All of the code used to analyze and report the data as well as build confidence in the approach is available as a supplementary RMarkdown report, and a tool to derive PODBioactivity and PODRead-Across is available as an RShiny web-application. The data used in the workflow are either available on public databases or are included in the supplementary material to allow for reproducibility of results. The results and output of the workflow (i.e., chemical info, PODs, etc.) are provided in the supplementary material (available as a download from the journal article). This dataset is associated with the following publication: Beal, M., M. Gagne, S. Kulkarni, G. Patlewicz, R. Thomas, and T. Barton-Maclaren. Implementing in vitro Bioactivity Data to Modernize Priority Setting of Chemical Inventories. ALTEX. Society ALTEX Edition, Kuesnacht, SWITZERLAND, 39(1): 123-139, (2022).

Dataset for Jesse Rogers et al., 'Integrating Transcriptomic and Targeted New Approach Methodologies into a Tiered Framework for Chemical Bioactivity Screening' published in Environmental Health Perspectives, Vol 133, Issue 6, 067013, June 2025. DOI: https://doi.org/10.1289/EHP16024, PMC12165737 R scripts for reproducing all analyses are available on Github (https://github.com/USEPA/CompTox-HTTr-RCAS). All sequencing data are available via the Gene Expression Omnibus repository (accessionnumbers GSE274318 for U-2 OS and GSE284321 for HepaRG). High-throughput screening assay data are available from InvitroDB via download 29 or the USEPA CompTox Chemicals Dashboard(https://comptox.epa.gov/dashboard/). This dataset is associated with the following publication: Rogers, J., J. Bundy, J. Harrill, R. Judson, K. Friedman, and L. Everett. Integrating Transcriptomic and Targeted New Approach Methodologies into a Tiered Framework for Chemical Bioactivity Screening. ENVIRONMENTAL HEALTH PERSPECTIVES. National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, NC, USA, 133(6): 067013, (2025).

- 물질별 국제 화학 식별자(International Chemical Identifier, InChI) 표기법 등의 기본정보를 비롯하여, 유전독성 속성별 실험에 관한 가공 데이터와 과학적 근거 및 기타 메타데이터를 포함하는 데이터

*********** Note to Josh Harrill- I don't have a copy of the final manuscript so could you please add the description of this dataset (just delete this comment and enter or cut and paste and then it should be ready to route by clicking on 'Submit for Review' button above) **********. This dataset is associated with the following publication: Nyffeler, J., D. Haggard, C. Willis, W. Setzer, R. Judson, K. Paul-Friedman, L. Everett, and J. Harrill. Comparison of Approaches for Determining Bioactivity Hits from High-Dimensional Profiling Data. SLAS Discovery. SAGE Publications, THOUSAND OAKS, CA, USA, 26(2): 292-308, (2021).

Data for Reardon AJF, et al., From vision toward best practices: Evaluating in vitro transcriptomic points of departure for application in risk assessment using a uniform workflow. Front. Toxicol. 5:1194895. doi: 10.3389/ftox.2023.1194895. PMC10242042. This dataset is associated with the following publication: Reardon, A., R. Farmahin, A. Williams, M. Meier, G. Addicks, C. Yauk, G. Matteo, E. Atlas, J. Harrill, L. Everett, I. Shah, R. Judson, S. Ramaiahgari, S. Ferguson, and T. Barton-Maclaren. From vision toward best practices: Evaluating in vitro transcriptomic points of departure for application in risk assessment using a uniform workflow. Frontiers in Toxicology. Frontiers, Lausanne, SWITZERLAND, 5: 1194895, (2023).

The files and .R scripts here are provided to aid in understanding and reproduction of the analyses in the submitted paper, "Examining the Utility of In Vitro Bioactivity as a Protective Point of Departure: A Case Study." The scripts in the .R folder reference source files in the other folders. This dataset is associated with the following publication: Friedman, K., M. Gagne, L. Loo, P. Karamertzanis, T. Netzeva, T. Sobanski, J. Franzosa, A. Richard, R. Lougee, A. Gissi, J.J. Lee, M. Angrish, J. Dorne, S. Foster, K. Raffaele, T. Bahadori, M. Gwinn, J. Lambert, M. Whelan, M. Rasenberg, T. Barton-Maclaren, and R. Thomas. Utility of In Vitro Bioactivity as a Lower Bound Estimate of In Vivo Adverse Effect Levels and in Risk-Based Prioritization. TOXICOLOGICAL SCIENCES. Society of Toxicology, RESTON, VA, 173(1): 202-225, (2020).

EPA researchers have been using advances in computational toxicology to address lack of data on the thousands of chemicals. EPA released chemical data on 1,800 chemicals. The 1,800 chemicals were screened in more than 800 rapid, automated tests (called high-throughput screening assays) to determine potential human health effects. The data is available through the interactive Chemical Safety for Sustainability Dashboards (iCSS dashboard) and the complete data sets are also available for download.

Dataset for "Matthew N. Newmeyer, Qinfan Lyu, Jon R. Sobus, Antony J. Williams, Keeve E. Nachman, and Carsten Prasse. Environmental Science & Technology 2024 58 (27), 12135-12146, DOI: 10.1021/acs.est.4c02934". This dataset is associated with the following publication: Newmeyer, M., Q. Lyu, J. Sobus, A. Williams, K. Nachman, and C. Prasse. Combining Nontargeted Analysis with Computer-Based Hazard Comparison Approaches to Support Prioritization of Unregulated Organic Contaminants in Biosolids. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 58(27): 12135-12146, (2024).

Data and code for "Grace Patlewicz, Ann M. Richard, Antony J. Williams, Richard S. Judson, Russell S. Thomas, Towards reproducible structure-based chemical categories for PFAS to inform and evaluate toxicity and toxicokinetic testing, Computational Toxicology, Volume 24, 2022, 100250, ISSN 2468-1113, https://doi.org/10.1016/j.comtox.2022.100250.". This dataset is associated with the following publication: Patlewicz, G., A. Richard, A. Williams, R. Judson, and R. Thomas. Towards reproducible structure-based chemical categories for PFAS to inform and evaluate toxicity and toxicokinetic testing.. Computational Toxicology. Elsevier B.V., Amsterdam, NETHERLANDS, 24: 100250, (2022).

시험연구비 첨부파일 정보를 조회한다.