An automated RNA-Seq analysis pipeline to identify and visualize differentially expressed genes and pathways in CHO cells

摘要: Recent advances in RNA-Seq based comparative transcriptomics have opened up a unique opportunity to understand the mechanisms of different phenotypes bioprocessing-related cell lines including Chinese hamster ovary (CHO) cells. However, simple and powerful tools are needed translate large data sets into biologically relevant information that can be leveraged for genetic engineering culture medium process development. While exist perform specific tasks associated with analysis, integrated end solutions span entire spectrum raw processing visualization gene expression changes on canonical pathways rare. Additionally, these not automated require substantial user intervention. To address this gap, we developed an analysis pipeline R which leverages latest public domain statistical analysis. This reads count data, identifies differentially expressed genes pathways, provides multiple intuitive visualizations as outputs. By using two publicly available CHO datasets, demonstrated utility pipeline. Subsequently, was used demonstrate transcriptomic similarity between laboratory- pilot-scale bioreactors, helping make case suitability lab-scale bioreactor scaled-down model. Automated approaches such one presented study will shorten time required from acquiring sequencing biological interpretation results help accelerate adoption thus mechanism-driven line bioprocess optimization.