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38 results for “exposomics”
The Blood Exposome Database
<p>The Blood Exposome Database catalogues the chemicals (endogenous and exogenous) that are expected and detected in human blood specimens. The database was created using a text mining approach using the <a href="https://pubchem.ncbi.nlm.nih.gov/">NCBI PubChem</a> , <a href="https://pubmed.ncbi.nlm.nih.gov/">NCBI PubMed</a> and <a href="https://www.ncbi.nlm.nih.gov/pmc/">NCBI PMC</a> databases. Chemicals that have been reported in the primary literature (original research articles) for blood specimens are included in the database. Additionally, data from biomonitoring surveys and metabolomics datasets (public available) for human blood specimens are also covered in the database. </p> <p>Citation: Barupal Dinesh, Fiehn Oliver. Generating the blood exposome database using a comprehensive text mining and database fusion approach. Environmental health perspectives. 2019 Sep 26;127(9):097008. <a href="https://ehp.niehs.nih.gov/doi/full/10.1289/EHP4713">EHP Link</a> </p>
PubChemLite for Exposomics + predicted CCS from CCSbase - 5 Sept. 2025
<p>PubChemLite is a subset of PubChem (<a href="https://pubchem.ncbi.nlm.nih.gov/">https://pubchem.ncbi.nlm.nih.gov/</a>) selected from major categories of the Table of Contents page at the PubChem Classification Browser (<a href="https://pubchem.ncbi.nlm.nih.gov/classification/#hid=72">https://pubchem.ncbi.nlm.nih.gov/classification/#hid=72</a>). This version of PubChemLite for Exposomics has predicted collision cross section (CCS) values for 11 adducts provided by Libin Xu and team at CCSbase (<a href="https://ccsbase.net/">https://ccsbase.net/</a>) calculated from the latest <a href="https://github.com/dylanhross/c3sdb/">c3sdb</a> code.</p> <p>PubChemLite <em>exposomics</em> is compiled from 11 categories: AgroChemInfo, BioPathway, DrugMedicInfo, FoodRelated, PharmacoInfo, SafetyInfo, ToxicityInfo, KnownUse, DisorderDisease, Identification, ChemClass.</p> <p>CCS adducts provided are: [M+H]+, [M-H]-, [M+Na]+, [M+K]+, [M+NH4]+, [M+H-H2O]+, [M+HCOO]-, [M+CH3COO]-, [M+Na-2H]-, [M]+, [M]-</p> <p>Details on the CCS prediction are given here: Ross <em>et al</em>. (2020) Analytical Chemistry, DOI: <a href="https://pubs.acs.org/doi/10.1021/acs.analchem.9b05772">10.1021/acs.analchem.9b05772</a></p> <p>PubChemLite is described in Schymanski <em>et al. </em>(2021) J. Cheminformatics, DOI: <a href="https://doi.org/10.1186/s13321-021-00489-0" target="_blank" rel="noopener">10.1186/s13321-021-00489-0</a></p> <p>An article describing these joint efforts is available: Elapavalore <em>et al</em>. (2025) ES&T Letters, DOI: <a href="https://doi.org/10.1021/acs.estlett.4c01003">10.1021/acs.estlett.4c01003</a></p> <p>PubChemCIDs have been collapsed by InChIKey first block, reporting the structure from the most annotated CID, plus related CIDs. Entries that will be ignored by MetFrag (salts, disconnected substances) or cause errors (e.g. transition metals) have been removed. The Patent and PubMed ID counts are extracted from files on the PubChem FTP site. The "AnnoTypeCount" term counts how many of the categories are represented, the subsequent column (named per category) counts the number of annotation categories available in the next sub-category of the TOC entry.</p> <p>These files can be used "as is" as localCSV for MetFrag Command Line (<a href="https://ipb-halle.github.io/MetFrag/">https://ipb-halle.github.io/MetFrag/</a>) - please do NOT upload these files directly to the web interface, they are too large and will be available in a drop-down menu.</p> <p>Further details are described in Schymanski <em>et al.</em> (2021) DOI:<a href="https://doi.org/10.1186/s13321-021-00489-0">10.1186/s13321-021-00489-0</a> and Elapavalore <em>et al</em>. (2025) DOI: <a href="https://doi.org/10.1021/acs.estlett.4c01003">10.1021/acs.estlett.4c01003</a></p> <p><strong><em>NOTE: The latest PubChemLite for Exposomics version can be downloaded at DOI:</em></strong><em><strong><a href="https://doi.org/10.5281/zenodo.5995885">10.5281/zenodo.5995885</a> (currently updating monthly). This file will be updated shortly after. <br></strong></em></p> <p>Please cite this data source and Elapavalore <em>et al</em>. (2025) DOI: <a href="https://doi.org/10.1021/acs.estlett.4c01003">10.1021/acs.estlett.4c01003</a> when using this dataset.</p>
Exposome Boot Camp Lab 4: MetFrag in Practice
<p>This is the full set of materials used for the MetFrag in Practice Lab at the Exposome Boot Camp</p> <p>https://www.mailman.columbia.edu/research/precision-prevention/exposome-boot-camp-measuring-exposures-omic-scale</p> <p>Overview: see PDF. Examples: see word document. Additional files are required for specific examples for upload to MetFrag. Remaining information is in the word document, in hyperlinks, online at MetFrag and MassBank or in screenshots in the PDF.</p>
Blood Exposome Database: MetFrag Local CSV
<p>This is a local CSV file of the Blood Exposome Database (<a href="http://bloodexposome.org/">http://bloodexposome.org/</a>) for MetFrag (<a href="https://msbi.ipb-halle.de/MetFrag/">https://msbi.ipb-halle.de/MetFrag/</a>).</p> <p>Data was trimmed to necessary columns from parent-mapped TSV provided by Dinesh Barupal. Approx. 15 entries containing elements not processed by MetFrag were removed. Entries with singly charged formulas had the charge removed from the formula to produce results consistent with other MetFrag files (where neutral formula is required; no adjustment for +/-H was performed so these remained consistent with the mass entries with minimum manipulation - see xlsx file for traceback). Subsequent versions could be adjusted for different behaviour if desired. </p> <p>This file is for users wanting to integrate the latest Blood Exposome Database into MetFrag CL workflows (offline), this file will be integrated into MetFrag online; please use the file in the dropdown menu rather than uploading this one.</p> <p>Please credit the data source in any use of this file as the licence is CC-BY: <a href="http://bloodexposome.org/">http://bloodexposome.org/</a></p>
Processed metabolomic data from the EXPOsOMICS Personal Exposure Monitoring study
<p>Metabolomic data from the 'Variability of the Human Serum Metabolome over 3 Months in the EXPOsOMICS Personal Exposure Monitoring Study' paper <a href="https://doi.org/10.1021/acs.est.3c03233">DOI: 10.1021/acs.est.3c03233</a> . </p> <p>The data was originally collected and generated by the multicenter EXPOsOMICS Personal Exposure Monitoring study. Details on data collection and processing are described in the aforementioned paper. The statistical analysis from that paper is available at <a href="https://github.com/moosterwegel/variability-metabolites-paper">https://github.com/moosterwegel/variability-metabolites-paper</a> and may contain useful information/code to work with this data.</p> <p>`processed_covariate_data.csv`:<br> ```<br> Rows: 298<br> Columns: 7<br> $ subjectid: hashed identifier subject<br> $ sample_code: indicates if it's the first (A) or second (B) blood sample<br> $ centre: indicates in which centre the data was collected<br> $ age_cat: indicates age category at the time of a PEM session<br> $ sq_sex: indicates the sex of the participant (male, female) as filled in during the screening questionaire<br> $ traf: indicates the exposure to traffic (PM2.5 and UFP) as measured during the PEM sessions. <br> $ bmi_cat: indicates BMI category at the time of a PEM session<br> ```</p> <p>`processed_lcms_data data.csv` contains the processed LCMS data:<br> ```<br> Rows: 298<br> Columns: 4297<br> $ subjectid: hashed identifier subject<br> $ sample_code: indicates if it's the first (A) or second (B) blood sample<br> $ centre: indicates in which centre the data was collected<br> $ compounds: measured features (compounds) are prefixed by the letter X. The name contains information on the measured monoisotopicmass_retentiontime.<br> Non-detects (below limit of detection (LOD) are coded as 1 for the compounds.<br> ....<br> ```<br> In the datasets each row indicates a measurement on a day (`sample_code`) and person (`subjectid`). The datasets can be joined on these variables.</p> <p>The other data files (`annotations.xslx`, `ancestors_annotations.xlsx`, `annotations_plus_kegg_pathways.csv`) contain the annotations, ancestors of the annotations (to assign a class to a compound based on ChEBI ontology, see our paper for details), annotations plus KEGG pathways respectively. </p>
An exposome atlas of serum reveals risk of chronic diseases in Chinese population
<p>Although adverse environmental exposures are considered to be a major cause to chronic diseases, current studies have provided limited knowledge on real-world chemical exposures and related risks. Here, we collected serum samples from 5696 healthy people and patients, including 12 chronic diseases in China, and completed serum biomonitoring containing 267 chemicals using gas and liquid chromatography-tandem mass spectrometry. 74 high-frequently detected exposures were used for exposure characterization and risk analysis. Results showed that region was the most critical factor influencing human exposure levels, followed by age. Organochlorine pesticides and perfluoroalkyl substances were associated with multiple chronic diseases, and some of them exceeded safe ranges. Mixture effect models showed significant risk effects of exposure on hyperlipidemia, metabolic syndrome and hyperuricemia. Overall, this study provided a comprehensive human serum exposure atlas and its disease risk, which could guide subsequent more in-depth cause-and-effect studies between environmental exposures and human health. The R codes and related example data for statistical analysis and figure production have been deposited to the GitHub (https://github.com/youlei2023/ExposomeAtlas).</p>
RECETOX Exposome HR-[EI+]-MS library
<p>The RECETOX Mass Spectrum Reference Libraries is a collection of MS spectra collected from authentic compounds. Each library comprises the spectra in MSP format and an accompanying SDF database of compounds. The collection is shared under terms & conditions of <a href="https://creativecommons.org/licenses/by-nc/4.0/">CC-BY-NC</a>.</p> <p>The RECETOX Exposome HR-[EI+]-MS library is a collection of mostly anthropogenic compounds. Spectra were acquired at 70 eV on Thermo Fisher Q Exactive™ GC Orbitrap™ GC-MS/MS at 60000 resolving power.</p>
Exposomics Spectral Library
<p><strong>Title: </strong>Repurposing Public Metabolomics Datasets for Construction of an Exposomics Spectral Library</p> <p><strong>Introduction</strong></p> <p>Publicly archived metabolomics datasets from diverse human biosamples provides an opportunity to repurpose the shared datasets for further exploratory analysis into human health. Though, most of the times the endogenous metabolome is implicated in disease research as biomarkers, the growing role of exposome in human health underscores the need for identification of chemical exposures in human samples. In this regard, I explored the possibility of finding previously unreported exposomal compounds (i.e., anthropogenic, industrial, dietary, and microbial chemicals) from the true unknowns in these studied datasets. Using in silico spectral library matching followed by molecular structure prediction approaches, the aim of this study is to recognize the exposome, and minimize the gap between potential number of true exposomic substances in biosamples.</p> <p><strong>Methods</strong></p> <p>Raw metabolomics (GC-MS) datasets were downloaded from Metabolomics Workbench, GNPS, and MetaboLights using key words- ‘human, GC-MS, serum, plasma, muscle, liver, kidney’. The vendor formatted mass spectrometry datasets were converted to .mzML formats using MSConvertGUI (ProteoWizad) for data processing and spectral library (GOLM, MoNA, Fiehnlib, MassBank) matching using MS-DIAL. For EI-MS spectral annotation, the identity was confirmed by the presence of [M−CH<sub>3</sub>]<sup>+</sup>, [M+H]<sup>+</sup>, [M+C<sub>2</sub>H<sub>5</sub>]<sup>+</sup> and [M+C<sub>3</sub>H<sub>5</sub>]<sup>+</sup> and using Global Natural Products Social (GNPS) molecular networking. Exposomal metabolites were separated from the rest based on identifiers at the Blood Exposome DB. True unassigned spectra were further interrogated using MS-FINDER for structural prediction. Exported spectra in .msp and .txt formats were pooled into a single file for free public download and use.</p> <p><strong>Preliminary data</strong></p> <p>The pooled GC-MS datasets (50) obtained from the three repositories were from multiple human samples, multiple vendors, and were generated using multiple mass analyzers (single and triple quads, ToFs, and Orbitraps). The .mzML files were processed for data preprocessing such as deconvolution, peak picking, and peak alignment followed by compound identification using MS-DIAL and GNPS tools. Processing parameters for the datasets were optimized individually in a study-specific manner. Altogether, the data resulted in spectral assignment of approx. 400 compounds of endogenous origin, associated with a KEGG and HMDB identifier relating to generic metabolic pathways, using only open source spectral libraries. Given extremely limited overlap between spectral libraries, I used a pooled spectral library generated from all available open source spectral data. Further, 350 unassigned spectra (displaying insufficient matching scores for an assignment, i.e., < 500; with S/N >25 in each dataset) were interrogated using MS-FINDER and Global Natural Products Social (GNPS) molecular networking approach (both cosine score, > 0.5; balance score, > 0.9) that resulted in annotation of 250 exposomic compounds. Using ClassyFire the exposomal compounds (InChIs) were assigned a hierarchical chemical classification which indicated diverse origin of these compounds ranging from medications, industrial chemicals, pollutants to phytochemicals of dietary origin. The assigned spectra were individually manually curated and then compiled as a single file available as the ‘Exposomics Spectral Library’ to public as .txt and .msp file formats for free use and is available: 10.5281/zenodo.3755855.</p>
PubChemLite for Exposomics
<p>PubChemLite is a subset of PubChem (<a href="https://pubchem.ncbi.nlm.nih.gov/">https://pubchem.ncbi.nlm.nih.gov/</a>) selected from major categories of the Table of Contents page at the PubChem Classification Browser (<a href="https://pubchem.ncbi.nlm.nih.gov/classification/#hid=72">https://pubchem.ncbi.nlm.nih.gov/classification/#hid=72</a>). With this release, there is now just one "exposomics" flavour, which is the former tier1 plus two new categories (Associated Disorders & Diseases and Identification):</p> <p>PubChemLite "exposomics" is 371,663 compounds (31 Oct 2020) compiled from 10 categories: AgroChemInfo, BioPathway, DrugMedicInfo, FoodRelated, PharmacoInfo, SafetyInfo, ToxicityInfo, KnownUse, DisorderDisease, Identification.</p> <p>PubChemCIDs have been collapsed by InChIKey first block, reporting the structure from the most annotated CID, plus related CIDs. Entries that will be ignored by MetFrag (salts, disconnected substances) or cause errors (e.g. transition metals) have been removed. The Patent and PubMed ID counts are extracted from files on the PubChem FTP site. The "AnnoTypeCount" term counts how many of the categories are represented, the subsequent column (named per category) counts the number of annotation categories available in the next sub-category of the TOC entry.</p> <p>These files can be used "as is" as localCSV for MetFrag Command Line (<a href="https://ipb-halle.github.io/MetFrag/">https://ipb-halle.github.io/MetFrag/</a>) - please do NOT upload these files directly to the web interface, they are too large and will instead be available in a drop-down menu.</p> <p>Further details are described in Schymanski <em>et al.</em> (2021) DOI:<a href="https://doi.org/10.1186/s13321-021-00489-0">10.1186/s13321-021-00489-0</a>.</p> <p><strong><em>NOTE: The latest PubChemLite for Exposomics version can be downloaded at DOI:</em></strong><em><strong><a href="https://doi.org/10.5281/zenodo.5995885">10.5281/zenodo.5995885</a> (currently updating monthly).</strong></em></p>
Blood Exposome Database Compounds with DDA spectra coverage
<p>The last two columns in the CSV file has 1) DDA spectra (publicly available) count for blood exposome database compound list and 2) coverage (y/n) in the NIST 2020 database. First block of the InchiKey was used to query the spectral data that was downloaded from the MONA, GNPS, MS-DIAL repositories. </p> <p>It can be useful for prioritizing compounds that need to be purchased for new DDA data collection, or they can be candidates for in-silico fragmentation analyses. </p>
Exposome Chemicals without MS data in the public domain
<p>List of high priority exposome chemicals that lack mass spectral data in public mass spectral repositories and the NIST MS 2020 database. Some of them are not amenable to mass spectrometry instruments. These compounds should be candidates for MS library expansion projects in exposome. </p>
RECETOX Exposome HR-[ESI]-MS library
<p>The RECETOX Mass Spectrum Reference Libraries is a collection of MS spectra collected from authentic compounds. Each Library comprises the spectra in MSP format. The collection is shared under terms & conditions of CC-BY-NC.</p> <p>The RECETOX Exposome Pesticide HR-[ESI]-MS library is a collection of pesticides obtained from Restek Corporation. The RECETOX Exposome Pharmaceutical HR-[ESI]-MS library is a collection of pharmaceuticals selected from Prestwick Chemical Libraries.</p> <p>Compounds were analyzed by liquid chromatography coupled to a Thermo Scientific™ Orbitrap Fusion™ Tribrid™ Mass Spectrometer. Reversed-phase chromatography was performed using a pentabromobenzyl column. Tandem mass spectra were acquired via data-dependent acquisition with higher energy C-trap dissociation and real-time collision energy optimization for positive and negative mode electrospray ionization. The scan range was 85-1275 m/z with MS1 collected at 120K and MS2 at 60K resolving power. </p>
S34 | EXPOSOMEXPL | Biomarkers from Exposome Explorer
<p>This is the collection associated with list S34 EXPOSOMEXPL on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>S34 | EXPOSOMEXPL | <strong>Biomarkers from Exposome Explorer</strong></p> <p>Exposome Explorer Biomarkers Download <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/210119Update/ExposomeExplorer-biomarkers.xlsx">XLSX</a> (24/01/2019)<br> EXPOSOMEXPL Mapped <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/210119Update/EXPOSOMEXPL_wDTXSIDs_24012019.csv">CSV</a>, <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/210119Update/EXPOSOMEXPL_wDTXSIDs_24012019.xlsx">XLSX</a> (24/01/2019)<br> CompTox <a href="https://comptox.epa.gov/dashboard/chemical_lists/exposomexpl">EXPOSOMEXPL List</a></p> <p>EXPOSOMEXPL <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/210119Update/EXPOSOMEXPL_InChIKeys_24012019.txt">InChIKeys</a> (24/01/2019)</p> <p>The Exposome-Explorer (<a href="http://exposome-explorer.iarc.fr/">http://exposome-explorer.iarc.fr/</a>) is dedicated to biomarkers of exposure to environmental risk factors for diseases (Neveu <em>et al</em> 2017, DOI: <a href="http://doi.org/10.1093/nar/gkw980">10.1093/nar/gkw980</a>). Provided by Reza Salek and Vanessa Neveu (IARC), mapping files to all discrete chemicals by A. Williams/E. Schymanski. </p>
In-silico CID MS/MS Spectra for the Blood Exposome Compounds
<p>In-silico spectra (+ve mode, M+H) were generated for the Blood Exposome Compounds ( BloodExposome.org ) using the ICEBERG model, re-trained using MONA/GNPS/NIST2020 spectra. </p> <p>Disclaimer: These are simulated spectra so they should be used with cautions to annotate compounds in LC-HRMS datasets. </p> <p>Files are available in the NIST MSP format and the NIST Library (lib2nist conversion)</p>
Data and Code Repository for Expanding non-target analysis methods to characterize the prenatal exposome
<p>Data and Code Repository for the following manuscript: Expanding non-target analysis methods to characterize the prenatal exposome. </p>
Data from: A database of human exposomes and phenomes from the US National Health and Nutrition Examination Survey
The National Health and Nutrition Examination Survey (NHANES) is a population survey implemented by the Centers for Disease Control and Prevention (CDC) to monitor the health of the United States whose data is publicly available in hundreds of files. This Data Descriptor describes a single unified and universally accessible data file, merging across 255 separate files and stitching data across 4 surveys, encompassing 41,474 individuals and 1,191 variables. The variables consist of phenotype and environmental exposure information on each individual, specifically (1) demographic information, physical exam results (e.g., height, body mass index), laboratory results (e.g., cholesterol, glucose, and environmental exposures), and (4) questionnaire items. Second, the data descriptor describes a dictionary to enable analysts find variables by category and human-readable description. The datasets are available on DataDryad and a hands-on analytics tutorial is available on GitHub. Through a new big data platform, BD2K Patient Centered Information Commons (http://pic-sure.org), we provide a new way to browse the dataset via a web browser (https://nhanes.hms.harvard.edu) and provide application programming interface for programmatic access.
In-silico Mass Spectra for PFAS in the Blood Exposome Database
<p>In-silico spectra were generated for the PFAS compounds in the Blood Exposome Database using the ICEBERG model, re-trained using MONA/GNPS/NIST2020 spectra. </p>
PubChem lists to Study the Exposome of Mild Cognitive Impairment and Alzheimer's Disease on Cerebrospinal Fluid
<p>Collection of 4 lists of chemicals associated to Alzheimer's disease (AD) and related disorders. </p> <p>The following table describes the mentioned lists. </p> <table> <tbody> <tr> <td>Name</td> <td>Description</td> </tr> <tr> <td>AD-database</td> <td>Database of 41,917 chemicals related to AD and other neurodegenerative disorders. CIDs co-ocurring with the 27 selected MeSH terms (*see below) were merged and mapped to the parent CIDS.</td> </tr> <tr> <td>TOP1</td> <td>List of 1,268 chemicals having D000544 (AD MeSH code) as the first neighbor .</td> </tr> <tr> <td>SC20</td> <td>List of 247 chemicals created after truncating by 1/20 of the maximum co-ocurrence score of the chemical neighbors for D000544. </td> </tr> <tr> <td>AD-CTD</td> <td>List of 86 chemicals specifically related to AD in the Comparative Toxicogenomic Database (CTD)</td> </tr> </tbody> </table> <p> *Selected MeSH terms for the AD-database: D000647,D000647,D000647,D000647,D000647,D000647,D000647,D000647,D000647,D000647,D000647,D000647,D000647,D000647,D000647,D000647,D000647,D000647,D000647,D000647,D000647,D000647,D000647,D000647,D000647,D000647 and D000647.</p> <p>HMDB-CSF can be downloaded <a href="https://csfmetabolome.ca/downloads"><strong>here</strong></a>.</p> <p>PubChemLite for Exposomics (version 1.12.0) can be found <a href="https://doi.org/10.5281/zenodo.6936117"><strong>here</strong></a>.</p>
Enhancing Diabetes Care: Exposome &Amp; Sensors
ClinicalTrials.gov study NCT06989008. IPD Sharing: YES. Countries: 1. Publications: 6.
ProspEXPO : Study of the Associations Between Hepatocellular Carcinoma and Chemical and Psychosocial Environmental EXPOsome
ClinicalTrials.gov study NCT07119957. IPD Sharing: NO. Countries: 1. Publications: 13.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.