Modeling data and analysis scripts generated during the current study are available in the github repository: https://github.com/USEPA/CompTox-MIEML. RefChemDB is available for download as supplemental material from its original publication (PMID: 30570668). LINCS gene expression data are publicly available and accessible through the gene expression omnibus (GSE92742 and GSE70138) at https://www.ncbi.nlm.nih.gov/geo/ . This dataset is associated with the following publication: Bundy, J., R. Judson, A. Williams, C. Grulke, I. Shah, and L. Everett. Predicting Molecular Initiating Events Using Chemical Target Annotations and Gene Expression. BioData Mining. BioMed Central Ltd, London, UK, issue}: 7, (2022).

Dataset for Harrill, J.A. et al., 'Signature Analysis of High-Throughput Transcriptomics Screening Data for Mechanistic Inference and Chemical Grouping' published in Toxicological Sciences, https://doi.org/10.1093/toxsci/kfae108 This dataset contains gene expression profiles and gene signature concentration-response modeling results for 1751 unique chemicals. The chemicals were tested in MCF7 cells using an exposure duration of six hours. The datasets also contains the results of molecular target enrichment and chemotype enrichment analyses performed downstream of the gene signature concentration-response modeling. Descriptions of each data file can be found in the supplementary material of the published article that is hosted by the journal. This dataset is associated with the following publication: Harrill, J., L. Everett, D. Haggard, L. Word, J. Bundy, B. Chambers, D. Harris, C. Willis, R. Thomas, I. Shah, and R. Judson. Signature Analysis of High-Throughput Transcriptomics Screening Data for Mechanistic Inference and Chemical Grouping. TOXICOLOGICAL SCIENCES. Society of Toxicology, RESTON, VA, 202(1): 103-122, (2024).

The dataset(s) supporting the conclusions of this article are available via the CompTox Chemistry Dashboard Downloads Page (https://comptox.epa.gov/dashboard/downloads) and MS-Ready GitHub repository (https://github.com/kmansouri/MS-ready). The MetFrag functionality is available through the web interface (https://msbi.ipb-halle.de/MetFragBeta/) and the command line version (http://c-ruttkies.github.io/MetFrag/projects/metfragcl/). All additional data supporting the conclusions of this article are included within the article and its additional files. This dataset is associated with the following publication: McEachran, A., K. Mansouri, C. Grulke, E. Schymanski, C. Ruttkies, and A. Williams. “MS-Ready” structures for non-targeted high-resolution mass spectrometry screening studies. Journal of Cheminformatics. Springer, New York, NY, USA, 10(45): 1-16, (2018).

All data associated with publication are publicly available online at https://datadryad.org/ by searching doi:10.5061/dryad.2tg6967 Upon accessing data on datadryad.org: Data descriptions are found with Supplemental Data presented in the pdf (Supplementary Data.pdf (600.4 Kb)). Data description of supplemental Data (Supplementary Data File.xlsx (1.167 Mb)) is provided below: Tabs one, two, and three contain the raw data for acetylcholinesterase (AChE) Level 1 (Figure 1A), Level 2 (Figure 1B), and Level 3 (Table 2) SeqAPASS analysis, respectively. Tab four contains the raw data for AChE Level 3 improved SeqAPASS analysis (Table 5). Tabs five, six, and seven contain the raw data for ecdysone receptor (EcR) Level 1 (Figure 2A), Level 2 (Figure 2B), and Level 3 (Table 6) SeqAPASS analysis, respectively. Tab eight contains the raw data for EcR Level 3 improved SeqAPASS analysis (Table 8). This dataset is associated with the following publication: Doering, J., S. Lee, K. Kristiansen, L. Evenseth, M. Barron, I. Sylte, and C. LaLone. In silico site-directed mutagenesis informs species-specific predictions of chemical susceptibility derived from the Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) tool.. TOXICOLOGICAL SCIENCES. Society of Toxicology, RESTON, VA, 166(1): 131-145, (2018).

All data associated with publication are publicly available online at https://datadryad.org/ by searching doi:10.5061/dryad.2tg6967 Upon accessing data on datadryad.org: Data descriptions are found with Supplemental Data presented in the pdf (Supplementary Data.pdf (600.4 Kb)). Data description of supplemental Data (Supplementary Data File.xlsx (1.167 Mb)) is provided below: Tabs one, two, and three contain the raw data for acetylcholinesterase (AChE) Level 1 (Figure 1A), Level 2 (Figure 1B), and Level 3 (Table 2) SeqAPASS analysis, respectively. Tab four contains the raw data for AChE Level 3 improved SeqAPASS analysis (Table 5). Tabs five, six, and seven contain the raw data for ecdysone receptor (EcR) Level 1 (Figure 2A), Level 2 (Figure 2B), and Level 3 (Table 6) SeqAPASS analysis, respectively. Tab eight contains the raw data for EcR Level 3 improved SeqAPASS analysis (Table 8). This dataset is associated with the following publication: Doering, J., S. Lee, K. Kristiansen, L. Evenseth, M. Barron, I. Sylte, and C. LaLone. In silico site-directed mutagenesis informs species-specific predictions of chemical susceptibility derived from the Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) tool.. TOXICOLOGICAL SCIENCES. Society of Toxicology, RESTON, VA, 166(1): 131-145, (2018).

This repository provides analysis code and results produced during evaluation of metagenomic sequencing (MGS) data collected through the Mosaic Standards Challenge. The Mosaic Standards Challenge asked participating laboratories analyze the same set of 7 samples using their own favored MGS laboratory methods. Each lab submitted their raw sequencing results and protocol information. The resulting MGS data was analyzed through a common bioinformatic pipeline and then evaluated to determine the effects of methodological choices.

This repository provides analysis code and results produced during evaluation of metagenomic sequencing (MGS) data collected through the Mosaic Standards Challenge. The Mosaic Standards Challenge asked participating laboratories analyze the same set of 7 samples using their own favored MGS laboratory methods. Each lab submitted their raw sequencing results and protocol information. The resulting MGS data was analyzed through a common bioinformatic pipeline and then evaluated to determine the effects of methodological choices.

This is STATA software code for analysis on publicly available NHANES data

This is STATA software code for analysis on publicly available NHANES data

This dataset is a project file generated by BMDExpress 2.2 SW (Sciome, Research Triangle Park, NC). It contains gene expression data for livers of rats exposed to 4 chemicals (crude MCHM, neat MCHM, DMPT, p-toluidine) and kidneys of rats exposed to PPH. The project file includes normalized expression data (GeneChip Rat 230 2.0 Array) using 7 different pre-processing methods (RMA, GCRMA, MAS5.0, MAS5.0_noA calls, PLIER, PLIER16, and PLIER16_noA calls); differentially expressed probe-sets detected by William's method (p<0.05, and minimum fold change of 1.5); probeset-level and pathway-level BMD and BMDL values from transcriptomic dose-response modeling. This dataset is associated with the following publication: Mezencev, R., and S. Auerbach. The sensitivity of transcriptomics BMD modeling to the methods used for microarray data normalization. PLOS ONE. Public Library of Science, San Francisco, CA, USA, 15(5): e0232955, (2020).