BMDExpress 2: enhanced transcriptomic dose-response analysis workflow.

SUMMARY: A new version (version 2) of the genomic dose-response analysis software, BMDExpress, has been created. The software addresses the increasing use of transcriptomic dose-response data in toxicology, drug design, risk assessment and translational research. In this new version, we have implemented additional statistical filtering options (e.g. Williams' trend test), curve fitting models, Linux and Macintosh compatibility and support for additional transcriptomic platforms with up-to-date gene annotations. Furthermore, we have implemented extensive data visualizations, on-the-fly data filtering, and a batch-wise analysis workflow. We have also significantly re-engineered the code base to reflect contemporary software engineering practices and streamline future development. The first version of BMDExpress was developed in 2007 to meet an unmet demand for easy-to-use transcriptomic dose-response analysis software. Since its original release, however, transcriptomic platforms, technologies, pathway annotations and quantitative methods for data analysis have undergone a large change necessitating a significant re-development of BMDExpress. To that end, as of 2016, the National Toxicology Program assumed stewardship of BMDExpress. The result is a modernized and updated BMDExpress 2 that addresses the needs of the growing toxicogenomics user community. AVAILABILITY AND IMPLEMENTATION: BMDExpress 2 is available at https://github.com/auerbachs/BMDExpress-2/releases. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Correlation between in vitro peptide binding profiles and cellular activities for estrogen receptor-modulating compounds.

Numerous biochemical and structural studies have shown that the conformation of the estrogen receptor alpha (ERalpha) can be influenced by ligand binding. In turn, the conformational state of ERalpha affects the ability of the receptor to interact with a wide variety of protein accessory factors. To globally investigate ligand-based cofactor recruitment activities of ERalpha, we have applied a flow cytometric multiplexed binding assay to determine the simultaneous binding of ERalpha to over 50 different peptides derived from both known cofactor proteins and random peptide phage display. Using over 400 ERalpha-binding compounds, we have observed that the multiplexed in vitro peptide-binding profiles are distinct for a number of compounds and that these profiles can predict the effect that ERalpha ligands have on various cellular activities. These cell-based activities include transcriptional regulation at an estrogen response element, MCF-7 cell proliferation, and Ishikawa endometrial cell stimulation. The majority of the compound-induced diversity in the peptide profiling assay is provided by the unique phage display peptides. Importantly, some of these peptides show a sequence relationship with the corepressor motif, suggesting that peptides identified via phage display might represent natural binding partners of ERalpha. These in vitro:cellular correlations may in part explain tissue-specific activities of ERalpha-modulating compounds.

The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance.

The concordance of RNA-sequencing (RNA-seq) with microarrays for genome-wide analysis of differential gene expression has not been rigorously assessed using a range of chemical treatment conditions. Here we use a comprehensive study design to generate Illumina RNA-seq and Affymetrix microarray data from the same liver samples of rats exposed in triplicate to varying degrees of perturbation by 27 chemicals representing multiple modes of action (MOAs). The cross-platform concordance in terms of differentially expressed genes (DEGs) or enriched pathways is linearly correlated with treatment effect size (R(2)0.8). Furthermore, the concordance is also affected by transcript abundance and biological complexity of the MOA. RNA-seq outperforms microarray (93% versus 75%) in DEG verification as assessed by quantitative PCR, with the gain mainly due to its improved accuracy for low-abundance transcripts. Nonetheless, classifiers to predict MOAs perform similarly when developed using data from either platform. Therefore, the endpoint studied and its biological complexity, transcript abundance and the genomic application are important factors in transcriptomic research and for clinical and regulatory decision making.