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Front Genet. 2017 Nov 1;8:168. doi: 10.3389/fgene.2017.00168. eCollection 2017.

A Pipeline for High-Throughput Concentration Response Modeling of Gene Expression for Toxicogenomics.

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Bioinformatics Research Center, North Carolina State University, Raleigh, NC, United States.
Center for Human Health and the Environment, North Carolina State University, Raleigh, NC, United States.
Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, TX, United States.
Department of Biological Sciences, North Carolina State University, Raleigh, NC, United States.
Department of Statistics, North Carolina State University, Raleigh, NC, United States.


Cell-based assays are an attractive option to measure gene expression response to exposure, but the cost of whole-transcriptome RNA sequencing has been a barrier to the use of gene expression profiling for in vitro toxicity screening. In addition, standard RNA sequencing adds variability due to variable transcript length and amplification. Targeted probe-sequencing technologies such as TempO-Seq, with transcriptomic representation that can vary from hundreds of genes to the entire transcriptome, may reduce some components of variation. Analyses of high-throughput toxicogenomics data require renewed attention to read-calling algorithms and simplified dose-response modeling for datasets with relatively few samples. Using data from induced pluripotent stem cell-derived cardiomyocytes treated with chemicals at varying concentrations, we describe here and make available a pipeline for handling expression data generated by TempO-Seq to align reads, clean and normalize raw count data, identify differentially expressed genes, and calculate transcriptomic concentration-response points of departure. The methods are extensible to other forms of concentration-response gene-expression data, and we discuss the utility of the methods for assessing variation in susceptibility and the diseased cellular state.


bioinformatics & computational biology; bioinformatics-pipeline; cardiomyocytes; dose–response modeling; expression profiling; expression-based dose–response modeling; iPSCs; toxicogenomics

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