Characterizing information content of Markush libraries with InChI
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Characterizing information content of Markush libraries with InChI (CINF—InChI) Authors: Gabriel Sinclair, Inthirany Thillainadarajah, Brian Meyer, Vicente Samano, Sakuntala Sivasupramaniam, Linda Adams, Ann Richard, Antony Williams The application of Markush structures in the regulatory world is expanding to describe chemical categories, such as subclasses of per- and polyfluoroalkyl substances (PFAS), and substances of unknown or variable composition. However, the nature of Markush enumeration is a challenge of combinatoric complexity. Beyond the up-front computation, incorporation of unexamined Markush library data has often overwhelmed chemical data resources with synthetically intractable or experimentally unimportant structures. The interoperability of InChI provides a “first line” to compare generated structures to each other and across resources. We use this comparison to evaluate the informativeness of individual Markush structures of regulatory importance and expand on strategies to select maximally informative and concise libraries to describe chemical categories. This abstract does not necessarily represent the views or policies of the U.S. Environmental Protection Agency.
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This presentation was given at the conference: "Cheminformatics Resources of U.S. Governmental Organizations" and focuses on the CompTox Chemicals Dashboard and the sharing of our data https://www.fda.gov/news-events/fda-meetings-conferences-and-workshops/cheminformatics-resources-us-governmental-organizations-05092022#event-information Structure identification approaches using the EPA CompTox Chemicals Dashboard...
Structure identification approaches using the EPA CompTox Chemicals Dashboard...US Environmental Protection Agency (EPA), Center for Computational Toxicology and Exposure
High resolution mass spectrometry (HRMS) and non-targeted analysis (NTA) are utilized to identify emerging contaminants and chemical signatures of interest detected in various media. At the US Environmental Protection Agency the CompTox Chemicals Dashboard (https://comptox.epa.gov/dashboard) is an open chemistry resource and web-based application containing data for ~900,000 substances and supports non-targeted and suspect screening analyses. Searching functionality includes identifier searches (e.g. systematic names, trade names and CAS Registry Numbers), mass and formula-based searches and prototype developments include combined substructure-mass/formula searches and searching experimental mass spectral data against predicted fragmentation spectra. A specific type of data mapping in the database uses “MS-Ready” structures, a way to process all registered substances to separate multi-component chemicals into their individual components, removal of stereochemical bonds and desalting and neutralization. This MS-Ready processing supports batch-searching using either mass or formulae to identify candidate chemicals and their mapped substances. A number of chemical lists (https://comptox.epa.gov/dashboard/chemical_lists) have also been developed to support the identification of chemicals related to agrochemistry, specifically pesticides (both active and inert constituents), insecticides and their metabolites and environmental breakdown products). This presentation will provide an overview of how the CompTox Chemicals Dashboard supports mass spectrometry based structure identification and non-targeted analysis of chemicals in agrochemistry. This abstract does not necessarily represent the views or policies of the U.S. Environmental Protection Agency.Lipinski in silico drug discovery durham nc 2014
“Medicinal Chemistry Due Diligence: Computational Predictions of an Expert’s Evaluation of the NIH Chemical Probes”, a paper presented elsewhere at this meeting is the starting point of my story. Colleagues at Collaborative Drug Discovery were eager to see if they could mimic my medicinal chemistry evaluation of the NIH’s 308 Molecular Library Probes. As background, I had been part of a crowd-sourcing evaluation of the probes in 1969 when there were just 64 probes. In initially compiling the data which would eventually reside in machine readable form I discovered a hidden NIH spreadsheet with about two thirds of some of the data I needed. In getting the remaining data I discovered the near non-existent communication between the public world of PubChem and the proprietary world of ACS’s CAS SciFinder©. I rethought my ideas on what a drug discovery team should do when exploring freedom to operate on a new lead. I found a data dump of 5000 compounds into a US patent application from an HTS assay on the NIH’s Molecular Library compounds. I am fairly sure this led to unexpected intellectual property results for a lot of academic labs. Working on our initial computational paper I had the opportunity to put some of my favorite ideas into a second commentary-like paper with idea input from new collaborators. Ideas (especially mine) can be dangerous. What really constitutes prior art? Can disclosing too much data be a problem? Why do medicinal chemists tend to repeat the same motifs in their syntheses? What really is medicinal chemistry due diligence and how does it differ from what a biologist would do? How does medicinal chemistry due diligence tie in with target and ligand network maps and with evolution?
PFAS Chemistry: Range, Complexity, Groupings, and the CompTox Chemicals Dash...
PFAS Chemistry: Range, Complexity, Groupings, and the CompTox Chemicals Dash...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 Chemicals Dashboard (https://comptox.epa.gov/dashboard), a publicly accessible website providing access to data for ~880,000 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 the database. 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.SciFinder and its utility in Drug discovery
SciFinder Scholar® is a Z39.50 Windows-based interface that provides easy access to the rich and diverse scientific information contained in the CAS databases including Chemical Abstracts from 1907 onwards. SFS is an elegant search interface to six core chemical-related databases. Five of these databases are produced by CAS itself
Structure standardization approaches for mass spectrometry data integration
Structure standardization approaches for mass spectrometry data integrationUS Environmental Protection Agency (EPA), Center for Computational Toxicology and Exposure
Structure standardization approaches for mass spectrometry data integration
Antony J. Williams, Charles Lowe, Gabriel Sinclair, Todd Martin and Valery Tkachenko
1 Center for Computational Toxicology & Exposure, Office of Research & Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
2Oak Ridge Institute for Science and Education (ORISE) Research Participant hosted by EPA. Office of Water, U.S. Environmental Protection Agency, 1200 Pennsylvania Avenue, N.W. Washington, DC 20460, USA.
3 Science Data Experts, 14909 Forest Landing Circle, Rockville, MD, USA
Chemical database searching is a key component of the identification of “known-unknowns” using high-resolution mass spectrometry (HRMS), especially the availability of metadata for the ranking of candidate hits returned from either mass or formula searches. The form of a chemical structure observed spectroscopically does not always match the form(s) stored in a database. For example, the database may host chemical substances in their salt form, or as complex mixtures, whereas mass spectrometry will detect the neutral form, or only one component of the mixture. The US Environmental Protection Agency (EPA) has adopted the approach of standardizing chemical structures into “MS-Ready” forms to link structures observed via mass spectrometry to all related form(s) within our chemical registration database and providing access to these data via the CompTox Chemicals Dashboard. The adoption of MS-Ready structures is now a staple of our structure identification workflows, especially for non-targeted analysis.
However, examination of the MS-Ready structures generated from our initially developed workflows indicated that we required more control of the structure standardization transformations in order to allow us to further test and optimize the associated rules. This necessitated the development of a web-based tool for us to optimize the rules set. This presentation will provide an overview of the MS-Ready structures approach, its value to our non-targeted analysis mass spectrometry workflows, and the web-based tool that has been developed to assist in processing, review and optimization of the standardization rules. This abstract does not necessarily represent the views or policies of the U.S. Environmental Protection Agency.
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
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.US-EPA Chemicals Dashboard – an integrated data hub for environmental science
US-EPA Chemicals Dashboard – an integrated data hub for environmental scienceUS Environmental Protection Agency (EPA), Center for Computational Toxicology and Exposure
The U.S. Environmental Protection Agency (EPA) Computational Toxicology Program utilizes computational and data-driven approaches that integrate chemistry, exposure and biological data to help characterize potential risks from chemical exposure. 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. The CompTox Chemicals Dashboard website provides access to data associated with ~900,000 chemical substances. New data are added on an ongoing basis, including the registration of new and emerging chemicals, data extracted from the literature, chemicals studied in our labs, and data of interest to specific research projects at the EPA. Hazard and exposure data have been assembled from a large number of public databases and as a result the dashboard surfaces hundreds of thousands of data points. Other data includes experimental and predicted physicochemical property data, in vitro bioassay data for over 4000 chemicals and ~1500 assays, and millions of chemical identifiers (names and CAS Registry Numbers) to facilitate searching. Other integrated modules include an interactive read-across module, real-time physicochemical and toxicity endpoint prediction and an integrated search to PubMed. This presentation will provide an overview of the CompTox Chemicals Dashboard and how it has developed into an integrated data hub for environmental 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
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.Applying Cheminformatics to Develop a Structure Searchable Database of Analyt...
Applying Cheminformatics to Develop a Structure Searchable Database of Analyt...US Environmental Protection Agency (EPA), Center for Computational Toxicology and Exposure
Analytical methods can vary in nature from detailed regulatory methods to more summary in nature. Regulatory method documents can include details of analytes which can be studied, supported matrices, reagents, methodological details, statistical performance, interlaboratory validation and other details. Summary methods provide a general overview of reagents, instrumentation and commonly a short list of analytes. Regulatory bodies including the US Environmental Protection Agency (US-EPA), US Geological Survey (USGS), US Department of Agriculture (USDA) and others provide detailed analytical methods and collections of summary methods from industry. Instrument vendors including Agilent, Shimadzu and others, also provide access to many hundreds of application notes which can be considered as summary methods. We have developed a cheminformatically enabled database of methods whereby chemicals have been extracted from the methods, with the identifiers (names and/or chemical abstracts registry numbers) mapped to chemical structures. The resulting database of almost 3000 methods can be searched by chemical name, CASRN, structure and similarity of chemical structure. The resulting database has been integrated into a web-based application and includes integration to public domain mass spectral data and filtering of the methods based on analyte, chemical class, method source and other related metadata. This abstract does not necessarily represent the views or policies of the U.S. Environmental Protection Agency.Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...
We characterize the earliest galaxy population in the JADES Origins Field (JOF), the deepest
imaging field observed with JWST. We make use of the ancillary Hubble optical images (5 filters
spanning 0.4−0.9µm) and novel JWST images with 14 filters spanning 0.8−5µm, including 7 mediumband filters, and reaching total exposure times of up to 46 hours per filter. We combine all our data
at > 2.3µm to construct an ultradeep image, reaching as deep as ≈ 31.4 AB mag in the stack and
30.3-31.0 AB mag (5σ, r = 0.1” circular aperture) in individual filters. We measure photometric
redshifts and use robust selection criteria to identify a sample of eight galaxy candidates at redshifts
z = 11.5 − 15. These objects show compact half-light radii of R1/2 ∼ 50 − 200pc, stellar masses of
M⋆ ∼ 107−108M⊙, and star-formation rates of SFR ∼ 0.1−1 M⊙ yr−1
. Our search finds no candidates
at 15 < z < 20, placing upper limits at these redshifts. We develop a forward modeling approach to
infer the properties of the evolving luminosity function without binning in redshift or luminosity that
marginalizes over the photometric redshift uncertainty of our candidate galaxies and incorporates the
impact of non-detections. We find a z = 12 luminosity function in good agreement with prior results,
and that the luminosity function normalization and UV luminosity density decline by a factor of ∼ 2.5
from z = 12 to z = 14. We discuss the possible implications of our results in the context of theoretical
models for evolution of the dark matter halo mass function.
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Delivering chemical-associated data via EPA web applications
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“Medicinal Chemistry Due Diligence: Computational Predictions of an Expert’s Evaluation of the NIH Chemical Probes”, a paper presented elsewhere at this meeting is the starting point of my story. Colleagues at Collaborative Drug Discovery were eager to see if they could mimic my medicinal chemistry evaluation of the NIH’s 308 Molecular Library Probes. As background, I had been part of a crowd-sourcing evaluation of the probes in 1969 when there were just 64 probes. In initially compiling the data which would eventually reside in machine readable form I discovered a hidden NIH spreadsheet with about two thirds of some of the data I needed. In getting the remaining data I discovered the near non-existent communication between the public world of PubChem and the proprietary world of ACS’s CAS SciFinder©. I rethought my ideas on what a drug discovery team should do when exploring freedom to operate on a new lead. I found a data dump of 5000 compounds into a US patent application from an HTS assay on the NIH’s Molecular Library compounds. I am fairly sure this led to unexpected intellectual property results for a lot of academic labs. Working on our initial computational paper I had the opportunity to put some of my favorite ideas into a second commentary-like paper with idea input from new collaborators. Ideas (especially mine) can be dangerous. What really constitutes prior art? Can disclosing too much data be a problem? Why do medicinal chemists tend to repeat the same motifs in their syntheses? What really is medicinal chemistry due diligence and how does it differ from what a biologist would do? How does medicinal chemistry due diligence tie in with target and ligand network maps and with evolution?
PFAS Chemistry: Range, Complexity, Groupings, and the CompTox Chemicals Dash...
PFAS Chemistry: Range, Complexity, Groupings, and the CompTox Chemicals Dash...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 Chemicals Dashboard (https://comptox.epa.gov/dashboard), a publicly accessible website providing access to data for ~880,000 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 the database. 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.SciFinder and its utility in Drug discovery
SciFinder Scholar® is a Z39.50 Windows-based interface that provides easy access to the rich and diverse scientific information contained in the CAS databases including Chemical Abstracts from 1907 onwards. SFS is an elegant search interface to six core chemical-related databases. Five of these databases are produced by CAS itself
Structure standardization approaches for mass spectrometry data integration
Structure standardization approaches for mass spectrometry data integrationUS Environmental Protection Agency (EPA), Center for Computational Toxicology and Exposure
Structure standardization approaches for mass spectrometry data integration
Antony J. Williams, Charles Lowe, Gabriel Sinclair, Todd Martin and Valery Tkachenko
1 Center for Computational Toxicology & Exposure, Office of Research & Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
2Oak Ridge Institute for Science and Education (ORISE) Research Participant hosted by EPA. Office of Water, U.S. Environmental Protection Agency, 1200 Pennsylvania Avenue, N.W. Washington, DC 20460, USA.
3 Science Data Experts, 14909 Forest Landing Circle, Rockville, MD, USA
Chemical database searching is a key component of the identification of “known-unknowns” using high-resolution mass spectrometry (HRMS), especially the availability of metadata for the ranking of candidate hits returned from either mass or formula searches. The form of a chemical structure observed spectroscopically does not always match the form(s) stored in a database. For example, the database may host chemical substances in their salt form, or as complex mixtures, whereas mass spectrometry will detect the neutral form, or only one component of the mixture. The US Environmental Protection Agency (EPA) has adopted the approach of standardizing chemical structures into “MS-Ready” forms to link structures observed via mass spectrometry to all related form(s) within our chemical registration database and providing access to these data via the CompTox Chemicals Dashboard. The adoption of MS-Ready structures is now a staple of our structure identification workflows, especially for non-targeted analysis.
However, examination of the MS-Ready structures generated from our initially developed workflows indicated that we required more control of the structure standardization transformations in order to allow us to further test and optimize the associated rules. This necessitated the development of a web-based tool for us to optimize the rules set. This presentation will provide an overview of the MS-Ready structures approach, its value to our non-targeted analysis mass spectrometry workflows, and the web-based tool that has been developed to assist in processing, review and optimization of the standardization rules. This abstract does not necessarily represent the views or policies of the U.S. Environmental Protection Agency.
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
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.US-EPA Chemicals Dashboard – an integrated data hub for environmental science
US-EPA Chemicals Dashboard – an integrated data hub for environmental scienceUS Environmental Protection Agency (EPA), Center for Computational Toxicology and Exposure
The U.S. Environmental Protection Agency (EPA) Computational Toxicology Program utilizes computational and data-driven approaches that integrate chemistry, exposure and biological data to help characterize potential risks from chemical exposure. 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. The CompTox Chemicals Dashboard website provides access to data associated with ~900,000 chemical substances. New data are added on an ongoing basis, including the registration of new and emerging chemicals, data extracted from the literature, chemicals studied in our labs, and data of interest to specific research projects at the EPA. Hazard and exposure data have been assembled from a large number of public databases and as a result the dashboard surfaces hundreds of thousands of data points. Other data includes experimental and predicted physicochemical property data, in vitro bioassay data for over 4000 chemicals and ~1500 assays, and millions of chemical identifiers (names and CAS Registry Numbers) to facilitate searching. Other integrated modules include an interactive read-across module, real-time physicochemical and toxicity endpoint prediction and an integrated search to PubMed. This presentation will provide an overview of the CompTox Chemicals Dashboard and how it has developed into an integrated data hub for environmental 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
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.Applying Cheminformatics to Develop a Structure Searchable Database of Analyt...
Applying Cheminformatics to Develop a Structure Searchable Database of Analyt...US Environmental Protection Agency (EPA), Center for Computational Toxicology and Exposure
Analytical methods can vary in nature from detailed regulatory methods to more summary in nature. Regulatory method documents can include details of analytes which can be studied, supported matrices, reagents, methodological details, statistical performance, interlaboratory validation and other details. Summary methods provide a general overview of reagents, instrumentation and commonly a short list of analytes. Regulatory bodies including the US Environmental Protection Agency (US-EPA), US Geological Survey (USGS), US Department of Agriculture (USDA) and others provide detailed analytical methods and collections of summary methods from industry. Instrument vendors including Agilent, Shimadzu and others, also provide access to many hundreds of application notes which can be considered as summary methods. We have developed a cheminformatically enabled database of methods whereby chemicals have been extracted from the methods, with the identifiers (names and/or chemical abstracts registry numbers) mapped to chemical structures. The resulting database of almost 3000 methods can be searched by chemical name, CASRN, structure and similarity of chemical structure. The resulting database has been integrated into a web-based application and includes integration to public domain mass spectral data and filtering of the methods based on analyte, chemical class, method source and other related metadata. This abstract does not necessarily represent the views or policies of the U.S. Environmental Protection Agency.Similar to Characterizing information content of Markush libraries with InChI (20)
Non-targeted analysis supported by data and cheminformatics delivered via the...
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US EPA CompTox Chemicals Dashboard Data Integration Hub to Support Environmen...
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Introduction:
RNA interference (RNAi) or Post-Transcriptional Gene Silencing (PTGS) is an important biological process for modulating eukaryotic gene expression.
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What are small ncRNAs?
micro RNA (miRNA)
short interfering RNA (siRNA)
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Involved in silencing mRNA transcripts.
Called “small” because they are usually only about 21-24 nucleotides long.
Synthesized by first cutting up longer precursor sequences (like the 61nt one that Lee discovered).
Silence an mRNA by base pairing with some sequence on the mRNA.
Discovery of siRNA?
The first small RNA:
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MiRNA
Length (23-25 nt)
Trans acting
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Si RNA
Length 21 nt.
Cis acting
Bind with target Mrna in perfect complementary sequence
Piwi-RNA
Length ; 25 to 36 nt.
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extra-chromosomal-inheritance[1].pptx.pdfpdf
Slide 1: Title Slide
Extrachromosomal Inheritance
Slide 2: Introduction to Extrachromosomal Inheritance
Definition: Extrachromosomal inheritance refers to the transmission of genetic material that is not found within the nucleus.
Key Components: Involves genes located in mitochondria, chloroplasts, and plasmids.
Slide 3: Mitochondrial Inheritance
Mitochondria: Organelles responsible for energy production.
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Slide 4: Chloroplast Inheritance
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Slide 5: Plasmid Inheritance
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Slide 6: Mechanisms of Extrachromosomal Inheritance
Non-Mendelian Patterns: Do not follow Mendel’s laws of inheritance.
Cytoplasmic Segregation: During cell division, organelles like mitochondria and chloroplasts are randomly distributed to daughter cells.
Heteroplasmy: Presence of more than one type of organellar genome within a cell, leading to variation in expression.
Slide 7: Examples of Extrachromosomal Inheritance
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Petite Mutants in Yeast: Result from mutations in mitochondrial DNA affecting respiration.
Slide 8: Importance of Extrachromosomal Inheritance
Evolution: Provides insight into the evolution of eukaryotic cells.
Medicine: Understanding mitochondrial inheritance helps in diagnosing and treating mitochondrial diseases.
Agriculture: Chloroplast inheritance can be used in plant breeding and genetic modification.
Slide 9: Recent Research and Advances
Gene Editing: Techniques like CRISPR-Cas9 are being used to edit mitochondrial and chloroplast DNA.
Therapies: Development of mitochondrial replacement therapy (MRT) for preventing mitochondrial diseases.
Slide 10: Conclusion
Summary: Extrachromosomal inheritance involves the transmission of genetic material outside the nucleus and plays a crucial role in genetics, medicine, and biotechnology.
Future Directions: Continued research and technological advancements hold promise for new treatments and applications.
Slide 11: Questions and Discussion
Invite Audience: Open the floor for any questions or further discussion on the topic.
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Characterizing information content of Markush libraries with InChI
- 1. Characterizing information content of Markush libraries with InChI Gabriel Sinclair1, Inthirany Thillainadarajah2, Brian Meyer2, Vicente Samano2, Sakuntala Sivasupramaniam2, Linda Adams2, Ann Richard3 and Antony Williams3 1 Center for Computational Toxicology & Exposure, U.S. Environmental Protection Agency, 2 ORAU Student Services Contractor 3 Senior Environmental Employment (SEE) Program August 2022: ACS Chicago http://www.orcid.org/0000-0002-2668-4821 The views expressed in this presentation are those of the author and do not necessarily reflect the views or policies of the U.S. EPA
- 2. Characterizing information content of Markush libraries with InChI Gabriel Sinclair1, Inthirany Thillainadarajah2, Brian Meyer2, Vicente Samano2, Sakuntala Sivasupramaniam2, Linda Adams2, Ann Richard3 and Antony Williams3 1 Center for Computational Toxicology & Exposure, U.S. Environmental Protection Agency, 2 ORAU Student Services Contractor 3 Senior Environmental Employment (SEE) Program August 2022: ACS Chicago http://www.orcid.org/0000-0002-2668-4821 The views expressed in this presentation are those of the author and do not necessarily reflect the views or policies of the U.S. EPA
- 3. The CompTox Chemicals Dashboard https://comptox.epa.gov/dashboard • Integration hub for >906k substances and associated data (1.2M are about to release) • Curated data, with ongoing daily curation and expansion, focused on environmental chemistry and impacts on human health • Aggregated sets of chemical lists of interest – endocrine disruptors, PFAS, azo dyes, disinfectant by-products, and 100s more 2
- 4. ~906k chemicals includes explicit structures and complexity • Many chemicals cannot be defined as distinct chemical structures but have significant complexity – UVCB chemicals (unknown or variable composition, complex reaction products and biological substances) 3
- 5. ~906k chemicals includes explicit structures and complexity • Many chemicals cannot be defined as distinct chemical structures but have significant complexity – UVCB chemicals (unknown or variable composition, complex reaction products and biological substances) – Some chemicals can be described with Markush structure boundaries • WELL-DEFINED: Xylenes: 3 chemicals. Polychlorinated biphenyls and polybrominated diphenyl ethers: 209 chemicals in each class • WELL-DEFINED: C10-C14 linear alkylbenzenesulfonic acids • LESS WELL-DEFINED: Linear perfluoroalkylsulfonic acids, becomes well defined once the limits are defined (C1-C100) • HARDLY DEFINED: Chloroparaffins, Cobalt/Cobalt-related substances • HARDLY DEFINED: Polymers – length, HH, TT, HT, capped, 4
- 6. Well-Defined Complex Substances PCBs: 209 distinct structures 5 • 209 “Markush-enumerated” structures are mapped • Markush-enumerated structures are mapped to existing chemicals in the database
- 7. Well-Defined Complex Substances Linear alkylbenzene sulfonates 6
- 8. Well-Defined Complex Substances Linear alkylbenzene sulfonates 7 • Mapping to other generic structures and explicit structures
- 9. Less Well-Defined Substances PFAS Categories of chemicals (326) 8
- 10. Less Well-Defined Substances PFAS “Categories” of chemicals 9
- 11. Markush chemicals are now essential… • Markush structures are essential for: – Mapping constrained structure datasets (PCBs, PBDEs etc) – Enumerating possible structures and mapping to existing chemicals – As we enumerate Markush structures we search for validation 10
- 12. Markush chemicals are now essential… • Markush structures are essential for: – Mapping constrained structure datasets (PCBs, PBDEs etc) – Enumerating possible structures and mapping to existing chemicals – As we enumerate Markush structures we search for validation • The benefits of this approach – Querying external resources for validation and expansion of DSSTox – Sourcing identifiers for us to register and validate in DSSTox – Sourcing chemicals to purchase 11
- 13. Markush Enumeration mapping • When we add Markush structures to our database we enumerate and map related substance mappings 12
- 14. Markush Enumeration mapping • When we add Markush structures to our database we enumerate and map related substance mappings • Complex substances therefore map to individual structures 13
- 16. Sourcing data from external sites • When Markush are enumerated we source data by InChI • InChI keys pinged against resources for “chemical exists” – EBI Unichem 15
- 17. Sourcing data from external sites • When Markush are enumerated we source data by InChI • InChI keys pinged against resources for “chemical exists” – EBI Unichem – PubChem – Other sources 16
- 18. Retrieving data for validation • When we retrieve data for Markush structures we use it to assist in curation – 5 different curation levels 17
- 19. InChIs is our PRIMARY mapping identifier • Since we can map Markush to explicit structures we end up with “representative structures” for searching. This helps: – Search public resources for chemical vendors (thanks PubChem!). Sourcing chemicals for study in our studies such as bioactivity, HTTK, metabolism (InChI first block will find us isotopically labeled chemicals) – Searching across “literature” – Google scholar, books and patents – Sourcing toxicity data for read-across purposes • All of this is available to the public via “External Links” 18
- 25. Future Work • We are desperately waiting for the Markush InChI Key - we have use cases for it immediately • We are definitely interested in the Mixture InChI, and it’s applications to mixtures and potentially polymers 24 Polytriazine There is one Google hit for the combined InChI for polytriazine But thousands of hits for one of the components of the mixture (Atrazine)
- 26. Future Work – adding Markush where we can • TSCA non-confidential has >33k substances with half have no structures. Some can have Markush added (and mapped) 25
- 27. Conclusions • Dashboard data is under constant curation and Markush addition has become a standard part of our work • Mapped relationships between chemicals is of value for complex substances and important for us to source data • Markush enumerated to distinct chemicals with associated InChIs then allows us to harvest data and link across sites • Chemistry is complex and informatics solutions are more complete for distinct structures than complex substances. There is lots more to do… we await the Markush InChI 26