and/or clinical results. We also have an RNA-Seq analysis method developed to serve the pharmaceutical and biopharmaceutical industries. Through advanced bioinformatics analysis, our company counts on an exclusive service for the drug or biopharmaceuticals’ mechanism of action (MoA) prediction. By integrating the results of the transcriptomic analysis with in vitro, preclinical, and/or clinical data, through our application of Data Analytic techniques, we work to provide evidence of the mechanism of action supporting regulatory processes and adding economic value to investigational products.

How we do?

Developed in the mid-2000s, RNA sequencing (RNA-Seq) represents one of the greatest milestones in Life Science. This is because RNA-Seq allows to simultaneous assessment of the presence and quantity of gene products (also known as RNA or transcripts) in biological samples of interest. Through the combination of computational tools (bioinformatics) and appropriate statistical methods, the transcripts expressed by each biological sample (transcriptome) can be compared with the reference transcriptome of the studied specie or with the transcriptome of control samples (for experiments) or other samples from the same analyzed specie. Such analyzes allow identifying:

• Differentially expressed genes
• Mutations
• Gene fusions
• Transcripts that underwent alternative splicing

Due to the high analytical power of this method, RNA-Seq has proven to be highly valuable technique for pharmaceutical industry, allowing to accelerate the mechanism of action (MoA) discovery of investigational products (IP). This is because functional enrichment analysis of differentially expressed genes between treated and untreated samples (in vitro cells, animal models and even patients) with the IP provide evidence of the biological pathways that were affected by the treatment. For this, we integrate the latest computational tools dedicated to the analysis of RNA sequencing data, statistical methods and Data Science techniques optimized to meet the needs of each project. Following best practices recommendations for RNA-Seq, our analyzes have rigorous quality control process that ensure the accuracy of the results. After quality control, the reads obtained are aligned to the reference genome to map the expressed genes. After mapping, the reads obtained are counted. The obtained counts are normalized using appropriated methods. Next, a differential expression analysis is performed, looking for gene that are up or downregulated. The list of differentially expressed genes is subjected to functional enrichment analyses to identify the biological pathways regulated by these genes.

The RNA-Seq analysis workflow is summarized below:

Imagem RNASeq