extractox is a comprehensive R package designed to
simplify querying various chemical, toxicological, and biological
databases:
The package facilitates interaction with APIs, providing curated and
user-friendly outputs. To communicate with Pubchem,
extractox relies on the packagewebchem.
Install the package extractox from CRAN.
# From CRAN
install.packages("extractox")To get a bug fix or to use a feature from the development version,
you can install the development version of extractox from
GitHub.
# Install pak if not already installed
if (!requireNamespace("pak", quietly = TRUE)) {
install.packages("pak")
}
# Install the package from GitHub
pak::pkg_install("c1au6i0/extractox")The ICE database provides access to a variety of data related to chemical toxicity, exposure, and risk assessment. It includes data from high-throughput screening assays, in vivo studies, and computational models.
extr_ice provides access to NTP’s ICE database for
toxicological and exposure-related data.
library(extractox)
# assays is null so all assays are retrieved
ice_data <- extr_ice(casrn = c("50-00-0"), assays = NULL, verbose = FALSE)
names(ice_data)
#> [1] "assay" "endpoint" "substance_type"
#> [4] "casrn" "qsar_ready_id" "value"
#> [7] "unit" "species" "receptor_species"
#> [10] "route" "sex" "strain"
#> [13] "life_stage" "tissue" "lesion"
#> [16] "location" "assay_source" "in_vitro_assay_format"
#> [19] "reference" "reference_url" "dtxsid"
#> [22] "substance_name" "pubmed_id" "query"There are more than 2000 possible assays in ICE. The
extr_ice_assay_names() function allows to search for assay
names that match a pattern you’re interested in. Please note that
searches are case sensitive and accept regexp.
extr_ice_assay_names("Rat Acute", verbose = FALSE) # keep empty to retrieve all
#> [1] "Rat Acute Oral Toxicity" "Rat Acute Inhalation Toxicity"
#> [3] "Rat Acute Dermal Toxicity" "CATMoS, Rat Acute Oral Toxicity"The IRIS database contains information on the health effects of exposure to various substances found in the environment. It provides qualitative and quantitative health risk information.
extr_iris provides access to EPA’s IRIS database and
accepts queries CASRN or IUPAC names of chemicals.
iris_info <- extr_iris(c("glyphosate", "50-00-0"), verbose = FALSE)
names(iris_info)
#> [1] "chemical_name" "casrn"
#> [3] "exposure_route" "assessment_type"
#> [5] "critical_effect_or_tumor_type" "woe_characterization"
#> [7] "toxicity_value_type" "toxicity_value"
#> [9] "query"The extr_pprtv function allows you to extract data for
specified identifiers (CASRN or chemical names) from the EPA’s PPRTVs
database. This function retrieves the file containing all the chemicals
and processes it, but if has an argument (force) to allow
users to use a cached file or force a fresh download.
# Example usage to extract data for a specific CASRN
dat_pprtv_1 <- extr_pprtv(ids = "107-02-8", search_type = "casrn", verbose = FALSE)
# Example usage to extract data for a chemical name
dat_pprtv_2 <- extr_pprtv(ids = "Acrolein", search_type = "name", verbose = FALSE)The CompTox Chemistry Dashboard provides access to data on chemical structures, properties, and associated bioactivity data. It integratesdata from various sources to support chemical safety assessments.
extr_comptox extracts data from CompTox using either
CASRN or IUPAC names of chemicals and returns a list of
dataframes.
info_comptox <- extr_comptox(ids = c("Aspirin", "50-00-0"), verbose = FALSE)
names(info_comptox)
#> [1] "comptox_cover_sheet" "comptox_main_data"
#> [3] "comptox_abstract_sifter" "comptox_synonym_identifier"
#> [5] "comptox_related_relationships" "comptox_toxcast_assays_ac50"
#> [7] "comptox_toxval_details" "comptox_chemical_properties"The IARC Monographs database contains evaluations of the carcinogenic risks of various substances to humans. It provides detailed information about Monographs, including publication volumes and years, evaluation years, and additional relevant details.
The function extr_monograph provides access to the WHO
IARC Monographs database and accepts queries using CASRN or the names of
chemicals.
dat <- extr_monograph(
search_type = "casrn",
ids = c("105-74-8", "120-58-1"),
verbose = FALSE
)
str(dat)
#> 'data.frame': 2 obs. of 8 variables:
#> $ casrn : chr "105-74-8" "120-58-1"
#> $ agent : chr "Lauroyl peroxide" "Isosafrole"
#> $ group : chr "3" "3"
#> $ volume : chr "36, Sup 7, 71" "10, Sup 7"
#> $ volume_publication_year: chr "1999" "1987"
#> $ evaluation_year : int 1998 1987
#> $ additional_information : chr "" ""
#> $ query : chr "105-74-8" "120-58-1"
# Example usage for name search
dat2 <- extr_monograph(
search_type = "name",
ids = c("Aloe", "Schistosoma", "Styrene")
)
#> ℹ Extracting WHO IARC monographs...
#> Last updated: 2024-11-29 5:08pm (CET)PubChem provides information on chemical structures, identifiers, chemical and physical properties, biological activities, safety and toxicity information, patents, literature citations, and more.
A series of functions that rely on the webchem package
are used to extract chemical information, Globally Harmonized System
(GHS) classification data, or flavor classification
(FEMA) from PubChem.
The function extr_chem_info retrieves chemical
information of IUPAC-named chemicals. A warning is displayed if the
chemical is not found.
chem_info <- extr_chem_info(
iupac_names = c("Formaldehyde", "Aflatoxin B1"),
verbose = FALSE
)
names(chem_info)
#> [1] "cid" "iupac_name"
#> [3] "casrn" "cid_all"
#> [5] "casrn_all" "molecular_formula"
#> [7] "molecular_weight" "canonical_smiles"
#> [9] "isomeric_smiles" "inchi"
#> [11] "inchi_key" "iupac_name"
#> [13] "x_log_p" "exact_mass"
#> [15] "monoisotopic_mass" "tpsa"
#> [17] "complexity" "charge"
#> [19] "h_bond_donor_count" "h_bond_acceptor_count"
#> [21] "rotatable_bond_count" "heavy_atom_count"
#> [23] "isotope_atom_count" "atom_stereo_count"
#> [25] "defined_atom_stereo_count" "undefined_atom_stereo_count"
#> [27] "bond_stereo_count" "defined_bond_stereo_count"
#> [29] "undefined_bond_stereo_count" "covalent_unit_count"
#> [31] "volume3d" "x_steric_quadrupole3d"
#> [33] "y_steric_quadrupole3d" "z_steric_quadrupole3d"
#> [35] "feature_count3d" "feature_acceptor_count3d"
#> [37] "feature_donor_count3d" "feature_anion_count3d"
#> [39] "feature_cation_count3d" "feature_ring_count3d"
#> [41] "feature_hydrophobe_count3d" "conformer_model_rmsd3d"
#> [43] "effective_rotor_count3d" "conformer_count3d"
#> [45] "fingerprint2d" "query"Two functions are used to extract specific sections of PubChem chemical information using CASRN:
extr_pubchem_ghs extracts GHS codes.extr_pubchem_fema extracts FEMA data.ghs_info <- extr_pubchem_ghs(casrn = c("50-00-0", "64-17-5"), verbose = FALSE)
fema_info <- extr_pubchem_fema(casrn = c("50-00-0", "123-68-2"), verbose = FALSE)The function extr_tox is a wrapper used to call all the
above-mentioned functions and retrieve a list of dataframes.
info_tox <- extr_tox(casrn = c("Aspirin", "50-00-0"), verbose = FALSE)The CTP provides information about the interactions between chemicals, genes, and diseases. It helps in understanding the effects of environmental exposures on human health.
A series of functions interact with the CTP database.
extr_ctd extracts information related to chemical-gene
or pathway associations.
input_terms <- c("50-00-0", "64-17-5", "methanal", "ethanol")
ctd_association <- extr_ctd(
input_terms = input_terms,
category = "chem",
report_type = "genes_curated",
input_term_search_type = "directAssociations",
action_types = "ANY",
ontology = c("go_bp", "go_cc"),
verbose = FALSE
)
names(ctd_association)
#> [1] "chemical_name" "chemical_id" "casrn" "gene_symbol"
#> [5] "gene_id" "organism" "organism_id" "pubmed_ids"
#> [9] "query"
# Get expresssion data
ctd_expression <- extr_ctd(
input_terms = input_terms,
report_type = "cgixns",
category = "chem",
action_types = "expression",
verbose = FALSE
)
names(ctd_expression)
#> [1] "chemical_name" "chemical_id" "casrn" "gene_symbol"
#> [5] "gene_id" "organism" "organism_id" "pubmed_ids"
#> [9] "query" NA NATetramers are computationally generated information units that interrelate four data types from the CTP: a chemical, gene product, phenotype, and disease. They help in understanding the complex relationships between these entities and their combined impact on human health.
extr_tetramer extracts info related to tetramers from
CTD.
tetramer_data <- extr_tetramer(
chem = c("50-00-0", "ethanol"),
disease = "",
gene = "",
go = "",
input_term_search_type = "directAssociations",
qt_match_type = "equals",
verbose = FALSE
)
names(tetramer_data)
#> [1] "chemical" "chemical_id" "gene" "gene_id" "phenotype"
#> [6] "phenotype_id" "disease" "disease_id" "query"Please note that functions that pull data from EPA servers may
encounter issues on some Linux systems. This is because these servers do
not accept secure legacy renegotiation. On Linux system, those functions
depend on curl and OpenSSL, which have known
problems with unsafe legacy renegotiation in newer versions. One
workaround is to downgrade to curl v7.78.0 and
OpenSSL v1.1.1. However, please be aware that using these
older versions might introduce potential security vulnerabilities. Refer
to this
gist for instructions on how to downgrade curl and
OpenSSL on Ubuntu.