A New Workflow Automating the Data Analysis in Gene Expression Screens Across Assay Formats Produces Consistent Results at Scale and Efficiency


J Florez1; R Chassefeyre2; G Nass Kovacs1; X Marechal2; S Heyse1; E Perret2; S Steigele1
1 Genedata AG, Switzerland;  2 Evotec (France) SAS, France


The characterization of gene expression changes has broad applications, including (1) screening of RNA-based or small molecule drugs which function by direct modulation of gene expression, e.g., by altering pre-mRNA splicing, (2) verifying effects of protein-targeting drugs on given cellular pathways, and (3) toxicity profiling or assessing target selectivity. Today, assays such as multiplexed reverse transcription qPCR (RT-qPCR) or bead-based technologies like the QuantiGeneTM assay can be performed on automated platforms to enable screening of gene expression at scale. These technologies allow a large increase in throughput, but to date, there exists no common analysis workflow which can consistently and efficiently process high-volume data for all gene expression assays. In this poster, we present a new, highly automated analysis workflow embedded in Genedata Screener which yields major efficiency gains and standardized, high-quality results. This workflow provides built-in functionality for processing and quality control (QC) procedures common to all gene expression assays, such as: normalization to house-keeping genes, which can be assigned per assay; dedicated fits for fold change measurements in dose-response; automated QC including masking of unreliable measurements, flagging of cytotoxic compounds, and dedicated quality plots. Additionally, the workflow features analyses specific to RT-qPCR, such as viewing raw amplification curves for RT-qPCR experiments and robust, automated determination of Ct values. Recently, this workflow was deployed at Evotec to streamline RT-qPCR-based screens, enabling the routine screening of up to 400’000 compounds, resulting in rapid analysis and significantly shortened cycle times for gene expression assays.

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