Developing tools for high resolution mass spectrometry-based screening via the EPA’s CompTox Dashboard
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Non-targeted and suspect screening studies using high resolution mass spectrometry (HRMS) have revolutionized how chemicals are detected in complex matrices. However, data processing remains challenging due to the vast number of chemicals detected in samples, software and computational requirements of data processing, and inherent uncertainty in confidently identifying chemicals from candidate lists. The US EPA has developed functionality within the CompTox Chemistry Dashboard (https://comptox.epa.gov) to address challenges related to data processing and analysis in HRMS. These tools include the generation of “MS-Ready” structures to optimize database searching, retention time prediction for candidate reduction, consensus ranking using chemical metadata, and in silico MS/MS fragmentation prediction for spectral matching. Combining these tools into a comprehensive workflow improves certainty in candidate identification. This presentation will introduce the tools and combined workflow including visualization and access via the Chemistry Dashboard. These tools, data, and visualization approaches within an open chemistry resource provide a freely available software tool to support structure identification and non-targeted analysis. This abstract does not necessarily represent the views or policies of the U.S. Environmental Protection Agency.
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![Innovative Research for a Sustainable Futurewww.epa.gov/research
Developing tools for high resolution mass spectrometry-based screening via the EPA’s
CompTox Dashboard
Andrew D. McEachran1,2*, Kamel Mansouri3, Hussein Al-Ghoul4, Alex Chao4, Chris Grulke2, Jon R. Sobus5, and Antony J. Williams2
1Oak Ridge Institute of Science and Education (ORISE) Research Participant, Research Triangle Park, NC
2U.S. Environmental Protection Agency, Office of Research and Development, National Center for Computational Toxicology, Research Triangle Park, NC
3Integrated Laboratory Systems, Inc., Morrisville, NC | 4Oak Ridge Associated Universities, Research Triangle Park, NC
5U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, Research Triangle Park, NC
* mceachran.andrew@epa.gov l ORCiD: 0000-0003-1423-330X
Problem Definition and Goals
The CompTox Dashboard
MS-Ready Structures for Database Searching MS/MS Data in NTA/SSA
References
Acknowledgements
1. Williams, AJ, et al. 2017. The CompTox Chemistry Dashboard: a community data resource for environmental chemistry. J
Cheminf. https://doi.org/10.1186/s13321-017-0247-6
2. McEachran, AD, et al. 2017. Identifying known unknowns using the US EPA's CompTox Chemistry Dashboard. Anal. Bioanal.
Chem. 409(7): 1729-1735. doi:10.1007/s00216-016-0139-z
3. McEachran, AD, et al. 2017. A comparison of three chromatographic retention time prediction models. Talanta.
https://doi.org/10.1016/j.talanta.2018.01.022
4. McEachran, AD, et al. 2018. “MS-Ready” structures for non-targeted high-resolution mass spectrometry screening studies. J
Cheminf. Accepted for publication.
5. Allen, et al. 2015. Competitive fragmentation modeling of ESI-MS/MS spectra for putative metabolite identification.
Metabolomics. https://doi.org/10.1007/s11306-014-0676-4
6. Newton, SR, et al. 2018. Suspect screening and non-targeted analysis of drinking water using point-of-use filters. Environ Poll.
https://doi.org/10.1016/j.envpol.2017.11.033
Applications
The authors would like to thank Emma Schymanski and Christoph Ruttkies for their valuable contributions on the MS-Ready Structures,
associated publication, and integration to MetFrag. The authors would also like to thank Reza Aalizadeh and Nikolaos Thomaidis for use
of the UOA-RTI software for retention time index prediction (manuscript submitted for publication). The authors would like to
acknowledge the Dashboard development team for their efforts in surfacing all functionality. This work was supported in part by an
appointment to the ORISE Research Participation Program at the Office of Research and Development, U.S. EPA, through an
interagency agreement between the US EPA and DOE.
This presentation does not necessarily represent the views or policies of the U.S. Environmental Protection Agency.
Chemical metadata for ranking
Problem: Non-targeted and suspect screening studies using high
resolution mass spectrometry (HRMS) have revolutionized the detection of
chemicals in complex matrices. However, data processing remains
challenging due to the vast number of chemicals detected in samples,
software and computational requirements of data processing, and inherent
uncertainty in confidently identifying chemicals from candidate lists.
Goals: Develop tools, data, and visualization approaches within an open
chemistry resource to provide a freely available software tool to support
structure identification and non-targeted analysis.
Home page. The CompTox Dashboard (https://comptox.epa.gov) is a comprehensive
chemistry resource containing chemistry data on more than 760,000 chemical substances.
(Left and Below) To facilitate database searching, structures
in DSSTox were processed into their “MS-Ready” form [4].
This processing workflow removes salts and stereochemistry
and separates components of mixtures while retaining
linkages to the original structure and substance. This enables
the form of a structure observed via HRMS to be related to all
variants of a structure in the database.
(Left) The results of an MS-Ready query include
all those substances where one component of a
substance matches the input query terms [4]. In
this example, C10H14N2 returns single component
chemicals, mixtures, and salts. Returning all
linked DTXSIDs enables access to varied
metadata.
Percentage of chemicals identified using data source ranking
combining multiple metadata sources. Total n=1748 unique
chemicals from the ENTACT trial and CASMI 2016 contest (training
and test sets). DS=DSSTox Data Sources, PC= PubChem Data
Sources, PM=PubMed Reference Counts, STOFF= Presence in
STOFF-IDENT, KEMI_half= Swedish Chemicals Agency (KEMI)
Market List, weighted by 0.5.
# Identified % of Total
#1 Hits 89 43%
Top 5 154 74%
Top 10 174 84%
Top 20 190 91%
# Identified % of Total
#1 Hits 154 74%
Top 5 195 94%
Top 10 198 95%
Top 20 202 97%
CFM-ID only CFM-ID +DSSTox Data Sources
Using CFM-ID command line tools [5], MS/MS spectra were predicted for >700,000 structures in
ESI+, ESI-, and EI mode (manuscript in prep). The ESI+/- data were used to analyze the CASMI
2016 datasets. Data below are structure identification ranks for the challenge set (n=208).
Retention Time Prediction
logP ChromGenius OPERA-RT
Combined Test and
Training Set (n=97)
R2 0.66 0.83 0.86
RMSE (min) 5.50 3.93 3.60
Absolute Mean Error
(min)
4.65 3.03 2.93
Suspect screening of drinking water using point of
use filters [6].
• Dashboard tools used for identification (data
source ranking and batch searching)
• Dashboard tools used for prioritization of
identified compounds (exposure, bioactivity)
EPA’s Non-targeted Analysis Collaborative Trial
(ENTACT):
• MS-Ready structures underpinning analyses
and provided to participants, improving
accessibility to data
• In-house analysis utilizing data source
ranking, metadata in Dashboard
0 2 0 0 4 0 0 6 0 0 8 0 0 1 0 0 0
0
2 0 0
4 0 0
6 0 0
8 0 0
1 0 0 0
A C N M e th o d
E xp e rim e n ta l R T I
PredictedRTI
(Top) Results from [2] indicated that an in-house QSRR model
termed OPERA-RT performed on par with the commercial
software ACD/ChromGenius.
(Bottom) Evaluation of retention time index (RTI) modeling
developed by Aalizadeh et al (submitted manuscript).
Experimental vs. predicted RTI value from ENTACT mixtures.
Batch Search Page. The Batch Search page enables users to query the underlying
database using data collected from HRMS studies as molecular formulae or monoisotopic
masses (i.e. 10s to 100s at a time). Metadata, structural information, presence in chemical
lists, and many more pieces of data can be included in the download.
Single Chemical Page. A single chemical page includes chemical structures,
experimental and predicted physicochemical and toxicity data, exposure data, and much
more.](https://image.slidesharecdn.com/mceachrananylposterdevelopingtoolsforhrmsscreening-180820121748/85/Developing-tools-for-high-resolution-mass-spectrometry-based-screening-via-the-EPA-s-CompTox-Dashboard-1-320.jpg)
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The iCSS Chemistry Dashboard is a publicly accessible dashboard provided by the National Center for Computation Toxicology at the US-EPA. It serves a number of purposes, including providing a chemistry database underpinning many of our public-facing projects (e.g. ToxCast and ExpoCast). The available data and searches provide a valuable path to structure identification using mass spectrometry as the source data. With an underlying database of over 720,000 chemicals, the dashboard has already been used to assist in identifying chemicals present in house dust. However, it can also be applied to many other purposes, e.g., the identification of agrochemicals in waste streams. This presentation will provide a review of the EPA’s platform and underlying algorithms used for the purpose of compound identification using high-resolution mass spectrometry data. We will also discuss progress towards a high-throughput non-targeted analysis platform for use by the mass spectrometry community. This abstract does not reflect U.S. EPA policy.Structure Identification Using High Resolution Mass Spectrometry Data and the...
Structure Identification Using High Resolution Mass Spectrometry Data and the...US Environmental Protection Agency (EPA), Center for Computational Toxicology and Exposure
Cheminformatics methods form an essential basis for providing analytical scientists with access to data, algorithms and workflows. There are an increasing number of free online databases (compound databases, spectral libraries, data repositories) and a rich collection of software approaches that can be used to support automated structure verification and elucidation, specifically for Nuclear Magnetic Resonance (NMR) and Mass Spectrometry (MS). This presentation will provide an overview of freely available data, tools, databases and approaches available to support chemical structure verification and elucidation and highlight some of the known issues regarding data quality and suggest approaches for resolving some of the issues. The importance of structure and spectral standards for data exchange will be discussed, especially with regard to how spectral data can be made openly available to the community via online tools and through scientific publishing. This work does not necessarily reflect U.S. EPA policy.Overview of open resources to support automated structure verification and e...
Overview of open resources to support automated structure verification and e...US Environmental Protection Agency (EPA), Center for Computational Toxicology and Exposure
High resolution mass spectrometry (HRMS) and non-targeted analysis (NTA) are advancing the identification of emerging contaminants in environmental matrices, improving the means by which exposure analyses can be conducted. However, confidence in structure identification of unknowns in NTA presents challenges to analytical chemists. Structure identification requires integration of complementary data types such as reference databases, fragmentation prediction tools, and retention time prediction models. The goal of this research is to optimize and implement structure identification functionality within the US EPA’s CompTox Chemistry Dashboard, an open chemistry resource and web application containing data for ~760,000 substances. Rank-ordering the number of sources associated with chemical records within the Dashboard (Data Source Ranking) improves the identification of unknowns by bringing the most likely candidate structures to the top of a search results list. Database searching has been further optimized with the generation of MS-Ready Structures. MS-Ready structures are de-salted, stripped of stereochemistry, and mixture separated to replicate the form of a chemical observed via HRMS. Functionality to conduct batch searching of molecular formulae and monoisotopic masses was designed and released to improve searching efforts. Finally, a scoring-based identification scheme was developed, optimized, and surfaced via the Dashboard using multiple data streams contained within the database underlying the Dashboard. The scoring-based identification scheme improved the identification of unknowns over previous efforts using data source ranking alone. Combining these steps within an open chemistry resource provides a freely available software tool for structure identification and NTA. This abstract does not necessarily represent the views or policies of the U.S. Environmental Protection Agency.Using the US EPA’s CompTox Chemistry Dashboard for structure identification a...
Using the US EPA’s CompTox Chemistry Dashboard for structure identification a...US Environmental Protection Agency (EPA), Center for Computational Toxicology and Exposure
As part of our efforts to develop a public platform to provide access to predictive models we have attempted to disentangle the influence of the quality versus quantity of data available to develop and validate QSAR models. Using a thorough manual review of the data underlying the well-known EPI Suite software, we developed automated processes for the validation of the data using a KNIME workflow. This includes: approaches to validate different chemical structure representations (e.g. molfile and SMILES), identifiers (chemical names and registry numbers), and methods to standardize the data into QSAR-consumable formats for modeling. Our efforts to quantify and segregate data into various quality categories has allowed us to thoroughly investigate the resulting models developed from these data slices, as well as allowing us to examine whether or not efforts into the development of large high-quality datasets has the expected pay-off in terms of prediction performance. Machine-learning approaches have been applied to create a series of models that have been used to generate predicted physicochemical and environmental parameters for over 700,000 chemicals. These data are available online via the EPA’s iCSS Chemistry Dashboard. This abstract does not reflect U.S. EPA policy.The EPA Online Prediction Physicochemical Prediction Platform to Support Envi...
The EPA Online Prediction Physicochemical Prediction Platform to Support Envi...US Environmental Protection Agency (EPA), Center for Computational Toxicology and Exposure
In the domain of chemistry one of the greatest benefits to publishing research is that data can be shared. Unfortunately, the vast majority of chemical structure data associated with scientific publications remain locked up in document form, primarily in PDF files or trapped on webpages. Despite the explosive growth of online chemical databases and the overall maturity of cheminformatics platforms, many barriers stifle the exchange of chemical structures via publications. These challenges include incomplete support by accepted standards (especially InChI) for advanced stereochemistry, organometallic compounds and generic “Markush” representations, the difference between human-readable and computer-readable forms of data, and challenges with the computer representation of chemical structures. To address these obstacles to chemical structure sharing, US EPA National Center for Computational Toxicology scientists are using a combination of cheminformatics applications and online repositories to distribute chemical structure data associated with their publications. This presentation will describe how EPA-NCCT chemical structure data that is amenable to indexing and distribution are shared and highlight the benefit of open data sharing for modeling, data integration, and increasing research impact. This abstract does not reflect U.S. EPA policy.Sharing chemical structures with peer reviewed publications
Sharing chemical structures with peer reviewed publications US Environmental Protection Agency (EPA), Center for Computational Toxicology and Exposure
The iCSS CompTox Dashboard is a publicly accessible dashboard provided by the National Center for Computation Toxicology at the US-EPA. It serves a number of purposes, including providing a chemistry database underpinning many of our public-facing projects (e.g. ToxCast and ExpoCast). The available data and searches provide a valuable path to structure identification using mass spectrometry as the source data. With an underlying database of over 720,000 chemicals, the dashboard has already been used to assist in identifying chemicals present in house dust. However, it can also be applied to many other purposes, e.g., the identification of agrochemicals in waste streams. This presentation will provide a review of the EPA’s platform and underlying algorithms used for the purpose of compound identification using high-resolution mass spectrometry data. In order to examine its performance for structure identification, especially in terms of rank-ordering database hits, we have compared it with the ChemSpider database, a well-regarded public database that has become one of the community standards for structure identification. The study has shown that the CompTox Dashboard outperforms ChemSpider in terms of structure identification and ranking providing improved outcomes for mass spectrometry analysis of “known unknowns”. Structure Identification Using High Resolution Mass Spectrometry Data and the...
Structure Identification Using High Resolution Mass Spectrometry Data and the...US Environmental Protection Agency (EPA), Center for Computational Toxicology and Exposure
The increasing popularity of high mass accuracy non-target mass spectrometry methods has yielded extensive identification efforts based on chemical compound databases. Candidate structures are often retrieved with either exact mass or molecular formula from large resources such as PubChem, ChemSpider or the EPA CompTox Chemistry Dashboard. Additional data (e.g. fragmentation, physicochemical properties, reference and data source information) is then used to select potential candidates, depending on the experimental context. However, these strategies require the presence of substances of interest in these compound databases, which is often not the case as no database can be fully inclusive. A prominent example with clear data gaps are surfactants, used in many products in our daily lives, yet often absent as discrete structures in compound databases. Linear alkylbenzene sulfonates (LAS) are a common, high use and high priority surfactant class that have highly complex transformation behaviour in wastewater. Despite extensive reports in the environmental literature, few of the LAS and none of the related transformation products were reported in any compound databases during an investigation into Swiss wastewater effluents, despite these forming the most intense signals. The LAS surfactant class will be used to demonstrate how the coupling of environmental observations with high resolution mass spectrometry and detailed literature data (expert knowledge) on the transformation of these species can be used to progressively “fill the gaps” in compound databases. The LAS and their transformation products have been added to the CompTox Chemistry Dashboard (https://comptox.epa.gov/) using a combination of “representative structures” and “related structures” starting from the structural information contained in the literature. By adding this information into a centralized open resource, future environmental investigations can now profit from the expert knowledge previously scattered throughout the literature. Note: This abstract does not reflect US EPA policy.Adding complex expert knowledge into chemical database and transforming surfa...
Adding complex expert knowledge into chemical database and transforming surfa...US Environmental Protection Agency (EPA), Center for Computational Toxicology and Exposure
Researchers at the EPA’s National Center for Computational Toxicology integrate advances in biology, chemistry, and computer science to examine the toxicity of chemicals and help prioritize chemicals for further research based on potential human health risks. The intention of this research program is to quickly evaluate thousands of chemicals for potential risk but with much reduced cost relative to historical approaches. This work involves computational and data driven approaches including high-throughput screening, modeling, text-mining and the integration of chemistry, exposure and biological data. We have developed a number of databases and applications that are delivering on the vision of developing a deeper understanding of chemicals and their effects on exposure and biological processes that are supporting a large community of scientists in their research efforts. This presentation will provide an overview of our work to bring together diverse large scale data from the chemical and biological domains, our approaches to integrate and disseminate these data, and the delivery of models supporting computational toxicology. This abstract does not reflect U.S. EPA policy.Delivering The Benefits of Chemical-Biological Integration in Computational T...
Delivering The Benefits of Chemical-Biological Integration in Computational T...US Environmental Protection Agency (EPA), Center for Computational Toxicology and Exposure
Read-across, a popular data gap filling technique, traditionally relies on a thorough expert assessment. This approach can lead to inconsistent predictions, has limitations in terms of the numbers of chemicals that can be evaluated and offers little insight into the generalizability of the approach or its performance. We sought to evaluate the baseline performance of read-across for a large set of chemicals in a systematic, objective and reproducible manner and to provide quantitative measures of uncertainty for the predictions derived. The approach developed, generalized read-across (GenRA), relies on chemical descriptor information and/or in vitro bioactivity data (from ToxCast high throughput screening data) to derive read-across predictions of effects observed in in vivo repeat-dose toxicity studies. A web-based GenRA tool was then developed anchored around the established category workflow and a dynamic grid interface structure. The default starting point is identifying source analogues with associated in vivo data on the basis of chemical fingerprints. The next step is to analyze the scope and quantity of available data (both in vitro and in vivo). The third step generates a data matrix in order to evaluate the analogues – in terms of their consistency and concordance of effects across the different toxicity effects. The final steps involve generating a GenRA prediction and exporting the predictions. Here we describe the functionality and features of the GenRA web application which has recently been released as a new feature in the US EPA CompTox Chemistry Dashboard. Users are able to search the dashboard for a substance of interest, browse available information including toxicity information and then derive GenRA predictions. GenRA offers a novel and practical means of being able to perform objective read-across that can be helpful in screening level hazard assessments. This abstract does not necessarily represent U.S. EPA policy.Translating research into practical tools: A case study of GenRA, a new read...
Translating research into practical tools: A case study of GenRA, a new read...US Environmental Protection Agency (EPA), Center for Computational Toxicology and Exposure
Non-targeted and suspect screening studies using high resolution mass spectrometry (HRMS) have revolutionized the detection of chemicals in complex matrices. However, data processing remains challenging due to the vast number of chemicals detected in samples, software and computational requirements of data processing, and inherent uncertainty in confidently identifying chemicals from candidate lists. The US EPA has developed functionality within the CompTox Chemicals Dashboard (https://comptox.epa.gov) to address challenges related to data processing and analysis in HRMS. These tools include the generation of “MS-Ready” structures to optimize database searching, retention time prediction for candidate reduction, consensus ranking using chemical metadata, and in silico MS/MS fragmentation prediction for spectral matching. Combining these tools into a comprehensive workflow improves certainty in candidate identification. This presentation will introduce the tools and combined workflow, including visualization and access via the CompTox Chemicals Dashboard. These tools, data, and visualization approaches within an open chemistry resource provides a publicly available software tool to support structure identification and non-targeted analyses. This abstract does not necessarily represent the views or policies of the U.S. Environmental Protection Agency.Chemical identification of unknowns in high resolution mass spectrometry usin...
Chemical identification of unknowns in high resolution mass spectrometry usin...US Environmental Protection Agency (EPA), Center for Computational Toxicology and Exposure
High resolution mass spectrometry (HRMS) and non-targeted analysis (NTA) are of increasing interest in chemical forensics for the identification of emerging contaminants and chemical signatures of interest. At the US Environmental Protection Agency, our research using HRMS for non-targeted and suspect screening analyses utilizes databases and cheminformatics approaches that are applicable to chemical forensics. The CompTox Chemistry Dashboard is an open chemistry resource and web-based application containing data for ~760,000 substances. Basic functionality for searching through the data is provided through identifier searches, such as systematic name, trade names and CAS Registry Numbers. Advanced Search capabilities supporting mass spectrometry include mass and formula-based searches, combined substructure-mass searches and searching experimental mass spectral data against predicted fragmentation spectra. A specific type of data mapping in the underpinning database, using “MS-Ready” structures, has proven to be a valuable approach for structure identification that links structures that can be identified via HRMS with related substances in the form of salts, and other multi-component mixtures that are available in commerce. This presentation will provide an overview of the CompTox Chemistry Dashboard and demonstrate its utility for supporting structure identification and NTA in chemical forensics. This abstract does not necessarily represent the views or policies of the U.S. Environmental Protection Agency.Applications of the US EPA’s CompTox Chemistry Dashboard to support structure...
Applications of the US EPA’s CompTox Chemistry Dashboard to support structure...US Environmental Protection Agency (EPA), Center for Computational Toxicology and Exposure
The U.S. Environmental Protection Agency (EPA) Computational Toxicology Program integrates advances in biology, chemistry, and computer science to help prioritize chemicals for further research based on potential human health risks. This work involves computational and data driven approaches that integrate chemistry, exposure and biological data. As an outcome of these efforts the National Center for Computational Toxicology (NCCT) has measured, assembled and delivered an enormous quantity and diversity of data for the environmental sciences including high-throughput in vitro screening data, in vivo and functional use data, exposure models and chemical databases with associated properties. A series of software applications and databases have been produced over the past decade to deliver these data but recent developments have focused on the development of a new software architecture that assembles the resources into a single platform. A new web application, the CompTox Chemistry Dashboard provides access to data associated with ~720,000 chemical substances. These data include experimental and predicted physicochemical property data, bioassay screening data associated with the ToxCast program, product and functional use information and a myriad of related data of value to environmental scientists.
The dashboard provides chemical-based searching based on chemical names, synonyms and CAS Registry Numbers. Flexible search capabilities allow for chemical identification based on non-targeted analysis studies using mass spectrometry. Chemical identification using both mass and formula-based searching utilizes rank-ordering of results via functional use statistics, thereby providing a solution to help prioritize chemicals for further review when detected in environmental media.
This presentation will provide an overview of the CompTox Dashboard, its capabilities for delivering data to the environmental toxicology community and how the architecture provides a foundation for the development of additional applications to support chemical risk assessment. This abstract does not reflect U.S. EPA policy.
The EPA Comptox Chemistry Dashboard: A Web-Based Data Integration Hub for Tox...
The EPA Comptox Chemistry Dashboard: A Web-Based Data Integration Hub for Tox...US Environmental Protection Agency (EPA), Center for Computational Toxicology and Exposure
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Chemical risk assessment is both time-consuming and difficult because it requires the assembly of data for chemicals generally distributed across multiple sources. The US EPA CompTox Chemistry Dashboard is a publicly accessible web-based application providing access to various data streams on ~760,000 chemical substances. These data include experimental and predicted physicochemical property data, bioassay screening data associated with the ToxCast program, consumer product and functional use information and a myriad of related data of value to environmental scientists and toxicologists. At this stage of development, the public dashboard provides access to almost 20 predicted physicochemical and environmental fate and transport endpoints with full transparency in terms of model performance. Experimental and predicted human and ecological toxicity data are also available, as are in vitro to in vivo extrapolation dosimetry predictions and predicted exposure and functional use. In parallel to the CompTox Chemistry Dashboard we are developing RapidTox, a web-based application that enables a rapid, flexible and transparent prioritization process for sets of chemicals using several previously used workflows focused on scoring of traditional risk metrics and the inclusion of alternative hazard and exposure estimates. This presentation will give an overview of the CompTox Chemistry Dashboard, RapidTox, our approaches to building transparent and open prediction models, and our efforts to provide access to real time predictions. This abstract does not necessarily represent U.S. EPA policy.US EPA CompTox Chemistry Dashboard as a source of data to fill data gaps for ...
US EPA CompTox Chemistry Dashboard as a source of data to fill data gaps for ...US Environmental Protection Agency (EPA), Center for Computational Toxicology and Exposure
Identification of unknowns in mass spectrometry based non-targeted analyses (NTA) requires the integration of complementary pieces of data to arrive at a confident, consensus structure. Researchers use chemical reference databases, spectral matching, fragment prediction tools, retention time prediction tools, and a variety of other data to arrive at tentative, probable, and confirmed, if possible, identifications. With the diverse, robust data contained within the US EPA’s CompTox Chemistry Dashboard (https://comptox.epa.gov), the goal of this research is to identify and implement a harmonized identification tool and workflow using previously generated chemistry data. Data has been compiled from product use, functional use prediction models, environmental media occurrence prediction models, and PubMed references, among other sources. We will report on our development of a visualization tool whereby users can visualize the relative contribution of identification-based metrics on a list of candidate structures and observe the greatest likelihood of occurrence. These data and visualization tools support NTA identification via the Dashboard and demonstrate an open, accessible tool for all users of HRMS data. This abstract does not necessarily represent the views or policies of the U.S. Environmental Protection Agency.Structure identification by Mass Spectrometry Non-Targeted Analysis using the...
Structure identification by Mass Spectrometry Non-Targeted Analysis using the...US Environmental Protection Agency (EPA), Center for Computational Toxicology and Exposure
EPA’s National Center for Computational Toxicology is developing automated workflows for curating large databases within the DSSTox project, and providing accurate linkages of data to chemical structures, exposure and hazard information. The data are made available via the EPA’s CompTox Chemistry Dashboard (https://comptox.epa.gov/dashboard), a publicly accessible website providing access to data for ~760,000 chemical substances, the majority of these represented as chemical structures. The web application delivers a wide array of computed and measured physicochemical properties, in vitro high-throughput screening data and in vivo toxicity data, as well as integrated chemical linkages to a growing list of literature, toxicology, and analytical chemistry websites. In addition, several specific search types are in development to directly support the mass spectrometry non-targeted screening community, enabling cohesive workflows to support data generation for the detection and assessment of environmental exposures to chemicals contained within DSSTox. The application provides access to segregated lists of chemicals that are of specific interest to relevant stakeholders, including, for example, scientists interested in Per- & Polyfluoroalkyl Substances (PFAS). Added lists include those sourced from the European Union as well as developed in-house and now containing thousands of chemicals. A procured testing library of hundreds of PFAS chemicals annotated into chemical categories has been integrated into the dashboard with a number of resulting benefits: a searchable database of chemical properties, with hazard and exposure predictions, and links to the open literature. This presentation will provide an overview of the dashboard, the developing library of PFAS chemicals and associated categorization, and new physicochemical property and environmental fate and transport QSAR prediction models developed for these chemicals. The application of the dashboard to support mass spectrometry non-targeted analysis studies for the identification of PFAS chemicals will also be reviewed. This abstract does not necessarily represent the views or policies of the U.S. Environmental Protection Agency.Accessing information for Per- & Polyfluoroalkyl Substances using the US EPA ...
Accessing information for Per- & Polyfluoroalkyl Substances using the US EPA ...US Environmental Protection Agency (EPA), Center for Computational Toxicology and Exposure
EPA’s National Center for Computational Toxicology is developing automated workflows for curating large databases and providing accurate linkages of data to chemical structures, exposure and hazard information. The data are being made available via the EPA’s CompTox Chemistry Dashboard (https://comptox.epa.gov/dashboard), a publicly accessible website providing access to data for almost 760,000 chemical substances, the majority of these represented as chemical structures. The web application delivers a wide array of computed and measured physicochemical properties, in vitro high-throughput screening data and in vivo toxicity data as well as integrated chemical linkages to a growing list of literature, toxicology, and analytical chemistry websites. In addition, several specific search types are in development to directly support the mass spectroscopy non-targeted screening community, who are generating important data for detecting and assessing environmental exposures to chemicals contained within DSSTox. The application provides access to segregated lists of chemicals that are of specific interests to relevant stakeholders including, for example, scientists interested in algal toxins and hydraulic fracturing chemicals. This presentation will provide an overview of the challenges associated with the curation of data from EPA’s December 2016 Hydraulic Fracturing Drinking Water Assessment Report that represented chemicals reported to be used in hydraulic fracturing fluids and those found in produced water. The data have been integrated into the dashboard with a number of resulting benefits: a searchable database of chemical properties, with hazard and exposure predictions, and open literature. The application of the dashboard to support mass spectrometry non-targeted analysis studies will also be reviewed. This abstract does not reflect U.S. EPA policy.Accessing information for chemicals in hydraulic fracturing fluids using the ...
Accessing information for chemicals in hydraulic fracturing fluids using the ...US Environmental Protection Agency (EPA), Center for Computational Toxicology and Exposure
The iCSS Chemistry Dashboard is a publicly accessible dashboard provided by the National Center for Computation Toxicology at the US-EPA. It serves a number of purposes, including providing a chemistry database underpinning many of our public-facing projects (e.g. ToxCast and ExpoCast). The available data and searches provide a valuable path to structure identification using mass spectrometry as the source data. With an underlying database of over 720,000 chemicals, the dashboard has already been used to assist in identifying chemicals present in house dust. However, it can also be applied to many other purposes, e.g., the identification of agrochemicals in waste streams. This presentation will provide a review of the EPA’s platform and underlying algorithms used for the purpose of compound identification using high-resolution mass spectrometry data. We will also discuss progress towards a high-throughput non-targeted analysis platform for use by the mass spectrometry community. This abstract does not reflect U.S. EPA policy.Structure Identification Using High Resolution Mass Spectrometry Data and the...
Structure Identification Using High Resolution Mass Spectrometry Data and the...US Environmental Protection Agency (EPA), Center for Computational Toxicology and Exposure
Cheminformatics methods form an essential basis for providing analytical scientists with access to data, algorithms and workflows. There are an increasing number of free online databases (compound databases, spectral libraries, data repositories) and a rich collection of software approaches that can be used to support automated structure verification and elucidation, specifically for Nuclear Magnetic Resonance (NMR) and Mass Spectrometry (MS). This presentation will provide an overview of freely available data, tools, databases and approaches available to support chemical structure verification and elucidation and highlight some of the known issues regarding data quality and suggest approaches for resolving some of the issues. The importance of structure and spectral standards for data exchange will be discussed, especially with regard to how spectral data can be made openly available to the community via online tools and through scientific publishing. This work does not necessarily reflect U.S. EPA policy.Overview of open resources to support automated structure verification and e...
Overview of open resources to support automated structure verification and e...US Environmental Protection Agency (EPA), Center for Computational Toxicology and Exposure
High resolution mass spectrometry (HRMS) and non-targeted analysis (NTA) are advancing the identification of emerging contaminants in environmental matrices, improving the means by which exposure analyses can be conducted. However, confidence in structure identification of unknowns in NTA presents challenges to analytical chemists. Structure identification requires integration of complementary data types such as reference databases, fragmentation prediction tools, and retention time prediction models. The goal of this research is to optimize and implement structure identification functionality within the US EPA’s CompTox Chemistry Dashboard, an open chemistry resource and web application containing data for ~760,000 substances. Rank-ordering the number of sources associated with chemical records within the Dashboard (Data Source Ranking) improves the identification of unknowns by bringing the most likely candidate structures to the top of a search results list. Database searching has been further optimized with the generation of MS-Ready Structures. MS-Ready structures are de-salted, stripped of stereochemistry, and mixture separated to replicate the form of a chemical observed via HRMS. Functionality to conduct batch searching of molecular formulae and monoisotopic masses was designed and released to improve searching efforts. Finally, a scoring-based identification scheme was developed, optimized, and surfaced via the Dashboard using multiple data streams contained within the database underlying the Dashboard. The scoring-based identification scheme improved the identification of unknowns over previous efforts using data source ranking alone. Combining these steps within an open chemistry resource provides a freely available software tool for structure identification and NTA. This abstract does not necessarily represent the views or policies of the U.S. Environmental Protection Agency.Using the US EPA’s CompTox Chemistry Dashboard for structure identification a...
Using the US EPA’s CompTox Chemistry Dashboard for structure identification a...US Environmental Protection Agency (EPA), Center for Computational Toxicology and Exposure
As part of our efforts to develop a public platform to provide access to predictive models we have attempted to disentangle the influence of the quality versus quantity of data available to develop and validate QSAR models. Using a thorough manual review of the data underlying the well-known EPI Suite software, we developed automated processes for the validation of the data using a KNIME workflow. This includes: approaches to validate different chemical structure representations (e.g. molfile and SMILES), identifiers (chemical names and registry numbers), and methods to standardize the data into QSAR-consumable formats for modeling. Our efforts to quantify and segregate data into various quality categories has allowed us to thoroughly investigate the resulting models developed from these data slices, as well as allowing us to examine whether or not efforts into the development of large high-quality datasets has the expected pay-off in terms of prediction performance. Machine-learning approaches have been applied to create a series of models that have been used to generate predicted physicochemical and environmental parameters for over 700,000 chemicals. These data are available online via the EPA’s iCSS Chemistry Dashboard. This abstract does not reflect U.S. EPA policy.The EPA Online Prediction Physicochemical Prediction Platform to Support Envi...
The EPA Online Prediction Physicochemical Prediction Platform to Support Envi...US Environmental Protection Agency (EPA), Center for Computational Toxicology and Exposure
In the domain of chemistry one of the greatest benefits to publishing research is that data can be shared. Unfortunately, the vast majority of chemical structure data associated with scientific publications remain locked up in document form, primarily in PDF files or trapped on webpages. Despite the explosive growth of online chemical databases and the overall maturity of cheminformatics platforms, many barriers stifle the exchange of chemical structures via publications. These challenges include incomplete support by accepted standards (especially InChI) for advanced stereochemistry, organometallic compounds and generic “Markush” representations, the difference between human-readable and computer-readable forms of data, and challenges with the computer representation of chemical structures. To address these obstacles to chemical structure sharing, US EPA National Center for Computational Toxicology scientists are using a combination of cheminformatics applications and online repositories to distribute chemical structure data associated with their publications. This presentation will describe how EPA-NCCT chemical structure data that is amenable to indexing and distribution are shared and highlight the benefit of open data sharing for modeling, data integration, and increasing research impact. This abstract does not reflect U.S. EPA policy.Sharing chemical structures with peer reviewed publications
Sharing chemical structures with peer reviewed publications US Environmental Protection Agency (EPA), Center for Computational Toxicology and Exposure
The iCSS CompTox Dashboard is a publicly accessible dashboard provided by the National Center for Computation Toxicology at the US-EPA. It serves a number of purposes, including providing a chemistry database underpinning many of our public-facing projects (e.g. ToxCast and ExpoCast). The available data and searches provide a valuable path to structure identification using mass spectrometry as the source data. With an underlying database of over 720,000 chemicals, the dashboard has already been used to assist in identifying chemicals present in house dust. However, it can also be applied to many other purposes, e.g., the identification of agrochemicals in waste streams. This presentation will provide a review of the EPA’s platform and underlying algorithms used for the purpose of compound identification using high-resolution mass spectrometry data. In order to examine its performance for structure identification, especially in terms of rank-ordering database hits, we have compared it with the ChemSpider database, a well-regarded public database that has become one of the community standards for structure identification. The study has shown that the CompTox Dashboard outperforms ChemSpider in terms of structure identification and ranking providing improved outcomes for mass spectrometry analysis of “known unknowns”. Structure Identification Using High Resolution Mass Spectrometry Data and the...
Structure Identification Using High Resolution Mass Spectrometry Data and the...US Environmental Protection Agency (EPA), Center for Computational Toxicology and Exposure
The increasing popularity of high mass accuracy non-target mass spectrometry methods has yielded extensive identification efforts based on chemical compound databases. Candidate structures are often retrieved with either exact mass or molecular formula from large resources such as PubChem, ChemSpider or the EPA CompTox Chemistry Dashboard. Additional data (e.g. fragmentation, physicochemical properties, reference and data source information) is then used to select potential candidates, depending on the experimental context. However, these strategies require the presence of substances of interest in these compound databases, which is often not the case as no database can be fully inclusive. A prominent example with clear data gaps are surfactants, used in many products in our daily lives, yet often absent as discrete structures in compound databases. Linear alkylbenzene sulfonates (LAS) are a common, high use and high priority surfactant class that have highly complex transformation behaviour in wastewater. Despite extensive reports in the environmental literature, few of the LAS and none of the related transformation products were reported in any compound databases during an investigation into Swiss wastewater effluents, despite these forming the most intense signals. The LAS surfactant class will be used to demonstrate how the coupling of environmental observations with high resolution mass spectrometry and detailed literature data (expert knowledge) on the transformation of these species can be used to progressively “fill the gaps” in compound databases. The LAS and their transformation products have been added to the CompTox Chemistry Dashboard (https://comptox.epa.gov/) using a combination of “representative structures” and “related structures” starting from the structural information contained in the literature. By adding this information into a centralized open resource, future environmental investigations can now profit from the expert knowledge previously scattered throughout the literature. Note: This abstract does not reflect US EPA policy.Adding complex expert knowledge into chemical database and transforming surfa...
Adding complex expert knowledge into chemical database and transforming surfa...US Environmental Protection Agency (EPA), Center for Computational Toxicology and Exposure
Researchers at the EPA’s National Center for Computational Toxicology integrate advances in biology, chemistry, and computer science to examine the toxicity of chemicals and help prioritize chemicals for further research based on potential human health risks. The intention of this research program is to quickly evaluate thousands of chemicals for potential risk but with much reduced cost relative to historical approaches. This work involves computational and data driven approaches including high-throughput screening, modeling, text-mining and the integration of chemistry, exposure and biological data. We have developed a number of databases and applications that are delivering on the vision of developing a deeper understanding of chemicals and their effects on exposure and biological processes that are supporting a large community of scientists in their research efforts. This presentation will provide an overview of our work to bring together diverse large scale data from the chemical and biological domains, our approaches to integrate and disseminate these data, and the delivery of models supporting computational toxicology. This abstract does not reflect U.S. EPA policy.Delivering The Benefits of Chemical-Biological Integration in Computational T...
Delivering The Benefits of Chemical-Biological Integration in Computational T...US Environmental Protection Agency (EPA), Center for Computational Toxicology and Exposure
Read-across, a popular data gap filling technique, traditionally relies on a thorough expert assessment. This approach can lead to inconsistent predictions, has limitations in terms of the numbers of chemicals that can be evaluated and offers little insight into the generalizability of the approach or its performance. We sought to evaluate the baseline performance of read-across for a large set of chemicals in a systematic, objective and reproducible manner and to provide quantitative measures of uncertainty for the predictions derived. The approach developed, generalized read-across (GenRA), relies on chemical descriptor information and/or in vitro bioactivity data (from ToxCast high throughput screening data) to derive read-across predictions of effects observed in in vivo repeat-dose toxicity studies. A web-based GenRA tool was then developed anchored around the established category workflow and a dynamic grid interface structure. The default starting point is identifying source analogues with associated in vivo data on the basis of chemical fingerprints. The next step is to analyze the scope and quantity of available data (both in vitro and in vivo). The third step generates a data matrix in order to evaluate the analogues – in terms of their consistency and concordance of effects across the different toxicity effects. The final steps involve generating a GenRA prediction and exporting the predictions. Here we describe the functionality and features of the GenRA web application which has recently been released as a new feature in the US EPA CompTox Chemistry Dashboard. Users are able to search the dashboard for a substance of interest, browse available information including toxicity information and then derive GenRA predictions. GenRA offers a novel and practical means of being able to perform objective read-across that can be helpful in screening level hazard assessments. This abstract does not necessarily represent U.S. EPA policy.Translating research into practical tools: A case study of GenRA, a new read...
Translating research into practical tools: A case study of GenRA, a new read...US Environmental Protection Agency (EPA), Center for Computational Toxicology and Exposure
Non-targeted and suspect screening studies using high resolution mass spectrometry (HRMS) have revolutionized the detection of chemicals in complex matrices. However, data processing remains challenging due to the vast number of chemicals detected in samples, software and computational requirements of data processing, and inherent uncertainty in confidently identifying chemicals from candidate lists. The US EPA has developed functionality within the CompTox Chemicals Dashboard (https://comptox.epa.gov) to address challenges related to data processing and analysis in HRMS. These tools include the generation of “MS-Ready” structures to optimize database searching, retention time prediction for candidate reduction, consensus ranking using chemical metadata, and in silico MS/MS fragmentation prediction for spectral matching. Combining these tools into a comprehensive workflow improves certainty in candidate identification. This presentation will introduce the tools and combined workflow, including visualization and access via the CompTox Chemicals Dashboard. These tools, data, and visualization approaches within an open chemistry resource provides a publicly available software tool to support structure identification and non-targeted analyses. This abstract does not necessarily represent the views or policies of the U.S. Environmental Protection Agency.Chemical identification of unknowns in high resolution mass spectrometry usin...
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High resolution mass spectrometry (HRMS) and non-targeted analysis (NTA) are of increasing interest in chemical forensics for the identification of emerging contaminants and chemical signatures of interest. At the US Environmental Protection Agency, our research using HRMS for non-targeted and suspect screening analyses utilizes databases and cheminformatics approaches that are applicable to chemical forensics. The CompTox Chemistry Dashboard is an open chemistry resource and web-based application containing data for ~760,000 substances. Basic functionality for searching through the data is provided through identifier searches, such as systematic name, trade names and CAS Registry Numbers. Advanced Search capabilities supporting mass spectrometry include mass and formula-based searches, combined substructure-mass searches and searching experimental mass spectral data against predicted fragmentation spectra. A specific type of data mapping in the underpinning database, using “MS-Ready” structures, has proven to be a valuable approach for structure identification that links structures that can be identified via HRMS with related substances in the form of salts, and other multi-component mixtures that are available in commerce. This presentation will provide an overview of the CompTox Chemistry Dashboard and demonstrate its utility for supporting structure identification and NTA in chemical forensics. This abstract does not necessarily represent the views or policies of the U.S. Environmental Protection Agency.Applications of the US EPA’s CompTox Chemistry Dashboard to support structure...
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The EPA CompTox Chemistry Dashboard provides access to data associated with ~760,000 chemical substances. The available data includes experimental and predicted physicochemical properties, environmental fate and transport data, in vivo and in silico toxicity data, in vitro bioassay data, exposure data and a variety of other types of information. The data are under continuous expansion and curation and the experimental data have been used to develop QSAR and QSPR models. A number of these models are available via a web interface so that users can submit a chemical structure and predict properties in real time. The dashboard also provides access to pre-compiled chemical lists and categories, including pesticides, and chemicals detected in the environment via non-targeted mass spectrometry analysis. The data are searchable using chemical identifiers (systematic names, trade names, CAS Registry Numbers), by structure, mass and formula. Batch searches allow for data associated with thousands of chemicals to be obtained in a few seconds, with just a few button clicks, and downloaded to the desktop. This presentation will provide an overview of the Dashboard and its applications to accessing source data associated with agriculturally related chemicals. This abstract does not necessarily represent the views or policies of the U.S. Environmental Protection Agency.Web-based access to experimental and predicted data for environmental fate, t...
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Developing tools for high resolution mass spectrometry-based screening via the EPA’s CompTox Dashboard
- 1. Innovative Research for a Sustainable Futurewww.epa.gov/research Developing tools for high resolution mass spectrometry-based screening via the EPA’s CompTox Dashboard Andrew D. McEachran1,2*, Kamel Mansouri3, Hussein Al-Ghoul4, Alex Chao4, Chris Grulke2, Jon R. Sobus5, and Antony J. Williams2 1Oak Ridge Institute of Science and Education (ORISE) Research Participant, Research Triangle Park, NC 2U.S. Environmental Protection Agency, Office of Research and Development, National Center for Computational Toxicology, Research Triangle Park, NC 3Integrated Laboratory Systems, Inc., Morrisville, NC | 4Oak Ridge Associated Universities, Research Triangle Park, NC 5U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, Research Triangle Park, NC * mceachran.andrew@epa.gov l ORCiD: 0000-0003-1423-330X Problem Definition and Goals The CompTox Dashboard MS-Ready Structures for Database Searching MS/MS Data in NTA/SSA References Acknowledgements 1. Williams, AJ, et al. 2017. The CompTox Chemistry Dashboard: a community data resource for environmental chemistry. J Cheminf. https://doi.org/10.1186/s13321-017-0247-6 2. McEachran, AD, et al. 2017. Identifying known unknowns using the US EPA's CompTox Chemistry Dashboard. Anal. Bioanal. Chem. 409(7): 1729-1735. doi:10.1007/s00216-016-0139-z 3. McEachran, AD, et al. 2017. A comparison of three chromatographic retention time prediction models. Talanta. https://doi.org/10.1016/j.talanta.2018.01.022 4. McEachran, AD, et al. 2018. “MS-Ready” structures for non-targeted high-resolution mass spectrometry screening studies. J Cheminf. Accepted for publication. 5. Allen, et al. 2015. Competitive fragmentation modeling of ESI-MS/MS spectra for putative metabolite identification. Metabolomics. https://doi.org/10.1007/s11306-014-0676-4 6. Newton, SR, et al. 2018. Suspect screening and non-targeted analysis of drinking water using point-of-use filters. Environ Poll. https://doi.org/10.1016/j.envpol.2017.11.033 Applications The authors would like to thank Emma Schymanski and Christoph Ruttkies for their valuable contributions on the MS-Ready Structures, associated publication, and integration to MetFrag. The authors would also like to thank Reza Aalizadeh and Nikolaos Thomaidis for use of the UOA-RTI software for retention time index prediction (manuscript submitted for publication). The authors would like to acknowledge the Dashboard development team for their efforts in surfacing all functionality. This work was supported in part by an appointment to the ORISE Research Participation Program at the Office of Research and Development, U.S. EPA, through an interagency agreement between the US EPA and DOE. This presentation does not necessarily represent the views or policies of the U.S. Environmental Protection Agency. Chemical metadata for ranking Problem: Non-targeted and suspect screening studies using high resolution mass spectrometry (HRMS) have revolutionized the detection of chemicals in complex matrices. However, data processing remains challenging due to the vast number of chemicals detected in samples, software and computational requirements of data processing, and inherent uncertainty in confidently identifying chemicals from candidate lists. Goals: Develop tools, data, and visualization approaches within an open chemistry resource to provide a freely available software tool to support structure identification and non-targeted analysis. Home page. The CompTox Dashboard (https://comptox.epa.gov) is a comprehensive chemistry resource containing chemistry data on more than 760,000 chemical substances. (Left and Below) To facilitate database searching, structures in DSSTox were processed into their “MS-Ready” form [4]. This processing workflow removes salts and stereochemistry and separates components of mixtures while retaining linkages to the original structure and substance. This enables the form of a structure observed via HRMS to be related to all variants of a structure in the database. (Left) The results of an MS-Ready query include all those substances where one component of a substance matches the input query terms [4]. In this example, C10H14N2 returns single component chemicals, mixtures, and salts. Returning all linked DTXSIDs enables access to varied metadata. Percentage of chemicals identified using data source ranking combining multiple metadata sources. Total n=1748 unique chemicals from the ENTACT trial and CASMI 2016 contest (training and test sets). DS=DSSTox Data Sources, PC= PubChem Data Sources, PM=PubMed Reference Counts, STOFF= Presence in STOFF-IDENT, KEMI_half= Swedish Chemicals Agency (KEMI) Market List, weighted by 0.5. # Identified % of Total #1 Hits 89 43% Top 5 154 74% Top 10 174 84% Top 20 190 91% # Identified % of Total #1 Hits 154 74% Top 5 195 94% Top 10 198 95% Top 20 202 97% CFM-ID only CFM-ID +DSSTox Data Sources Using CFM-ID command line tools [5], MS/MS spectra were predicted for >700,000 structures in ESI+, ESI-, and EI mode (manuscript in prep). The ESI+/- data were used to analyze the CASMI 2016 datasets. Data below are structure identification ranks for the challenge set (n=208). Retention Time Prediction logP ChromGenius OPERA-RT Combined Test and Training Set (n=97) R2 0.66 0.83 0.86 RMSE (min) 5.50 3.93 3.60 Absolute Mean Error (min) 4.65 3.03 2.93 Suspect screening of drinking water using point of use filters [6]. • Dashboard tools used for identification (data source ranking and batch searching) • Dashboard tools used for prioritization of identified compounds (exposure, bioactivity) EPA’s Non-targeted Analysis Collaborative Trial (ENTACT): • MS-Ready structures underpinning analyses and provided to participants, improving accessibility to data • In-house analysis utilizing data source ranking, metadata in Dashboard 0 2 0 0 4 0 0 6 0 0 8 0 0 1 0 0 0 0 2 0 0 4 0 0 6 0 0 8 0 0 1 0 0 0 A C N M e th o d E xp e rim e n ta l R T I PredictedRTI (Top) Results from [2] indicated that an in-house QSRR model termed OPERA-RT performed on par with the commercial software ACD/ChromGenius. (Bottom) Evaluation of retention time index (RTI) modeling developed by Aalizadeh et al (submitted manuscript). Experimental vs. predicted RTI value from ENTACT mixtures. Batch Search Page. The Batch Search page enables users to query the underlying database using data collected from HRMS studies as molecular formulae or monoisotopic masses (i.e. 10s to 100s at a time). Metadata, structural information, presence in chemical lists, and many more pieces of data can be included in the download. Single Chemical Page. A single chemical page includes chemical structures, experimental and predicted physicochemical and toxicity data, exposure data, and much more.