Author(s): Chen C, Le H, Goudar CT
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Abstract Recent advances in RNA-Seq based comparative transcriptomics have opened up a unique opportunity to understand the mechanisms of different phenotypes in bioprocessing-related cell lines including Chinese hamster ovary (CHO) cells. However, simple and powerful tools are needed to translate large data sets into biologically relevant information that can be leveraged for genetic engineering and cell culture medium and process development. While tools exist to perform specific tasks associated with transcriptomics analysis, integrated end to end solutions that span the entire spectrum of raw data processing to visualization of gene expression changes on canonical pathways are rare. Additionally, these are not automated and require substantial user intervention. To address this gap, we have developed an automated RNA-Seq analysis pipeline in R which leverages the latest public domain statistical advances in transcriptomics data analysis. This pipeline reads RNA-Seq gene count data, identifies differentially expressed genes and differentially expressed pathways, and provides multiple intuitive visualizations as outputs. By using two publicly available CHO RNA-Seq datasets, we have demonstrated the utility of this pipeline. Subsequently, this pipeline was used to demonstrate transcriptomic similarity between laboratory- and pilot-scale bioreactors, helping make a case for the suitability of the lab-scale bioreactor as a scaled-down model. Automated end to end RNA-Seq data analysis approaches such as the one presented in this study will shorten the time required from acquiring sequencing data to biological interpretation of the results and can help accelerate the adoption of RNA-Seq analysis and thus mechanism-driven approaches for cell line and bioprocess optimization. © 2015 American Institute of Chemical Engineers.
This article was published in Biotechnol Prog
and referenced in Journal of Bioprocessing & Biotechniques