The dataset shows the results of the Running Fisher test between the TGx-DDI biomarker and each of the qPCR-generated gene lists from chemical treatments in TK6 cells. The results are divided into training and test sets. This dataset is associated with the following publication: Cho, E., J. Buick, A. Williams, R. Chen, H. Li, C. Corton, A. Fornace, J. Aubrecht, and C. Yauk. Assessment of the performance of the TGx-DDI biomarker to detect DNA damage-inducing agents using quantitative RT- PCR in TK6 cells. ENVIRONMENTAL AND MOLECULAR MUTAGENESIS. John Wiley & Sons, Inc, Hoboken, NJ, USA, 60(2): 122-133, (2019).

Dataset used to determine the predictive accuracy of the HDACi biomarker. This dataset is associated with the following publication: Cho, E., A. Rowan-Carroll, A. Williams, C. Corton, H. Li, A. Fornace, C. Hobbs, and C. Yauk. Development and Validation of the TGx-HDACi Transcriptomic Biomarker to Detect Histone Deacetylase Inhibitors in Human TK6 Cells. Archives of Toxicology. Springer, New York, NY, USA, 95(5): 1631-1645, (2021).

Datasets used in ORD-025118: Using a Gene Expression Biomarker to Identify DNA Damage-Inducing Agents in Microarray Profiles. This dataset is associated with the following publication: Corton, C., A. Williams, and C. Yauk. Using a Gene Expression Biomarker to Identify DNA Damage-Inducing Agents in Microarray Profiles. ENVIRONMENTAL AND MOLECULAR MUTAGENESIS. John Wiley & Sons, Inc, Hoboken, NJ, USA, 59(9): 772-784, (2018).

The links provided include the DrugMatrix-Affymetrix study, the TG-GATES study, and the TempO-Seq S1500+ study. This dataset is associated with the following publication: Lewis, R., T. Hill III, and C. Corton. A set of six Gene expression biomarkers and their thresholds identify rat liver tumorigens in short-term assays. TOXICOLOGY. Elsevier Science Ltd, New York, NY, USA, 443: 152547, (2020).

The dataset contains chemicals that were predicted to activate p53 using the biomarker or using a high throughput assay for p53. This dataset is associated with the following publication: Corton, J., K. Witt, and C. Yauk. Identification of p53 Activators in a Human Microarray Compendium. CHEMICAL RESEARCH IN TOXICOLOGY. American Chemical Society, Washington, DC, USA, 32(9): 1748-1759, (2019).

Gene Expression Omnibus and ArrayExpress archived study numbers of microarray data used in the study. This dataset is associated with the following publication: Rooney, J., B. Chorley, S. Hiemstra, S. Wink, X. Wang, D. Bell, B. van de Water, and J. Corton. Mining a human transcriptome database for chemical modulators of Nrf2 (PLOS ONE). PLoS ONE. Public Library of Science, San Francisco, CA, USA, 15(9): e0239367, (2020).

The data presented here support the application of the Stemina devTOXqP platform for predictive toxicology and further demonstrate its value in ToxCast as a novel resource that can generate testable hypotheses aimed at characterizing potential pathways for teratogenicity and HTS prioritization of environmental chemicals for an exposure-based assessment of developmental hazard. The dataset from the Stemina (STM) assay is annotated in the ToxCast portfolio as STM. Major findings from the analysis of ToxCast_STM dataset include (1) 19% of 1065 chemicals yielded a prediction of developmental toxicity, (2) assay performance reached 79%-82% accuracy with high specificity (> 84%) but modest sensitivity (< 67%) when compared with in vivo animal models of human prenatal developmental toxicity, (3) sensitivity improved as more stringent weights of evidence requirements were applied to the animal studies, and (4) statistical analysis of the most potent chemical hits on specific biochemical targets in ToxCast revealed positive and negative associations with the STM response, providing insights into the mechanistic underpinnings of the targeted endpoint and its biological domain. The results of this study will be useful to improving our ability to predict in vivo developmental toxicants based on in vitro data and in silico models. This dataset is associated with the following publication: Zurlinden, T., K. Saili, N. Rush, P. Kothiya, R. Judson, K. Houck, E. Hunter, N. Baker, J. Palmer, R. Thomas, and T. Knudsen. Profiling the ToxCast Library With a Pluripotent Human (H9) Stem Cell Line-Based Biomarker Assay for Developmental Toxicity. TOXICOLOGICAL SCIENCES. Society of Toxicology, RESTON, VA, 174(2): 189-209, (2020).

Previous work identified a ‘cytotoxic burst’ (CTB) phenomenon wherein large numbers of the ToxCast assays begin to respond at or near test chemical concentrations that elicit cytotoxicity, and a statistical approach to defining the bounds of the CTB was developed. To focus AOP development on the molecular targets corresponding to ToxCast assays indicating pathway-specific effects, we conducted a meta-analysis to identify which assays most frequently respond at concentrations below the CTB. A preliminary list of potentially important, target-specific assays was determined by ranking assays by the fraction of chemical hits below the CTB compared to the number of chemicals tested. Additional priority assays were identified using a diagnostic-odds-ratio approach which gives greater ranking to assays with high specificity but low responsivity. Combined, the two prioritization methods identified several novel targets (e.g., peripheral benzodiazepine and progesterone receptors) to prioritize for AOP development, and affirmed the importance of a number of existing AOPs aligned with ToxCast targets (e.g., thyroperoxidase, estrogen receptor, aromatase). This dataset is associated with the following publication: Fay, K., J. Swintek, D. Villeneuve, S. Edwards, M. Nelms, B. Blackwell, and G. Ankley. Differentiating pathway-specific from non-specific effects in high-throughput toxicity data: A foundation for prioritizing adverse outcome pathway development. TOXICOLOGICAL SCIENCES. Society of Toxicology, RESTON, VA, 163(2): 500-515, (2018).

환경영향평가 및 사후환경영향조사 수질정보(농약조사정보) 조사값* 데이터의 [조사구분(IVSTG_GB_CD)]는"공지사항>자료"메뉴의 2번 "환경평가 모니터링 및 활용데이터 개방 DB_코드정의서(2021.07.01기준)"문건의 첨부파일을 참조해 주세요. 실제 골프장 등 사업에 사용되는 농약류 항목의 통계 및 수계에 잔존하는 오염물질의 종류 및 농도 등에 관한 통계자료로 활용

The dataset that was evaluated in this approach was taken from Wambaugh et al [29] who filtered the Tox21 library to reflect substances with similar uses to those in NHANES. The zip file contains the supplementary information being provided for the re-analysis performed in this dataset. There was no specific code as such developed for the analysis aside from using KNIME to help combine different outputs from different tools including Leadscope in order to arrive at the counts reflected in Table 2 of the manuscript. Instead of this very laborious approach, we re-did the analysis using Toxtree alone and streamlined the processing of the outcomes with R. This is documented in the supplementary information file. List of files: SMARTS Toxtree schemes use to identify carbamates, OPs and steroids Carbamates.tml OPs.tml Steroids.tml R code used to manipulate the various outputs derived from processing the associated sdf through the Kroes, specific Toxtree schemes and Cramer scheme within Toxtree TTC_HTTK.R R data file HTTK_TTC_070218.RData sdf file used in the analysis HTTK_7K_mod_kekule.sdf. This dataset is associated with the following publication: Patlewicz, G., J. Wambaugh, S. Felter, T. Simon, and R. Becker. Utilizing Threshold of Toxicological Concern (TTC) with High Throughput Exposure Predictions (HTE) as a Risk-Based Prioritization Approach for thousands of chemicals. Computational Toxicology. Elsevier B.V., Amsterdam, NETHERLANDS, 7: 58-67, (2018).