COVID-19 is an emerging, rapidly evolving situation.

Get the latest public health information from CDC and research information from NIH.

U.S. flag

An official website of the United States government

Dot gov

The .gov means it's official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you're on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Share This:

Transcriptomic Profiling of Rat Liver Samples in A Comprehensive Study Design by RNA-Seq

Binsheng Gong, Charles Wang, Zhenqiang Su, Huixiao Hong, Jean Thierry-Mieg, Danielle Thierry-Mieg, Leming Shi, Scott S. Auerbach, Weida Tong, and Joshua Xu
Scientific Data (2014) DOI: https://doi.org/10.1038/sdata.2014.21 PMID: 25977778


Publication


Abstract

RNA-Seq provides the capability to characterize the entire transcriptome in multiple levels including gene expression, allele specific expression, alternative splicing, fusion gene detection, and etc. The US FDA-led SEQC (i.e., MAQC-III) project conducted a comprehensive study focused on the transcriptome profiling of rat liver samples treated with 27 chemicals to evaluate the utility of RNA-Seq in safety assessment and toxicity mechanism elucidation. The chemicals represented multiple chemogenomic modes of action (MOA) and exhibited varying degrees of transcriptional response. The paired-end 100 bp sequencing data were generated using Illumina HiScanSQ and/or HiSeq 2000. In addition to the core study, six animals (i.e., three aflatoxin B1 treated rats and three vehicle control rats) were sequenced three times, with two separate library preparations on two sequencing machines. This large toxicogenomics dataset can serve as a resource to characterize various aspects of transcriptomic changes (e.g., alternative splicing) that are byproduct of chemical perturbation.

Toxicogenomics


RNA-Seq Data

GEO Series: GSE47792, GSE47875, GSE55347

qPCR Data with Descriptions

Figshare: http://dx.doi.org/10.6084/m9.figshare.1032698

Supplemental Materials


Associated Publication

A Comprehensive Study Design Reveals Treatment- and Transcript Abundance–Dependent Concordance between RNA-Seq and Microarray Data
Wang C, Gong B, Bushel PR, Thierry-Mieg J, Thierry-Mieg D, Xu J, Fang H, Hong H, Shen J, Su Z, Meehan J, Li X, Yang L, Li H, Łabaj PP, Kreil DP, Megherbi D, Gaj S, Caiment F, van Delft J, Kleinjans J, Scherer A, Devanarayan V, Wang J, Yang Y, Qian HR, Lancashire LJ, Bessarabova M, Nikolsky Y, Furlanello C, Chierici M, Albanese D, Jurman G, Riccadonna S, Filosi M, Visintainer R, Zhang KK, Li J, Hsieh JH, Svoboda DL, Fuscoe JC, Deng Y, Shi L, Paules RS, Auerbach SS, Tong W.
Nat Biotechnol. (2014), http:/dx.doi.org/10.1038/nbt.3001. PMID: 25150839