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Vargas-Rojas L, Ting TC, Rainey KM, Reynolds M, Wang DR. AgTC and AgETL: open-source tools to enhance data collection and management for plant science research. Front Plant Sci 2024; 15:1265073. [PMID: 38450403 PMCID: PMC10915008 DOI: 10.3389/fpls.2024.1265073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 01/30/2024] [Indexed: 03/08/2024]
Abstract
Advancements in phenotyping technology have enabled plant science researchers to gather large volumes of information from their experiments, especially those that evaluate multiple genotypes. To fully leverage these complex and often heterogeneous data sets (i.e. those that differ in format and structure), scientists must invest considerable time in data processing, and data management has emerged as a considerable barrier for downstream application. Here, we propose a pipeline to enhance data collection, processing, and management from plant science studies comprising of two newly developed open-source programs. The first, called AgTC, is a series of programming functions that generates comma-separated values file templates to collect data in a standard format using either a lab-based computer or a mobile device. The second series of functions, AgETL, executes steps for an Extract-Transform-Load (ETL) data integration process where data are extracted from heterogeneously formatted files, transformed to meet standard criteria, and loaded into a database. There, data are stored and can be accessed for data analysis-related processes, including dynamic data visualization through web-based tools. Both AgTC and AgETL are flexible for application across plant science experiments without programming knowledge on the part of the domain scientist, and their functions are executed on Jupyter Notebook, a browser-based interactive development environment. Additionally, all parameters are easily customized from central configuration files written in the human-readable YAML format. Using three experiments from research laboratories in university and non-government organization (NGO) settings as test cases, we demonstrate the utility of AgTC and AgETL to streamline critical steps from data collection to analysis in the plant sciences.
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Affiliation(s)
- Luis Vargas-Rojas
- Department of Agronomy, Purdue University, West Lafayette, IN, United States
| | - To-Chia Ting
- Department of Agronomy, Purdue University, West Lafayette, IN, United States
| | - Katherine M. Rainey
- Department of Agronomy, Purdue University, West Lafayette, IN, United States
| | - Matthew Reynolds
- Wheat Physiology Group, International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Diane R. Wang
- Department of Agronomy, Purdue University, West Lafayette, IN, United States
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Feshuk M, Kolaczkowski L, Dunham K, Davidson-Fritz SE, Carstens KE, Brown J, Judson RS, Paul Friedman K. The ToxCast pipeline: updates to curve-fitting approaches and database structure. Front Toxicol 2023; 5:1275980. [PMID: 37808181 PMCID: PMC10552852 DOI: 10.3389/ftox.2023.1275980] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Accepted: 09/08/2023] [Indexed: 10/10/2023] Open
Abstract
Introduction: The US Environmental Protection Agency Toxicity Forecaster (ToxCast) program makes in vitro medium- and high-throughput screening assay data publicly available for prioritization and hazard characterization of thousands of chemicals. The assays employ a variety of technologies to evaluate the effects of chemical exposure on diverse biological targets, from distinct proteins to more complex cellular processes like mitochondrial toxicity, nuclear receptor signaling, immune responses, and developmental toxicity. The ToxCast data pipeline (tcpl) is an open-source R package that stores, manages, curve-fits, and visualizes ToxCast data and populates the linked MySQL Database, invitrodb. Methods: Herein we describe major updates to tcpl and invitrodb to accommodate a new curve-fitting approach. The original tcpl curve-fitting models (constant, Hill, and gain-loss models) have been expanded to include Polynomial 1 (Linear), Polynomial 2 (Quadratic), Power, Exponential 2, Exponential 3, Exponential 4, and Exponential 5 based on BMDExpress and encoded by the R package dependency, tcplfit2. Inclusion of these models impacted invitrodb (beta version v4.0) and tcpl v3 in several ways: (1) long-format storage of generic modeling parameters to permit additional curve-fitting models; (2) updated logic for winning model selection; (3) continuous hit calling logic; and (4) removal of redundant endpoints as a result of bidirectional fitting. Results and discussion: Overall, the hit call and potency estimates were largely consistent between invitrodb v3.5 and 4.0. Tcpl and invitrodb provide a standard for consistent and reproducible curve-fitting and data management for diverse, targeted in vitro assay data with readily available documentation, thus enabling sharing and use of these data in myriad toxicology applications. The software and database updates described herein promote comparability across multiple tiers of data within the US Environmental Protection Agency CompTox Blueprint.
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Affiliation(s)
- M. Feshuk
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, Durham, NC, United States
| | - L. Kolaczkowski
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, Durham, NC, United States
- National Student Services Contractor, Oak Ridge Associated Universities, Oak Ridge, TN, United States
| | - K. Dunham
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, Durham, NC, United States
- National Student Services Contractor, Oak Ridge Associated Universities, Oak Ridge, TN, United States
| | - S. E. Davidson-Fritz
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, Durham, NC, United States
| | - K. E. Carstens
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, Durham, NC, United States
| | - J. Brown
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, Durham, NC, United States
| | - R. S. Judson
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, Durham, NC, United States
| | - K. Paul Friedman
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, Durham, NC, United States
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Sawant S, Gurley SA, Overman RG, Sharak A, Mudrak SV, Oguin T, Sempowski GD, Sarzotti-Kelsoe M, Walter EB, Xie H, Pasetti MF, Moody MA, Tomaras GD. H3N2 influenza hemagglutination inhibition method qualification with data driven statistical methods for human clinical trials. Front Immunol 2023; 14:1155880. [PMID: 37090729 PMCID: PMC10117676 DOI: 10.3389/fimmu.2023.1155880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 03/09/2023] [Indexed: 04/09/2023] Open
Abstract
Introduction Hemagglutination inhibition (HAI) antibody titers to seasonal influenza strains are important surrogates for vaccine-elicited protection. However, HAI assays can be variable across labs, with low sensitivity across diverse viruses due to lack of standardization. Performing qualification of these assays on a strain specific level enables the precise and accurate quantification of HAI titers. Influenza A (H3N2) continues to be a predominant circulating subtype in most countries in Europe and North America since 1968 and is thus a focus of influenza vaccine research. Methods As a part of the National Institutes of Health (NIH)-funded Collaborative Influenza Vaccine Innovation Centers (CIVICs) program, we report on the identification of a robust assay design, rigorous statistical analysis, and complete qualification of an HAI assay using A/Texas/71/2017 as a representative H3N2 strain and guinea pig red blood cells and neuraminidase (NA) inhibitor oseltamivir to prevent NA-mediated agglutination. Results This qualified HAI assay is precise (calculated by the geometric coefficient of variation (GCV)) for intermediate precision and intra-operator variability, accurate calculated by relative error, perfectly linear (slope of -1, R-Square 1), robust (<25% GCV) and depicts high specificity and sensitivity. This HAI method was successfully qualified for another H3N2 influenza strain A/Singapore/INFIMH-16-0019/2016, meeting all pre-specified acceptance criteria. Discussion These results demonstrate that HAI qualification and data generation for new influenza strains can be achieved efficiently with minimal extra testing and development. We report on a qualified and adaptable influenza serology method and analysis strategy to measure quantifiable HAI titers to define correlates of vaccine mediated protection in human clinical trials.
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Affiliation(s)
- Sheetal Sawant
- Center for Human Systems Immunology, Department of Surgery, Duke University, Durham, NC, United States
- Duke Human Vaccine Institute, Duke University, Durham, NC, United States
- Department of Immunology, Duke University, Durham, NC, United States
| | - Sarah Anne Gurley
- Center for Human Systems Immunology, Department of Surgery, Duke University, Durham, NC, United States
- Duke Human Vaccine Institute, Duke University, Durham, NC, United States
- Department of Immunology, Duke University, Durham, NC, United States
| | - R. Glenn Overman
- Center for Human Systems Immunology, Department of Surgery, Duke University, Durham, NC, United States
- Duke Human Vaccine Institute, Duke University, Durham, NC, United States
- Department of Immunology, Duke University, Durham, NC, United States
| | - Angelina Sharak
- Center for Human Systems Immunology, Department of Surgery, Duke University, Durham, NC, United States
- Duke Human Vaccine Institute, Duke University, Durham, NC, United States
- Department of Immunology, Duke University, Durham, NC, United States
| | - Sarah V. Mudrak
- Center for Human Systems Immunology, Department of Surgery, Duke University, Durham, NC, United States
- Duke Human Vaccine Institute, Duke University, Durham, NC, United States
- Department of Immunology, Duke University, Durham, NC, United States
| | - Thomas Oguin
- Duke Human Vaccine Institute, Duke University, Durham, NC, United States
| | | | - Marcella Sarzotti-Kelsoe
- Center for Human Systems Immunology, Department of Surgery, Duke University, Durham, NC, United States
- Duke Human Vaccine Institute, Duke University, Durham, NC, United States
- Department of Immunology, Duke University, Durham, NC, United States
| | - Emmanuel B. Walter
- Duke Human Vaccine Institute, Duke University, Durham, NC, United States
- Department of Pediatrics, Duke University, Durham, NC, United States
- Duke Global Health Institute, Duke University, Durham, NC, United States
| | - Hang Xie
- Division of Viral Products, Office of Vaccines Research and Review, Center for Biologics Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD, United States
| | - Marcela F. Pasetti
- Department of Pediatrics, University of Maryland School of Medicine, Baltimore, MD, United States
- Center for Vaccine Development, University of Maryland School of Medicine, Baltimore, MD, United States
| | - M. Anthony Moody
- Duke Human Vaccine Institute, Duke University, Durham, NC, United States
- Department of Immunology, Duke University, Durham, NC, United States
- Department of Pediatrics, Duke University, Durham, NC, United States
| | - Georgia D. Tomaras
- Center for Human Systems Immunology, Department of Surgery, Duke University, Durham, NC, United States
- Duke Human Vaccine Institute, Duke University, Durham, NC, United States
- Department of Immunology, Duke University, Durham, NC, United States
- Duke Global Health Institute, Duke University, Durham, NC, United States
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