Package 'bioclients'

Description

Metadata only. No coordinates are downloaded; use pdb_url from the result for that, and only when something is actually going to render it.

Usage

alphafold_model(accession, ...)

Arguments

Value

A biohttp envelope whose data is a one-row tibble. See alphafold_parse_model().

References

Varadi et al. (2024). AlphaFold Protein Structure Database in 2024: providing structure coverage for over 214 million protein sequences. Nucleic Acids Research 52(D1), D368-D375. doi:10.1093/nar/gkad1011

Service documentation: https://alphafold.ebi.ac.uk/

Examples

biohttp::body_or_null(alphafold_model("P15056"))

Description

Pure.

Usage

alphafold_parse_model(body, accession = NA_character_)

Arguments

Value

A one-row tibble of accession, pdb_url, cif_url, version, mean_plddt, and source_url. NULL when there is no model.

The one-element array

The API returns a JSON array of model records, not an object, even for a single accession. Reading it as an object yields NULL for every field while looking like a successful parse. This handles either shape.

References

Varadi et al. (2024). AlphaFold Protein Structure Database in 2024: providing structure coverage for over 214 million protein sequences. Nucleic Acids Research 52(D1), D368-D375. doi:10.1093/nar/gkad1011

Service documentation: https://alphafold.ebi.ac.uk/

Examples

body <- list(list(
  pdbUrl = "https://alphafold.ebi.ac.uk/files/AF-P15056-F1-model_v6.pdb",
  latestVersion = 6, globalMetricValue = 66.38
))
alphafold_parse_model(body, "P15056")

Description

Curated clinical evidence counts for a gene

Usage

civic_gene(symbol, ...)

Arguments

Value

A biohttp envelope whose data is a one-row tibble. See civic_parse_gene().

On the interpolated query

CIViC's gene query takes the symbol as an inline string rather than a GraphQL variable, so the symbol is substituted into the query text. It is run through this package's internal clean_symbol() first, which strips everything outside ⁠[A-Za-z0-9._-]⁠. That is what stops a crafted symbol closing the string and appending its own query. Do not remove it, and do not switch to paste0() without it.

References

Griffith et al. (2017). CIViC is a community knowledgebase for expert crowdsourcing the clinical interpretation of variants in cancer. Nature Genetics 49(2), 170-174. doi:10.1038/ng.3774

Service documentation: https://civicdb.org/

Examples

biohttp::body_or_null(civic_gene("NF1"))

Description

Pure.

Usage

civic_parse_gene(body)

Arguments

Value

A one-row tibble of id, symbol, entrez_id, evidence_items, assertions, variants, and source_url. NULL when CIViC does not track the gene, which it reports as data.gene = null.

References

Griffith et al. (2017). CIViC is a community knowledgebase for expert crowdsourcing the clinical interpretation of variants in cancer. Nature Genetics 49(2), 170-174. doi:10.1038/ng.3774

Service documentation: https://civicdb.org/

Examples

body <- list(data = list(gene = list(
  id = 3867, name = "NF1", entrezId = 4763, link = "/features/3867",
  stats = list(evidenceItemCount = 56, assertionCount = 0, variantCount = 37)
)))
civic_parse_gene(body)

Description

One batched POST. The response comes back in input order.

Usage

clingen_alleles(hgvs, throttle = CLINGEN_THROTTLE, ...)

Arguments

Value

A biohttp envelope whose data is the tibble described in clingen_parse_allele().

References

Pawliczek et al. (2018). ClinGen Allele Registry links information about genetic variants. Human Mutation 39(11), 1690-1701. doi:10.1002/humu.23637

Service documentation: https://reg.clinicalgenome.org/

Examples

biohttp::body_or_null(clingen_alleles("NM_000546.6:c.215C>G"))

Description

Fetches the whole published CSV. See the note in the file header on why this is one download rather than a request per gene, and use clingen_validity_for() to narrow the result.

Usage

clingen_gene_validity(...)

Arguments

Value

A biohttp envelope whose data is the tibble described in clingen_parse_validity().

References

Strande et al. (2017). Evaluating the clinical validity of gene-disease associations: an evidence-based framework developed by the Clinical Genome Resource. The American Journal of Human Genetics 100(6), 895-906. doi:10.1016/j.ajhg.2017.04.015

Service documentation: https://search.clinicalgenome.org/

Examples

table <- biohttp::body_or_null(clingen_gene_validity())
clingen_validity_for(table, c("NF1", "TP53"))

Description

Pure.

Usage

clingen_parse_allele(element)

Arguments

Value

A one-row tibble.

An unresolved input is a row, not a gap

ClinGen answers an input it could not resolve with an object carrying no ⁠@id⁠, and an errorType/message instead. That becomes a row with resolved = FALSE and the reason, rather than being dropped. Dropping it would shift every later row onto the wrong variant, and would lose the reason the input was rejected.

References

Pawliczek et al. (2018). ClinGen Allele Registry links information about genetic variants. Human Mutation 39(11), 1690-1701. doi:10.1002/humu.23637

Service documentation: https://reg.clinicalgenome.org/

Examples

clingen_parse_allele(list(errorType = "IncorrectHgvsPosition"))

Description

Pure. One row per element, in the order returned, which is the order asked.

Usage

clingen_parse_batch(body)

Arguments

Value

A tibble with one row per element.

References

Pawliczek et al. (2018). ClinGen Allele Registry links information about genetic variants. Human Mutation 39(11), 1690-1701. doi:10.1002/humu.23637

Service documentation: https://reg.clinicalgenome.org/

Description

Pure. Takes the file as a single string, the way biohttp::get_text() returns it.

Usage

clingen_parse_validity(text)

Arguments

Value

A tibble of gene, hgnc, disease, mondo, moi, classification, sop, panel, date, and source_url, one row per curation. A gene appears once per disease it has been curated against. NULL when the file is empty or carries no header.

References

Strande et al. (2017). Evaluating the clinical validity of gene-disease associations: an evidence-based framework developed by the Clinical Genome Resource. The American Journal of Human Genetics 100(6), 895-906. doi:10.1016/j.ajhg.2017.04.015

Service documentation: https://search.clinicalgenome.org/

Examples

text <- paste(
  '"CLINGEN GENE DISEASE VALIDITY CURATIONS","",""',
  '"+++","+++","+++"',
  '"GENE SYMBOL","DISEASE LABEL","CLASSIFICATION"',
  '"+++","+++","+++"',
  '"NF1","neurofibromatosis type 1","Definitive"',
  sep = "\n"
)
clingen_parse_validity(text)

Description

Pure. Matching is case-insensitive on the symbol.

Usage

clingen_validity_for(table, symbols)

Arguments

Value

The matching rows, or NULL when none match.

References

Strande et al. (2017). Evaluating the clinical validity of gene-disease associations: an evidence-based framework developed by the Clinical Genome Resource. The American Journal of Human Genetics 100(6), 895-906. doi:10.1016/j.ajhg.2017.04.015

Service documentation: https://search.clinicalgenome.org/

Examples

table <- tibble::tibble(
  gene = c("NF1", "TP53"),
  classification = c("Definitive", "Definitive")
)
clingen_validity_for(table, "nf1")

Description

Pure. For colouring and grouping, where the full free-text significance is too granular to plot.

Usage

clinvar_category(significance)

Arguments

Details

The order of the checks matters. A conflicting record usually contains the word "pathogenic" too, so conflicting has to be tested first or every conflicting record reads as pathogenic.

Value

One of "Conflicting", "Pathogenic / likely", "Benign / likely", "Uncertain", or "Other".

References

Landrum et al. (2018). ClinVar: improving access to variant interpretations and supporting evidence. Nucleic Acids Research 46(D1), D1062-D1067. doi:10.1093/nar/gkx1153

Service documentation: https://www.ncbi.nlm.nih.gov/clinvar/

Examples

clinvar_category("Pathogenic")
clinvar_category("Conflicting interpretations of pathogenicity")

Description

Searches ClinVar for term and returns the classification of one record.

Usage

clinvar_classification(term, throttle = clinvar_throttle(), allele = NULL, ...)

Arguments

Value

A biohttp envelope whose data is a one-row tibble with the columns of clinvar_parse_record() and n_matches, the number of records that fit the request. no_data when nothing matches, including when no record has the protein change in allele.

A term can match several records

ClinVar's search is a text search, and an rsID or an HGVS name often matches more than one record. rs113488022 matches BRAF V600G and V600E, and ClinVar lists V600G first. NM_004333.6:c.1799T>A, the HGVS name of V600E, lists BRAF I208V first, a different variant at a different position.

There are two ways to get the record you mean:

• Pass a precise term. An SPDI such as NC_000007.14:140753335:A:T, or a VCV accession such as VCV000013961, matches one record.

• Pass allele, the protein change you mean, such as "V600E" or "p.Val600Glu". It is checked against each record's own list of protein changes, and the first record that has it is returned.

Without either, the first record ClinVar lists is returned, and n_matches says how many records the term matched. A value above 1 means the row may be for a different variant from the one you meant.

Only the first 20 records ClinVar lists are fetched, so allele is checked against those. An rsID or an HGVS name matches far fewer.

The NCBI API key

Set NCBI_API_KEY and both requests carry it, which raises the rate limit from 3 to 10 requests a second. It is passed as a secret_query, so it stays out of the cache key and out of every message. Without one the client works at the lower limit, and the default throttle follows: 3 a second without a key, 10 with one.

Identifying the caller

NCBI asks every client to send tool and email. They are read from BIOHTTP_CALLER_IDENTITY and BIOHTTP_CONTACT_EMAIL, the same variables biohttp builds its User-Agent from, and a blank one is omitted. They are ordinary query parameters, so they are part of the cache key.

References

Landrum et al. (2018). ClinVar: improving access to variant interpretations and supporting evidence. Nucleic Acids Research 46(D1), D1062-D1067. doi:10.1093/nar/gkx1153

Service documentation: https://www.ncbi.nlm.nih.gov/clinvar/

Examples

biohttp::body_or_null(clinvar_classification("NC_000007.14:140753335:A:T"))
biohttp::body_or_null(clinvar_classification("rs113488022", allele = "V600E"))

Description

Pure.

Usage

clinvar_conditions(germline)

Arguments

Value

A single semicolon-separated string, or NA_character_.

References

Landrum et al. (2018). ClinVar: improving access to variant interpretations and supporting evidence. Nucleic Acids Research 46(D1), D1062-D1067. doi:10.1093/nar/gkx1153

Service documentation: https://www.ncbi.nlm.nih.gov/clinvar/

Examples

clinvar_conditions(list(trait_set = list(list(trait_name = "RASopathy"))))

Description

Pure. Takes one record from the result block of an esummary response, not the whole response, because the record is keyed by UID and the caller already knows which UID it asked for.

Usage

clinvar_parse_record(record, uid = NA_character_)

Arguments

Value

A one-row tibble with uid, accession, title, significance, review_status, last_evaluated, and conditions. NULL when record is absent.

References

Landrum et al. (2018). ClinVar: improving access to variant interpretations and supporting evidence. Nucleic Acids Research 46(D1), D1062-D1067. doi:10.1093/nar/gkx1153

Service documentation: https://www.ncbi.nlm.nih.gov/clinvar/

Examples

record <- list(
  accession = "VCV000040389",
  title = "NM_004333.6(BRAF):c.1799T>G (p.Val600Gly)",
  germline_classification = list(
    description = "Pathogenic",
    review_status = "reviewed by expert panel"
  )
)
clinvar_parse_record(record, "40389")

Description

A thin wrapper over dgidb_genes().

Usage

dgidb_gene(symbol, ...)

Arguments

Value

A biohttp envelope whose data is a one-row tibble.

References

Cannon et al. (2024). DGIdb 5.0: rebuilding the drug-gene interaction database for precision medicine and drug discovery platforms. Nucleic Acids Research 52(D1), D1227-D1235. doi:10.1093/nar/gkad1040

Service documentation: https://dgidb.org/

Examples

biohttp::body_or_null(dgidb_gene("NF1"))

Description

One request for the whole list, because DGIdb's genes query already takes an array.

Usage

dgidb_genes(symbols, ...)

Arguments

Value

A biohttp envelope whose data is a tibble with one row per entry in symbols, in the same order. See dgidb_parse_genes() for the columns and for why an unknown gene is NA rather than 0.

References

Cannon et al. (2024). DGIdb 5.0: rebuilding the drug-gene interaction database for precision medicine and drug discovery platforms. Nucleic Acids Research 52(D1), D1227-D1235. doi:10.1093/nar/gkad1040

Service documentation: https://dgidb.org/

Examples

biohttp::body_or_null(dgidb_genes(c("NF1", "BRAF")))

Description

Pure. Rows come back in symbols order, one per input.

Usage

dgidb_parse_genes(body, symbols)

Arguments

Value

A tibble of symbol, concept_id, interaction_count, and source_url, one row per entry in symbols.

A real zero is not a miss

The distinction this parser exists to preserve. A gene DGIdb knows about with no recorded interactions has interaction_count = 0: that is an answer, and it means the gene is not currently druggable. A gene DGIdb has never heard of has interaction_count = NA: that is an absence of evidence.

Collapsing the two would tell a caller that an unknown gene is known not to be druggable, which is a different and much stronger claim than the data supports.

References

Cannon et al. (2024). DGIdb 5.0: rebuilding the drug-gene interaction database for precision medicine and drug discovery platforms. Nucleic Acids Research 52(D1), D1227-D1235. doi:10.1093/nar/gkad1040

Service documentation: https://dgidb.org/

Examples

body <- list(data = list(genes = list(nodes = list(
  list(name = "NF1", conceptId = "hgnc:7765", interactions = list(list(), list()))
))))
dgidb_parse_genes(body, "NF1")

Description

Genes DISEASES associates with a disease, from one channel

Usage

diseases_channel(
  doid,
  channel = c("Knowledge", "Textmining"),
  limit = 300,
  ...
)

Arguments

Value

A biohttp envelope whose data is the tibble described in diseases_parse_channel().

References

Pletscher-Frankild et al. (2015). DISEASES: text mining and data integration of disease-gene associations. Methods 74, 83-89. doi:10.1016/j.ymeth.2014.11.020

Service documentation: https://diseases.jensenlab.org/

Examples

biohttp::body_or_null(diseases_channel("DOID:0060293", "Knowledge"))

Description

Queries Knowledge and Textmining together and keeps the strongest score per gene. Read the note on diseases_merge_channels() before relying on the combined score.

Usage

diseases_gene_associations(doid, limit = 300, ...)

Arguments

Value

A biohttp envelope whose data is the tibble described in diseases_merge_channels(), with source_url added.

A failed channel fails the call

If either channel errors, its envelope is returned rather than a combined score built from the other one. A result that silently dropped the curated channel would look like a weaker association rather than a partial answer, and the envelope has no status that says "half of this is missing". Call diseases_channel() per channel to handle the halves separately.

References

Pletscher-Frankild et al. (2015). DISEASES: text mining and data integration of disease-gene associations. Methods 74, 83-89. doi:10.1016/j.ymeth.2014.11.020

Service documentation: https://diseases.jensenlab.org/

Examples

biohttp::body_or_null(diseases_gene_associations("DOID:0060293"))

Description

Pure.

Usage

diseases_merge_channels(tables)

Arguments

Value

A tibble of symbol, score, and channel, ordered by score, highest first. channel names the channel the winning score came from. NULL when there is nothing to combine.

This is a combining convention, not the source's own answer

DISEASES scores each channel separately and does not publish a combined figure. Taking the maximum treats curated and mined evidence as interchangeable, which suits a screen looking for any support at all and suits nothing that cares where the support came from. Use diseases_channel() and keep the channels apart if that distinction matters.

References

Pletscher-Frankild et al. (2015). DISEASES: text mining and data integration of disease-gene associations. Methods 74, 83-89. doi:10.1016/j.ymeth.2014.11.020

Service documentation: https://diseases.jensenlab.org/

Examples

diseases_merge_channels(list(
  tibble::tibble(symbol = "NF1", protein = NA, score = 5, channel = "Knowledge"),
  tibble::tibble(symbol = "NF1", protein = NA, score = 2, channel = "Textmining")
))

Description

Pure.

Usage

diseases_parse_channel(body, channel = NA_character_)

Arguments

Value

A tibble of symbol, protein, score, and channel, one row per associated gene. NULL when the channel has no associations. Rows with no symbol or a non-finite score are dropped, because neither is usable.

References

Pletscher-Frankild et al. (2015). DISEASES: text mining and data integration of disease-gene associations. Methods 74, 83-89. doi:10.1016/j.ymeth.2014.11.020

Service documentation: https://diseases.jensenlab.org/

Examples

body <- list(
  list(ENSP00000351015 = list(name = "NF1", score = 5)),
  list()
)
diseases_parse_channel(body, "Knowledge")

Description

The exon model for a gene

Usage

ensembl_gene_model(gene_id, ...)

Arguments

Value

A biohttp envelope whose data is the list described in ensembl_parse_gene_model().

References

Dyer et al. (2025). Ensembl 2025. Nucleic Acids Research 53(D1), D948-D957. doi:10.1093/nar/gkae1071

Service documentation: https://rest.ensembl.org/

Examples

biohttp::body_or_null(ensembl_gene_model("ENSG00000157764"))$exons

Description

Pure.

Usage

ensembl_parse_consequences(consequences)

Arguments

Details

Every consequence is returned, with its biotype, rather than only the protein-coding ones. Which biotypes are worth showing is the caller's call, and a filter here would hide the non-coding rows from a caller that wanted them.

Value

A tibble of gene, gene_id, transcript, biotype, consequence, impact, sift, and polyphen. NULL when there are none.

References

Dyer et al. (2025). Ensembl 2025. Nucleic Acids Research 53(D1), D948-D957. doi:10.1093/nar/gkae1071

Service documentation: https://rest.ensembl.org/

Examples

ensembl_parse_consequences(list(list(
  gene_symbol = "BRAF",
  transcript_id = "ENST00000646891",
  biotype = "protein_coding",
  consequence_terms = list("missense_variant"),
  impact = "MODERATE"
)))

Description

Pure.

Usage

ensembl_parse_gene_model(record)

Arguments

Value

A list of transcript, strand, region, gene_start, gene_end, and exons, a tibble of start, end, and number sorted by start. NULL when the record carries no transcript with exons.

Exon numbering follows the strand

Exons are sorted by genomic coordinate and then numbered in transcription order. On the minus strand that means the highest-coordinate exon is exon 1. Numbering by coordinate alone would put exon 1 at the wrong end of every minus-strand gene.

References

Dyer et al. (2025). Ensembl 2025. Nucleic Acids Research 53(D1), D948-D957. doi:10.1093/nar/gkae1071

Service documentation: https://rest.ensembl.org/

Examples

record <- list(
  seq_region_name = "17", start = 1, end = 900,
  Transcript = list(list(
    id = "ENST1", is_canonical = 1, strand = -1,
    Exon = list(list(start = 800, end = 900), list(start = 1, end = 100))
  ))
)
ensembl_parse_gene_model(record)$exons

Description

Pure.

Usage

ensembl_parse_vep(record)

Arguments

Value

A list of most_severe, assembly, and consequences (the tibble from ensembl_parse_consequences()). NULL when the record is empty.

References

Dyer et al. (2025). Ensembl 2025. Nucleic Acids Research 53(D1), D948-D957. doi:10.1093/nar/gkae1071

Service documentation: https://rest.ensembl.org/

Examples

ensembl_parse_vep(list(
  most_severe_consequence = "missense_variant",
  assembly_name = "GRCh38"
))

Description

Run VEP for a variant id

Usage

ensembl_vep_id(rsid, ...)

Arguments

Value

A biohttp envelope whose data is the list described in ensembl_parse_vep().

An rsID does not identify an allele

The same caveat as gnomad_frequency() and clinvar_classification(). 7:g.140753336A>T and 7:g.140753336A>C share rs113488022, so Ensembl may answer for more than one allele. The first record is used. Post coordinates with vep_variants() when the allele matters.

References

Dyer et al. (2025). Ensembl 2025. Nucleic Acids Research 53(D1), D948-D957. doi:10.1093/nar/gkae1071

Service documentation: https://rest.ensembl.org/

Examples

biohttp::body_or_null(ensembl_vep_id("rs113488022"))$consequences

Description

Asks for one row with resultType = "idlist", because only the count is wanted. Read the note in the file header: a count of 0 comes back as ok, not as no_data.

Usage

europepmc_count(query, ...)

Arguments

Value

A biohttp envelope whose data is a list of count, query, and source_url.

References

Ferguson et al. (2021). Europe PMC in 2020. Nucleic Acids Research 49(D1), D1507-D1514. doi:10.1093/nar/gkaa994

Service documentation: https://europepmc.org/

Examples

biohttp::body_or_null(europepmc_count(europepmc_query("NF1")))$count

Description

Pure. 0 is a real count and comes back as 0L; only an absent hitCount is NA.

Usage

europepmc_parse_count(body)

Arguments

Value

A single integer, or NA_integer_.

References

Ferguson et al. (2021). Europe PMC in 2020. Nucleic Acids Research 49(D1), D1507-D1514. doi:10.1093/nar/gkaa994

Service documentation: https://europepmc.org/

Examples

europepmc_parse_count(list(hitCount = 2530))
europepmc_parse_count(list(hitCount = 0))

Description

Pure.

Usage

europepmc_parse_results(body)

Arguments

Details

source_id is the citable identifier: ⁠PMID:<n>⁠ where there is a PMID, and ⁠<source>:<id>⁠ otherwise, because a preprint or a patent record has no PMID but is still groundable.

Value

A tibble of title, authors, year, journal, pmid, doi, cited_by, source, source_id, and source_url, newest first, which is the order Europe PMC returns. NULL when nothing matched.

References

Ferguson et al. (2021). Europe PMC in 2020. Nucleic Acids Research 49(D1), D1507-D1514. doi:10.1093/nar/gkaa994

Service documentation: https://europepmc.org/

Examples

body <- list(resultList = list(result = list(list(
  id = "36197410",
  source = "MED",
  pmid = "36197410",
  title = "TP53 or Not TP53",
  authorString = "Green SD.",
  journalTitle = "Clin Cancer Res",
  pubYear = "2022"
))))
europepmc_parse_results(body)

Description

Each term is quoted so a multi-word term matches as a phrase rather than as loose words, and the terms are ANDed. Quoting also stops a term containing a space or a colon from being read as query syntax.

Usage

europepmc_query(...)

Arguments

Value

A single query string, or NULL when nothing usable was given.

References

Ferguson et al. (2021). Europe PMC in 2020. Nucleic Acids Research 49(D1), D1507-D1514. doi:10.1093/nar/gkaa994

Service documentation: https://europepmc.org/

Examples

europepmc_query("BRAF")
europepmc_query("BRAF", "V600E")

Description

Search Europe PMC

Usage

europepmc_search(query, limit = 15, ...)

Arguments

Value

A biohttp envelope whose data is a list of results (the tibble from europepmc_parse_results()), count (Europe PMC's total, not the page size), and query.

References

Ferguson et al. (2021). Europe PMC in 2020. Nucleic Acids Research 49(D1), D1507-D1514. doi:10.1093/nar/gkaa994

Service documentation: https://europepmc.org/

Examples

biohttp::body_or_null(europepmc_search(europepmc_query("BRAF", "V600E")))

Description

How intolerant a gene is to variation. pli and loeuf summarize loss-of-function intolerance; the Z-scores summarize missense and synonymous depletion.

Usage

gnomad_constraint(symbol, reference_genome = "GRCh38", ...)

Arguments

Value

A biohttp envelope whose data is a one-row tibble. See gnomad_parse_constraint().

References

Chen et al. (2024). A genomic mutational constraint map using variation in 76,156 human genomes. Nature 625(7993), 92-100. doi:10.1038/s41586-023-06045-0

Service documentation: https://gnomad.broadinstitute.org/

Examples

biohttp::body_or_null(gnomad_constraint("BRAF"))

Description

Batched through GraphQL aliases, so a gene list costs a handful of requests rather than one per gene. Chunked at chunk_size to stay under gnomAD's query cost cap of 25. See GNOMAD_CHUNK.

Usage

gnomad_constraints(
  symbols,
  reference_genome = "GRCh38",
  chunk_size = GNOMAD_CHUNK,
  ...
)

Arguments

Value

A biohttp envelope whose data is a tibble with one row per entry in symbols, in the same order.

References

Chen et al. (2024). A genomic mutational constraint map using variation in 76,156 human genomes. Nature 625(7993), 92-100. doi:10.1038/s41586-023-06045-0

Service documentation: https://gnomad.broadinstitute.org/

Examples

biohttp::body_or_null(gnomad_constraints(c("BRAF", "TP53", "EGFR")))

Description

Batched through GraphQL aliases and chunked at chunk_size to stay under gnomAD's query cost cap of 25, which was verified for this query. See GNOMAD_CHUNK. Dispatched through biohttp::post_json_many(), so only the chunks the cache is missing go over the wire.

Usage

gnomad_frequencies(
  variant_ids,
  dataset = GNOMAD_DATASET,
  reference_genome = "GRCh38",
  chunk_size = GNOMAD_CHUNK,
  ...
)

Arguments

Details

A failed chunk yields a row of NA per variant rather than taking the whole call down, following gnomad_constraints(). A variant gnomAD has no record of is a row of NA too, because that is an answer.

Value

A biohttp envelope whose data is a tibble with one row per entry in variant_ids, in the same order. See gnomad_parse_variant().

References

Chen et al. (2024). A genomic mutational constraint map using variation in 76,156 human genomes. Nature 625(7993), 92-100. doi:10.1038/s41586-023-06045-0

Service documentation: https://gnomad.broadinstitute.org/

Examples

biohttp::body_or_null(gnomad_frequencies(
  c("17-7676154-G-C", "7-117559590-ATCT-A")
))

Description

Looked up by rsID, which is what gnomAD's variant query takes.

Usage

gnomad_frequency(rsid, dataset = GNOMAD_DATASET, ...)

Arguments

Value

A biohttp envelope whose data is the list described in gnomad_parse_frequency().

An rsID does not identify an allele

7:g.140753336A>T and 7:g.140753336A>C both map to rs113488022. Any lookup routed through an rsID is therefore lossy, and it fails silently while looking entirely plausible. The canonical key is ⁠(assembly, chromosome, position, ref, alt)⁠.

This function is faithful to what gnomAD's variant query accepts, so the limitation is gnomAD's rather than this package's. Know about it before you rely on the answer for a multi-allelic site.

References

Chen et al. (2024). A genomic mutational constraint map using variation in 76,156 human genomes. Nature 625(7993), 92-100. doi:10.1038/s41586-023-06045-0

Service documentation: https://gnomad.broadinstitute.org/

Examples

biohttp::body_or_null(gnomad_frequency("rs113488022"))

Description

Looked up by chrom-pos-ref-alt, which names one allele exactly. This is the lookup gnomad_frequency() cannot do, because an rsID is shared by every allele at a site.

Usage

gnomad_frequency_by_id(
  variant_id,
  dataset = GNOMAD_DATASET,
  reference_genome = "GRCh38",
  ...
)

Arguments

Value

A biohttp envelope whose data is the one-row tibble described in gnomad_parse_variant(). no_data when gnomAD has no record of the variant.

References

Chen et al. (2024). A genomic mutational constraint map using variation in 76,156 human genomes. Nature 625(7993), 92-100. doi:10.1038/s41586-023-06045-0

Service documentation: https://gnomad.broadinstitute.org/

Examples

biohttp::body_or_null(gnomad_frequency_by_id("17-7676154-G-C"))

Description

Pure. Takes an already-parsed response body and never touches the network.

Usage

gnomad_parse_constraint(body, symbol = NA_character_)

Arguments

Details

loeuf is gnomAD's oe_lof_upper. The two names are the same number, and the field is called oe_lof_upper in the API but LOEUF everywhere else, including in the ranking models that consume it. Both names appear here so a reader of either can find it.

Value

A one-row tibble with symbol, pli, loeuf, oe_lof, oe_mis, mis_z, syn_z, and lof_z. NULL when the gene has no constraint block, which is common and is an answer rather than a fault.

References

Chen et al. (2024). A genomic mutational constraint map using variation in 76,156 human genomes. Nature 625(7993), 92-100. doi:10.1038/s41586-023-06045-0

Service documentation: https://gnomad.broadinstitute.org/

Examples

body <- list(data = list(gene = list(
  gnomad_constraint = list(pli = 1, oe_lof_upper = 0.23)
)))
gnomad_parse_constraint(body, "BRAF")

Description

Pure. The batched query uses GraphQL aliases g1, g2, and so on, so the response is keyed by alias rather than by symbol. Rows are mapped back by alias index rather than by the echoed symbol, so the result stays aligned even for a gene gnomAD does not return a symbol for.

Usage

gnomad_parse_constraints(body, symbols)

Arguments

Value

A tibble with one row per entry in symbols, same order. A gene with no constraint block gets a row of NA rather than being dropped.

References

Chen et al. (2024). A genomic mutational constraint map using variation in 76,156 human genomes. Nature 625(7993), 92-100. doi:10.1038/s41586-023-06045-0

Service documentation: https://gnomad.broadinstitute.org/

Examples

body <- list(data = list(
  g1 = list(symbol = "BRAF", gnomad_constraint = list(oe_lof_upper = 0.23)),
  g2 = list(symbol = "TP53", gnomad_constraint = NULL)
))
gnomad_parse_constraints(body, c("BRAF", "TP53"))

Description

Pure.

Usage

gnomad_parse_frequency(body, dataset = GNOMAD_DATASET)

Arguments

Value

A list of variant_id, dataset, exome, genome, and populations, or NULL when the body carries no variant.

References

Chen et al. (2024). A genomic mutational constraint map using variation in 76,156 human genomes. Nature 625(7993), 92-100. doi:10.1038/s41586-023-06045-0

Service documentation: https://gnomad.broadinstitute.org/

Examples

body <- list(data = list(variant = list(
  variant_id = "7-140753336-A-T",
  exome = list(af = 0.001, ac = 2, an = 2000)
)))
gnomad_parse_frequency(body)$variant_id

Description

Pure. Sums the exome and genome sample sets per ancestry group.

Usage

gnomad_parse_populations(exome_pops, genome_pops)

Arguments

Details

Sex-split ids such as nfe_XX and the bare XX/XY breakdowns are dropped, by keeping only the known ancestry codes, so the result is one row per ancestry rather than a mix of ancestries and sexes.

Value

A tibble of pop, label, ac, an, af, sorted by frequency descending. NULL when there is nothing to report.

References

Chen et al. (2024). A genomic mutational constraint map using variation in 76,156 human genomes. Nature 625(7993), 92-100. doi:10.1038/s41586-023-06045-0

Service documentation: https://gnomad.broadinstitute.org/

Examples

gnomad_parse_populations(
  list(list(id = "nfe", ac = 3, an = 1000)),
  list(list(id = "nfe", ac = 1, an = 500))
)

Description

Pure. The flat, one-row form of a variant record, for a variant table that wants a frequency per row. gnomad_parse_frequency() is the nested form with the per-ancestry table.

Usage

gnomad_parse_variant(body, variant_id = NA_character_)

Arguments

Value

A one-row tibble of variant_id, rsid, exome_af, exome_ac, exome_an, exome_nhomalt, genome_af, genome_ac, genome_an, genome_nhomalt, grpmax_af, grpmax_an, grpmax_id, faf95, faf95_pop, and filters. NULL when the body carries no variant, which is how gnomAD answers for a variant it has never seen.

grpmax is derived

The API does not serve a group maximum. It is computed here from the per-group counts, exome and genome summed, leaving out the bottlenecked groups and the remaining bucket the same way gnomAD does. See GNOMAD_GRPMAX_EXCLUDED.

References

Chen et al. (2024). A genomic mutational constraint map using variation in 76,156 human genomes. Nature 625(7993), 92-100. doi:10.1038/s41586-023-06045-0

Service documentation: https://gnomad.broadinstitute.org/

Examples

body <- list(data = list(variant = list(
  variant_id = "17-7676154-G-C",
  rsids = list("rs1042522"),
  exome = list(af = 0.72, ac = 1046941, an = 1461558, homozygote_count = 380188)
)))
gnomad_parse_variant(body, "17-7676154-G-C")

Description

Pure. The batched query uses aliases v1, v2, and so on, so rows are mapped back by alias index rather than by the echoed id. A variant gnomAD has not seen comes back as null under its alias, with a "Variant not found" entry in errors, and becomes a row of NA.

Usage

gnomad_parse_variants(body, variant_ids)

Arguments

Value

A tibble with one row per entry in variant_ids, same order. See gnomad_parse_variant() for the columns.

References

Chen et al. (2024). A genomic mutational constraint map using variation in 76,156 human genomes. Nature 625(7993), 92-100. doi:10.1038/s41586-023-06045-0

Service documentation: https://gnomad.broadinstitute.org/

Examples

body <- list(data = list(
  v1 = list(variant_id = "17-7676154-G-C", exome = list(af = 0.72)),
  v2 = NULL
))
gnomad_parse_variants(body, c("17-7676154-G-C", "17-7676154-G-GTTTTT"))

Description

Pure. gnomAD's variant query takes chrom-pos-ref-alt, with no chr prefix and the alleles in upper case. Vectorised over its arguments.

Usage

gnomad_variant_id(chrom, pos, ref, alt)

Arguments

Value

A character vector of ids such as "1-55516888-G-GA".

References

Chen et al. (2024). A genomic mutational constraint map using variation in 76,156 human genomes. Nature 625(7993), 92-100. doi:10.1038/s41586-023-06045-0

Service documentation: https://gnomad.broadinstitute.org/

Examples

gnomad_variant_id("chr1", 55516888, "g", "ga")

Description

Resolve a gene to GTEx's versioned GENCODE id

Usage

gtex_gene_reference(gene, ...)

Arguments

Value

A biohttp envelope whose data is the tibble described in gtex_parse_reference().

Why this call exists

GTEx expression queries need a versioned GENCODE id such as ENSG00000141510.16. MyGene and most other sources return the unversioned ENSG00000141510, and GTEx returns nothing for it. There is no way to derive the version, so it has to be looked up here first.

This is the reason a GTEx expression lookup costs two requests.

References

The GTEx Consortium (2020). The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 369(6509), 1318-1330. doi:10.1126/science.aaz1776

Service documentation: https://gtexportal.org/home/

Examples

biohttp::body_or_null(gtex_gene_reference("TP53"))$gencode_id

Description

Two requests: one to resolve the versioned GENCODE id, one for the expression. See gtex_gene_reference() for why the first is unavoidable.

Usage

gtex_median_expression(
  gene = NULL,
  gencode_id = NULL,
  dataset = GTEX_DATASET,
  ...
)

Arguments

Details

Pass gencode_id directly to skip the lookup when you already have a versioned id.

Value

A biohttp envelope whose data is the tibble described in gtex_parse_expression().

References

The GTEx Consortium (2020). The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 369(6509), 1318-1330. doi:10.1126/science.aaz1776

Service documentation: https://gtexportal.org/home/

Examples

biohttp::body_or_null(gtex_median_expression("TP53"))

Description

Pure. Tissue ids arrive underscore-separated (Adipose_Subcutaneous) and are returned both raw and as a readable label.

Usage

gtex_parse_expression(body)

Arguments

Value

A tibble of tissue_id, tissue, median_tpm, and gencode_id. Rows with no median are dropped. NULL when there is nothing usable.

References

The GTEx Consortium (2020). The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 369(6509), 1318-1330. doi:10.1126/science.aaz1776

Service documentation: https://gtexportal.org/home/

Examples

body <- list(data = list(list(
  median = 22.459, tissueSiteDetailId = "Adipose_Subcutaneous",
  gencodeId = "ENSG00000141510.16", unit = "TPM"
)))
gtex_parse_expression(body)

Description

Pure.

Usage

gtex_parse_reference(body)

Arguments

Value

A tibble of symbol, gencode_id, entrez, chromosome, and gene_type. NULL when the gene is not in the reference.

References

The GTEx Consortium (2020). The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 369(6509), 1318-1330. doi:10.1126/science.aaz1776

Service documentation: https://gtexportal.org/home/

Examples

body <- list(data = list(list(
  geneSymbol = "TP53", gencodeId = "ENSG00000141510.16", entrezGeneId = 7157
)))
gtex_parse_reference(body)

Description

Keyed by Ensembl gene id, which is what HPA's per-gene JSON path takes. Resolve a symbol first with mygene_gene().

Usage

hpa_gene(ensembl, ...)

Arguments

Value

A biohttp envelope whose data is a one-row tibble. See hpa_parse_gene().

References

Uhlén et al. (2015). Tissue-based map of the human proteome. Science 347(6220), 1260419. doi:10.1126/science.1260419

Service documentation: https://www.proteinatlas.org/

Examples

biohttp::body_or_null(hpa_gene("ENSG00000141510"))

Description

Pure.

Usage

hpa_parse_gene(body, ensembl = NA_character_)

Arguments

Details

protein_class and disease_involvement are list columns, because a gene carries any number of tags and flattening them to one string would make them unusable without re-splitting.

Value

A one-row tibble of symbol, ensembl, uniprot, protein_class, disease_involvement, and source_url. NULL when the record is empty.

References

Uhlén et al. (2015). Tissue-based map of the human proteome. Science 347(6220), 1260419. doi:10.1126/science.1260419

Service documentation: https://www.proteinatlas.org/

Examples

body <- list(
  Gene = "TP53",
  Ensembl = "ENSG00000141510",
  Uniprot = list("P04637"),
  `Protein class` = list("Cancer-related genes", "Transcription factors"),
  `Disease involvement` = list("Tumor suppressor")
)
hpa_parse_gene(body, "ENSG00000141510")

Description

HPO's annotation for a gene

Usage

hpo_gene_annotation(entrez, ...)

Arguments

Value

A biohttp envelope whose data is a list of two tibbles, diseases and phenotypes, either of which may be NULL. Two tables rather than one, because they are independent arrays in the response and joining them would invent a disease-to-phenotype mapping the response does not carry. It also carries source_url. See hpo_parse_diseases() and hpo_parse_phenotypes().

References

Gargano et al. (2024). The Human Phenotype Ontology in 2024: phenotypes around the world. Nucleic Acids Research 52(D1), D1333-D1346. doi:10.1093/nar/gkad1005

Service documentation: https://hpo.jax.org/

Examples

biohttp::body_or_null(hpo_gene_annotation(7157))$diseases

Description

Pure.

Usage

hpo_parse_diseases(body)

Arguments

Details

Every row is grounded by an OMIM, ORPHA, or DECIPHER id in id, and by a MONDO id in mondo where HPO has one.

Value

A tibble of id, name, mondo, and description, one row per associated disease. NULL when there are none.

References

Gargano et al. (2024). The Human Phenotype Ontology in 2024: phenotypes around the world. Nucleic Acids Research 52(D1), D1333-D1346. doi:10.1093/nar/gkad1005

Service documentation: https://hpo.jax.org/

Examples

body <- list(diseases = list(
  list(
    id = "OMIM:151623",
    name = "Li-Fraumeni syndrome",
    mondoId = "MONDO:0018875",
    description = NULL
  )
))
hpo_parse_diseases(body)

Description

Pure.

Usage

hpo_parse_phenotypes(body)

Arguments

Details

These are the HPO terms observed in the diseases the gene is associated with, not a separate assertion about the gene.

Value

A tibble of id and name, one row per phenotype term. NULL when there are none.

References

Gargano et al. (2024). The Human Phenotype Ontology in 2024: phenotypes around the world. Nucleic Acids Research 52(D1), D1333-D1346. doi:10.1093/nar/gkad1005

Service documentation: https://hpo.jax.org/

Examples

body <- list(phenotypes = list(list(id = "HP:0003002", name = "Breast carcinoma")))
hpo_parse_phenotypes(body)

Description

Pure.

Usage

hpo_parse_search(body)

Arguments

Value

A tibble of id, name, definition, and descendant_count, best match first, which is the order JAX returns. NULL when nothing matched.

References

Gargano et al. (2024). The Human Phenotype Ontology in 2024: phenotypes around the world. Nucleic Acids Research 52(D1), D1333-D1346. doi:10.1093/nar/gkad1005

Service documentation: https://hpo.jax.org/

Examples

body <- list(terms = list(list(id = "HP:0001250", name = "Seizure")))
hpo_parse_search(body)

Description

Pure.

Usage

hpo_parse_term(body)

Arguments

Value

A one-row tibble of id, name, definition, comment, descendant_count, and the list columns synonyms and xrefs. NULL when the body carries no term.

References

Gargano et al. (2024). The Human Phenotype Ontology in 2024: phenotypes around the world. Nucleic Acids Research 52(D1), D1333-D1346. doi:10.1093/nar/gkad1005

Service documentation: https://hpo.jax.org/

Examples

body <- list(id = "HP:0001250", name = "Seizure", synonyms = list("Seizures"))
hpo_parse_term(body)

Description

Search HPO terms by free text

Usage

hpo_search(query, limit = 10, ...)

Arguments

Value

A biohttp envelope whose data is the tibble described in hpo_parse_search().

References

Gargano et al. (2024). The Human Phenotype Ontology in 2024: phenotypes around the world. Nucleic Acids Research 52(D1), D1333-D1346. doi:10.1093/nar/gkad1005

Service documentation: https://hpo.jax.org/

Examples

biohttp::body_or_null(hpo_search("seizure"))

Description

Resolve one HP id to its term

Usage

hpo_term(id, ...)

Arguments

Value

A biohttp envelope whose data is the one-row tibble described in hpo_parse_term().

References

Gargano et al. (2024). The Human Phenotype Ontology in 2024: phenotypes around the world. Nucleic Acids Research 52(D1), D1333-D1346. doi:10.1093/nar/gkad1005

Service documentation: https://hpo.jax.org/

Examples

biohttp::body_or_null(hpo_term("HP:0001250"))

Description

Resolves the mouse ortholog first, then queries the phenotype core by its MGI accession. See the note in the file header on why the second query cannot be made from the human symbol.

Usage

impc_gene_phenotypes(symbol, ...)

Arguments

Value

A biohttp envelope whose data is a list of mgi, marker_symbol, phenotypes (the tibble from impc_parse_phenotypes()), and source_url.

References

Groza et al. (2023). The International Mouse Phenotyping Consortium: comprehensive knockout phenotyping underpinning the study of human disease. Nucleic Acids Research 51(D1), D1038-D1045. doi:10.1093/nar/gkac972

Service documentation: https://www.mousephenotype.org/

Examples

biohttp::body_or_null(impc_gene_phenotypes("NF1"))$phenotypes

Description

The mouse ortholog IMPC holds for a human gene

Usage

impc_mouse_ortholog(symbol, ...)

Arguments

Value

A biohttp envelope whose data is the one-row tibble described in impc_parse_ortholog().

References

Groza et al. (2023). The International Mouse Phenotyping Consortium: comprehensive knockout phenotyping underpinning the study of human disease. Nucleic Acids Research 51(D1), D1038-D1045. doi:10.1093/nar/gkac972

Service documentation: https://www.mousephenotype.org/

Examples

biohttp::body_or_null(impc_mouse_ortholog("NF1"))

Description

Pure.

Usage

impc_parse_ortholog(body)

Arguments

Value

A one-row tibble of mgi and marker_symbol. NULL when the gene has no IMPC mouse ortholog, which includes a document carrying no usable accession.

References

Groza et al. (2023). The International Mouse Phenotyping Consortium: comprehensive knockout phenotyping underpinning the study of human disease. Nucleic Acids Research 51(D1), D1038-D1045. doi:10.1093/nar/gkac972

Service documentation: https://www.mousephenotype.org/

Examples

body <- list(response = list(docs = list(
  list(mgi_accession_id = "MGI:97306", marker_symbol = "Nf1")
)))
impc_parse_ortholog(body)

Description

Pure.

Usage

impc_parse_phenotypes(body, mgi = NA_character_)

Arguments

Value

A tibble of mp_id, mp_name, allele, allele_symbol, zygosity, and source_url, one row per distinct phenotype term. NULL when there are none.

One row per term, not per observation

IMPC reports a document per phenotype, sex, zygosity, and parameter combination, so the same term recurs many times over. Rows are collapsed to distinct mp_id, keeping the first occurrence. Counting the raw documents would report a gene's phenotype breadth several times over.

mp_id is an MP term for most phenotypes and an MPATH term for pathology findings. Both appear in the same field.

References

Groza et al. (2023). The International Mouse Phenotyping Consortium: comprehensive knockout phenotyping underpinning the study of human disease. Nucleic Acids Research 51(D1), D1038-D1045. doi:10.1093/nar/gkac972

Service documentation: https://www.mousephenotype.org/

Examples

body <- list(response = list(docs = list(
  list(
    mp_term_id = "MP:0011100",
    mp_term_name = "preweaning lethality, complete penetrance",
    allele_accession_id = "MGI:4364806",
    zygosity = "homozygote"
  )
)))
impc_parse_phenotypes(body, "MGI:97306")

Description

Associations with an entity on one end

Usage

monarch_associations(entity, end = c("subject", "object"), limit = 200, ...)

Arguments

Value

A biohttp envelope whose data is the tibble described in monarch_parse_associations().

One direction per call

end picks which end of the association the entity sits on. Both directions are two calls, and concatenating them returns any association that has the entity on both ends twice. Collapsing those, and deciding what to do with the publications on each copy, is the caller's model rather than Monarch's answer, so this function does neither.

References

Putman et al. (2024). The Monarch Initiative in 2024: an analytic platform integrating phenotypes, genes and diseases across species. Nucleic Acids Research 52(D1), D938-D949. doi:10.1093/nar/gkad1082

Service documentation: https://monarchinitiative.org/

Examples

biohttp::body_or_null(monarch_associations("MONDO:0007947"))

Description

Keyed on the HGNC id, which mygene_gene() returns as hgnc. The rows come back in the shape monarch_parse_associations() describes, so the phenotype term is object and its label is object_label.

Usage

monarch_gene_phenotypes(hgnc, limit = 50, ...)

Arguments

Value

A biohttp envelope whose data is a list of associations (the tibble) and total.

References

Putman et al. (2024). The Monarch Initiative in 2024: an analytic platform integrating phenotypes, genes and diseases across species. Nucleic Acids Research 52(D1), D938-D949. doi:10.1093/nar/gkad1082

Service documentation: https://monarchinitiative.org/

Examples

biohttp::body_or_null(monarch_gene_phenotypes("11998"))$associations

Description

Monarch's entity route takes HGNC:11998. MyGene returns the bare digits. Exported because a caller assembling its own entity id needs the same rule.

Usage

monarch_hgnc_id(hgnc)

Arguments

Value

A single string, or NULL when there is nothing usable.

References

Putman et al. (2024). The Monarch Initiative in 2024: an analytic platform integrating phenotypes, genes and diseases across species. Nucleic Acids Research 52(D1), D938-D949. doi:10.1093/nar/gkad1082

Service documentation: https://monarchinitiative.org/

Examples

monarch_hgnc_id("11998")
monarch_hgnc_id("hgnc:11998")

Description

Pure.

Usage

monarch_parse_associations(body)

Arguments

Details

One row per association, exactly as Monarch sent it. Nothing is dropped and nothing is merged. publications is a list column because an association carries any number of them, and null and an array both occur.

Value

A tibble of subject, subject_label, subject_category, predicate, object, object_label, object_category, primary_knowledge_source, knowledge_level, and publications. NULL when there are no associations.

References

Putman et al. (2024). The Monarch Initiative in 2024: an analytic platform integrating phenotypes, genes and diseases across species. Nucleic Acids Research 52(D1), D938-D949. doi:10.1093/nar/gkad1082

Service documentation: https://monarchinitiative.org/

Examples

body <- list(items = list(list(
  subject = "MONDO:0017309",
  subject_label = "neonatal Marfan syndrome",
  predicate = "biolink:has_phenotype",
  object = "HP:0001653",
  object_label = "Mitral regurgitation",
  publications = NULL
)))
monarch_parse_associations(body)

Description

Pure.

Usage

monarch_parse_search(body)

Arguments

Value

A tibble of id, name, category, description, taxon, and source_url, one row per match. NULL when there are none.

Read the taxon

A gene symbol is not unique across species in Monarch, and a search returns the orthologs alongside the human gene under the same name. taxon is the only column that tells them apart. It is NA for a disease or a phenotype, which are not species-scoped.

References

Putman et al. (2024). The Monarch Initiative in 2024: an analytic platform integrating phenotypes, genes and diseases across species. Nucleic Acids Research 52(D1), D938-D949. doi:10.1093/nar/gkad1082

Service documentation: https://monarchinitiative.org/

Examples

body <- list(items = list(list(
  id = "HGNC:3603",
  name = "FBN1",
  category = "biolink:Gene",
  full_name = "fibrillin 1",
  in_taxon_label = "Homo sapiens"
)))
monarch_parse_search(body)

Description

Turns typed text into the entity id the other calls need.

Usage

monarch_search(text, limit = 10, ...)

Arguments

Value

A biohttp envelope whose data is a list of matches (the tibble from monarch_parse_search()) and total. total is Monarch's own count, not the page size, so a caller can say how much was left behind rather than presenting the first few as everything.

References

Putman et al. (2024). The Monarch Initiative in 2024: an analytic platform integrating phenotypes, genes and diseases across species. Nucleic Acids Research 52(D1), D938-D949. doi:10.1093/nar/gkad1082

Service documentation: https://monarchinitiative.org/

Examples

biohttp::body_or_null(monarch_search("Marfan syndrome"))$matches

Description

Look up one gene

Usage

mygene_gene(symbol, species = "human", ...)

Arguments

Value

A biohttp envelope whose data is a one-row tibble. See mygene_parse_hits() for the columns.

References

Xin et al. (2016). High-performance web services for querying gene and variant annotation. Genome Biology 17, 91. doi:10.1186/s13059-016-0953-9

Service documentation: https://mygene.info/

Examples

res <- mygene_gene("TP53")
biohttp::body_or_null(res)

Description

The batch POST, which is the reason this client is worth installing. An N-symbol list is one round trip rather than N, and the round trip is where essentially all the time goes.

Usage

mygene_genes(symbols, species = "human", chunk_size = MYGENE_BATCH, ...)

Arguments

Details

MyGene takes at most 1000 identifiers per POST, see MYGENE_BATCH. A longer list is chunked, the chunks are dispatched through biohttp::post_json_many(), and the hits are merged back onto symbols in input order. A chunk that failed yields a row of NA per identifier rather than taking the whole call down, following gnomad_constraints(); only when every chunk failed is the failing envelope returned.

Value

A biohttp envelope whose data is a tibble with one row per entry in symbols, in the same order.

References

Xin et al. (2016). High-performance web services for querying gene and variant annotation. Genome Biology 17, 91. doi:10.1186/s13059-016-0953-9

Service documentation: https://mygene.info/

Examples

res <- mygene_genes(c("TP53", "BRCA1", "EGFR"))
biohttp::body_or_null(res)

Description

Pure. The batch ⁠/query⁠ POST returns a flat array where each element echoes its input query. An ambiguous query yields several elements, best ⁠_score⁠ first, and an unmatched one yields an element with notfound = true.

Usage

mygene_parse_batch(body, symbols)

Arguments

Details

Hits are grouped by the echoed query and resolved through mygene_pick_hit(), then mapped back onto symbols in input order, so a caller zips the result onto its input by position. An unmatched or invalid token yields a row of NA rather than being dropped, because a shorter table would silently shift every row after it.

Value

A tibble with one row per entry in symbols, same order, with the columns of mygene_parse_hits(). The note there on ensembl_gene and genomic_pos applies here too.

References

Xin et al. (2016). High-performance web services for querying gene and variant annotation. Genome Biology 17, 91. doi:10.1186/s13059-016-0953-9

Service documentation: https://mygene.info/

Examples

body <- list(
  list(query = "TP53", symbol = "TP53", entrezgene = "7157"),
  list(query = "NOPE", notfound = TRUE)
)
mygene_parse_batch(body, c("TP53", "NOPE"))

Description

Pure. Takes an already-parsed response body and never touches the network, so it is tested directly against a stored response.

Usage

mygene_parse_hits(body, symbol = NA_character_)

Arguments

Value

A one-row tibble with symbol, name, summary, entrez, ensembl_gene, uniprot, hgnc, and type_of_gene. NULL when the body carries no usable hit. When a gene has several Ensembl ids, ensembl_gene is the one on a reference chromosome (1 to 22, X, Y or MT), and NA when none of them is. Picking it needs genomic_pos in the body, which mygene_gene() and mygene_genes() ask for. A body fetched without it gives NA for any gene with several ids.

References

Xin et al. (2016). High-performance web services for querying gene and variant annotation. Genome Biology 17, 91. doi:10.1186/s13059-016-0953-9

Service documentation: https://mygene.info/

Examples

body <- list(hits = list(list(
  symbol = "TP53",
  name = "tumor protein p53",
  entrezgene = "7157"
)))
mygene_parse_hits(body, "TP53")

Description

MyGene's ⁠_score⁠ can rank a fuzzy alias or retired match to a different gene above the exact symbol match. Querying "TTN" with the alias and retired scopes returns TTR (entrez 7276, score 19.07) ahead of TTN (7273, score 18.29). Taking the top-scored hit therefore returns the wrong gene, quietly.

Usage

mygene_pick_hit(hits, token)

Arguments

Details

So a hit whose official symbol equals the token wins, case-insensitively. Only when none does, which is the case for a deliberate alias such as "p53", does MyGene's own best-scored hit get used.

This is the single most important thing in this file. It is exported so a caller assembling its own hits can apply the same rule.

Value

One hit record, or NULL when hits is empty.

References

Xin et al. (2016). High-performance web services for querying gene and variant annotation. Genome Biology 17, 91. doi:10.1186/s13059-016-0953-9

Service documentation: https://mygene.info/

Examples

hits <- list(
  list(symbol = "TTR", entrezgene = "7276"),
  list(symbol = "TTN", entrezgene = "7273")
)
mygene_pick_hit(hits, "TTN")$entrezgene

Description

Pure. MyVariant indexes variants by an HGVS g. string, so a caller holding chromosome, position, ref and alt needs this to ask about one.

Usage

myvariant_id(chrom, pos, ref, alt)

Arguments

Value

A single string.

This is not variant identity

This formats a variant the way one service wants it written. It is not a canonical variant key and it does not normalize anything. The canonical key is ⁠(assembly, chromosome, position, ref, alt)⁠, and turning arbitrary input into one is vcfcanon's job, not this package's.

References

Xin et al. (2016). High-performance web services for querying gene and variant annotation. Genome Biology 17, 91. doi:10.1186/s13059-016-0953-9

Service documentation: https://myvariant.info/

Examples

myvariant_id("17", 7676154, "G", "C")
myvariant_id("chr1", 100, "AT", "A")

Description

Pure. One row per requested id, in the order asked.

Usage

myvariant_parse_batch(body, ids)

Arguments

Value

A tibble with one row per entry in ids, plus an id column.

notfound is an answer, not an error

MyVariant reports an unknown variant as HTTP 200 with notfound: true. That is the source saying it has nothing, which is different from the call failing. Such rows come back as NA rather than being dropped, so a caller zipping by position stays aligned.

References

Xin et al. (2016). High-performance web services for querying gene and variant annotation. Genome Biology 17, 91. doi:10.1186/s13059-016-0953-9

Service documentation: https://myvariant.info/

Examples

body <- list(list(query = "chr17:g.7676154G>C", dbnsfp = list(
  genename = list("TP53"), cadd = list(raw_rankscore = 0.17)
)))
myvariant_parse_batch(body, "chr17:g.7676154G>C")

Description

Pure.

Usage

myvariant_parse_record(record)

Arguments

Value

A one-row tibble of gene, revel, cadd, clinpred, alphamissense, and clinvar_sig.

CADD's key is not what you would guess

Every other dbNSFP predictor exposes ⁠<name>.rankscore⁠. CADD does not: it is cadd.raw_rankscore, and there is no plain cadd.rankscore. Following the pattern gives NA for CADD on every variant with nothing to indicate it.

References

Xin et al. (2016). High-performance web services for querying gene and variant annotation. Genome Biology 17, 91. doi:10.1186/s13059-016-0953-9

Service documentation: https://myvariant.info/

Examples

record <- list(dbnsfp = list(
  genename = list("TP53"),
  cadd = list(raw_rankscore = 0.17018),
  revel = list(rankscore = 0.4)
))
myvariant_parse_record(record)

Description

Annotate many variants in one request

Usage

myvariant_variants(ids, assembly = "hg38", fields = MYVARIANT_FIELDS, ...)

Arguments

Value

A biohttp envelope whose data is a tibble with one row per entry in ids, in the same order. See myvariant_parse_batch().

assembly is not optional

assembly = "hg38" is always sent. Without it MyVariant answers 200 with notfound for every GRCh38 variant, so a whole cohort disappears with no error anywhere. There is no way to omit it through this function, and that is deliberate.

References

Xin et al. (2016). High-performance web services for querying gene and variant annotation. Genome Biology 17, 91. doi:10.1186/s13059-016-0953-9

Service documentation: https://myvariant.info/

Examples

ids <- myvariant_id("17", 7676154, "G", "C")
biohttp::body_or_null(myvariant_variants(ids))

Description

The discovery direction. disease_id is an Open Targets id in either separator form; MONDO:0018975 and MONDO_0018975 both work.

Usage

opentargets_disease_targets(disease_id, size = 1000, ...)

Arguments

Value

A biohttp envelope whose data is the tibble described in opentargets_parse_targets().

References

Buniello et al. (2025). Open Targets Platform: facilitating therapeutic hypotheses building in drug discovery. Nucleic Acids Research 53(D1), D1467-D1475. doi:10.1093/nar/gkae1128

Service documentation: https://platform.opentargets.org/

Examples

biohttp::body_or_null(opentargets_disease_targets("MONDO_0018975"))

Description

Takes an Ensembl gene id, the same as opentargets_gene_diseases().

Usage

opentargets_drugs(ensembl_id, ...)

Arguments

Value

A biohttp envelope whose data is the tibble described in opentargets_parse_drugs().

References

Buniello et al. (2025). Open Targets Platform: facilitating therapeutic hypotheses building in drug discovery. Nucleic Acids Research 53(D1), D1467-D1475. doi:10.1093/nar/gkae1128

Service documentation: https://platform.opentargets.org/

Examples

biohttp::body_or_null(opentargets_drugs("ENSG00000157764"))

Description

Takes an Ensembl gene id, not a symbol, because that is what the Open Targets target query accepts. Resolve a symbol first with mygene_gene() and read its ensembl_gene column.

Usage

opentargets_gene_diseases(ensembl_id, size = 20, ...)

Arguments

Details

Keeping the resolution out of this function is deliberate. A client that silently made a second call to a different service would make one failure look like the other's, and a MyGene outage would read as an Open Targets outage.

Value

A biohttp envelope whose data is the tibble described in opentargets_parse_diseases().

References

Buniello et al. (2025). Open Targets Platform: facilitating therapeutic hypotheses building in drug discovery. Nucleic Acids Research 53(D1), D1467-D1475. doi:10.1093/nar/gkae1128

Service documentation: https://platform.opentargets.org/

Examples

biohttp::body_or_null(opentargets_gene_diseases("ENSG00000141510"))

Description

Open Targets takes an exact id for a direct lookup and free text for a search, and the two are different queries. This is what tells them apart.

Usage

opentargets_is_id(term)

Arguments

Value

A single logical.

References

Buniello et al. (2025). Open Targets Platform: facilitating therapeutic hypotheses building in drug discovery. Nucleic Acids Research 53(D1), D1467-D1475. doi:10.1093/nar/gkae1128

Service documentation: https://platform.opentargets.org/

Examples

opentargets_is_id("MONDO:0018975")
opentargets_is_id("neurofibromatosis type 1")

Description

Pure. Rows arrive already sorted by score, best first, and that order is kept.

Usage

opentargets_parse_diseases(body, ensembl_id = NA_character_)

Arguments

Value

A tibble of disease, disease_id, score, and source_url, or NULL when the target is absent or has no associations.

References

Buniello et al. (2025). Open Targets Platform: facilitating therapeutic hypotheses building in drug discovery. Nucleic Acids Research 53(D1), D1467-D1475. doi:10.1093/nar/gkae1128

Service documentation: https://platform.opentargets.org/

Examples

body <- list(data = list(target = list(
  approvedSymbol = "TP53",
  associatedDiseases = list(count = 1, rows = list(
    list(score = 0.88, disease = list(id = "MONDO_0018875", name = "LFS"))
  ))
)))
opentargets_parse_diseases(body, "ENSG00000141510")

Description

Pure.

Usage

opentargets_parse_drugs(body)

Arguments

Value

A tibble of drug, drug_id, drug_type, max_phase, diseases, disease_ids, and source_url. NULL when the target has no known drugs.

Every disease, not just the first

A drug row carries the whole list of diseases it has been tried against, and the first entry is often the least useful one. In the stored BRAF response, BELVARAFENIB lists five, and the first has no mapped disease at all. So the diseases arrive as list columns rather than as one picked value.

diseases prefers the mapped disease name and falls back to diseaseFromSource, which is the label the trial registry used. disease_ids is NA in the positions Open Targets could not map.

max_phase is the raw maxClinicalStage, for example "PHASE_2". Turning that into "Phase 2" is presentation and belongs to whatever is presenting it.

References

Buniello et al. (2025). Open Targets Platform: facilitating therapeutic hypotheses building in drug discovery. Nucleic Acids Research 53(D1), D1467-D1475. doi:10.1093/nar/gkae1128

Service documentation: https://platform.opentargets.org/

Examples

body <- list(data = list(target = list(
  drugAndClinicalCandidates = list(count = 1, rows = list(
    list(
      maxClinicalStage = "PHASE_2",
      drug = list(id = "CHEMBL1", name = "DRUGX", drugType = "Small molecule"),
      diseases = list(list(
        diseaseFromSource = "melanoma",
        disease = list(id = "MONDO_0005105", name = "melanoma")
      ))
    )
  ))
)))
opentargets_parse_drugs(body)

Description

Pure, and it reads both response shapes on purpose: the search query returns a hits array, the direct disease lookup returns a single record. A caller resolving a term does not know in advance which query ran, so one parser reads either and the difference stops mattering downstream.

Usage

opentargets_parse_matches(body)

Arguments

Value

A tibble of id, name, score, description, and source_url, best match first, or NULL when nothing matched. score is NA for a direct lookup, which has no relevance score to report.

References

Buniello et al. (2025). Open Targets Platform: facilitating therapeutic hypotheses building in drug discovery. Nucleic Acids Research 53(D1), D1467-D1475. doi:10.1093/nar/gkae1128

Service documentation: https://platform.opentargets.org/

Examples

hits <- list(data = list(search = list(hits = list(
  list(id = "MONDO_0018975", name = "neurofibromatosis type 1", score = 24.9)
))))
opentargets_parse_matches(hits)

single <- list(data = list(disease = list(id = "EFO_0000508", name = "neurofibroma")))
opentargets_parse_matches(single)

Description

Pure.

Usage

opentargets_parse_pgx(body)

Arguments

Details

drugs is a list column because one annotation can name several. rsid is NA where the annotation is keyed on a genotype rather than a variant, which is common.

Value

A tibble of rsid, genotype_id, drugs, phenotype, annotation, and evidence_level. NULL when the target has none, which is the normal case for most genes.

References

Buniello et al. (2025). Open Targets Platform: facilitating therapeutic hypotheses building in drug discovery. Nucleic Acids Research 53(D1), D1467-D1475. doi:10.1093/nar/gkae1128

Service documentation: https://platform.opentargets.org/

Examples

body <- list(data = list(target = list(pharmacogenomics = list(
  list(
    variantRsId = "rs4244285",
    drugs = list(list(drugFromSource = "venlafaxine")),
    phenotypeText = "decreased metabolism of venlafaxine",
    evidenceLevel = "3"
  )
))))
opentargets_parse_pgx(body)

Description

Pure. The discovery direction: the genes Open Targets associates with a disease, each with its overall association score.

Usage

opentargets_parse_targets(body, disease_id = NA_character_)

Arguments

Value

A tibble of symbol, ensembl_id, score, and source_url, or NULL when the disease is absent or has no associations.

References

Buniello et al. (2025). Open Targets Platform: facilitating therapeutic hypotheses building in drug discovery. Nucleic Acids Research 53(D1), D1467-D1475. doi:10.1093/nar/gkae1128

Service documentation: https://platform.opentargets.org/

Examples

body <- list(data = list(disease = list(
  name = "NF1",
  associatedTargets = list(count = 1, rows = list(
    list(score = 0.9, target = list(id = "ENSG00000196712", approvedSymbol = "NF1"))
  ))
)))
opentargets_parse_targets(body, "MONDO_0018975")

Description

Takes an Ensembl gene id, the same as opentargets_gene_diseases().

Usage

opentargets_pgx(ensembl_id, ...)

Arguments

Details

Most genes have none, so no_data here is the ordinary answer rather than a sign anything went wrong.

Value

A biohttp envelope whose data is the tibble described in opentargets_parse_pgx().

References

Buniello et al. (2025). Open Targets Platform: facilitating therapeutic hypotheses building in drug discovery. Nucleic Acids Research 53(D1), D1467-D1475. doi:10.1093/nar/gkae1128

Service documentation: https://platform.opentargets.org/

Examples

biohttp::body_or_null(opentargets_pgx("ENSG00000165841"))

Description

Free text is searched; something that already looks like an ontology id is looked up directly, which is both exact and cheaper. opentargets_is_id() is what decides.

Usage

opentargets_resolve_disease(term, limit = 5, ...)

Arguments

Details

This is the resolve_disease that exists twice in the family, once in genescout/R/tools/disease_resolver.R and once in a sibling app.

Value

A biohttp envelope whose data is the tibble described in opentargets_parse_matches(), best match first.

References

Buniello et al. (2025). Open Targets Platform: facilitating therapeutic hypotheses building in drug discovery. Nucleic Acids Research 53(D1), D1467-D1475. doi:10.1093/nar/gkae1128

Service documentation: https://platform.opentargets.org/

Examples

biohttp::body_or_null(opentargets_resolve_disease("neurofibromatosis type 1"))
biohttp::body_or_null(opentargets_resolve_disease("MONDO:0018975"))

Description

Walks the paginated index and stacks the pages. Stops at max_pages, which is a guard against an unbounded walk rather than a tuned number.

Usage

panelapp_all_panels(max_pages = 6, page_size = 100, ...)

Arguments

Value

A biohttp envelope whose data is the tibble from panelapp_parse_index(), stacked across pages. A page that fails after the first is where the walk stops, keeping what was already collected; a failure on the first page is returned as-is.

References

Martin et al. (2019). PanelApp crowdsources expert knowledge to establish consensus diagnostic gene panels. Nature Genetics 51(11), 1560-1565. doi:10.1038/s41588-019-0528-2

Service documentation: https://panelapp.genomicsengland.co.uk/

Examples

biohttp::body_or_null(panelapp_all_panels(max_pages = 2))

Description

The genes on one PanelApp panel

Usage

panelapp_panel(panel_id, ...)

Arguments

Value

A biohttp envelope whose data is the tibble described in panelapp_parse_panel().

References

Martin et al. (2019). PanelApp crowdsources expert knowledge to establish consensus diagnostic gene panels. Nature Genetics 51(11), 1560-1565. doi:10.1038/s41588-019-0528-2

Service documentation: https://panelapp.genomicsengland.co.uk/

Examples

biohttp::body_or_null(panelapp_panel(255))

Description

One page of the PanelApp panel index

Usage

panelapp_panels(page = 1, page_size = 100, ...)

Arguments

Value

A biohttp envelope whose data is a list of panels (the tibble from panelapp_parse_index()) and has_more, which is TRUE when PanelApp sent a next link.

References

Martin et al. (2019). PanelApp crowdsources expert knowledge to establish consensus diagnostic gene panels. Nature Genetics 51(11), 1560-1565. doi:10.1038/s41588-019-0528-2

Service documentation: https://panelapp.genomicsengland.co.uk/

Examples

biohttp::body_or_null(panelapp_panels())$panels

Description

Pure.

Usage

panelapp_parse_index(body)

Arguments

Value

A tibble of id, name, version, disorders (a list column, as a panel carries any number), and source_url, one row per panel. NULL when the page is empty.

References

Martin et al. (2019). PanelApp crowdsources expert knowledge to establish consensus diagnostic gene panels. Nature Genetics 51(11), 1560-1565. doi:10.1038/s41588-019-0528-2

Service documentation: https://panelapp.genomicsengland.co.uk/

Examples

body <- list(results = list(
  list(id = 255, name = "Neurofibromatosis Type 1",
       relevant_disorders = list("NF1"))
))
panelapp_parse_index(body)

Description

Pure.

Usage

panelapp_parse_panel(body)

Arguments

Details

Every gene on the panel is returned, red included. See the note in the file header on why the confidence level is not converted to a weight here.

Value

A tibble of symbol, confidence (the raw level PanelApp sent), level ("green", "amber", "red", or NA for anything else), hgnc_id, and source_url. NULL when the panel has no genes.

References

Martin et al. (2019). PanelApp crowdsources expert knowledge to establish consensus diagnostic gene panels. Nature Genetics 51(11), 1560-1565. doi:10.1038/s41588-019-0528-2

Service documentation: https://panelapp.genomicsengland.co.uk/

Examples

body <- list(id = 255, genes = list(
  list(
    entity_name = "NF1",
    gene_data = list(gene_symbol = "NF1", hgnc_id = "HGNC:7765"),
    confidence_level = "3"
  )
))
panelapp_parse_panel(body)

Description

Pure.

Usage

pdbe_parse_structures(body, accession = NA_character_)

Arguments

Value

A tibble of pdb_id, method, resolution, coverage, and source_url, one row per distinct structure. NULL when there are none.

One entry per structure, not per chain

The response is keyed by the accession, and each value is a list of per chain mappings. A single PDB entry therefore recurs once for every chain it resolved, so 3 structures across 12 chains arrive as 12 records. Counting them raw overstates structural coverage several-fold.

Rows are collapsed to distinct pdb_id, keeping the first occurrence, which is PDBe's own best (highest coverage) chain for that entry.

References

Armstrong et al. (2020). PDBe: improved findability of macromolecular structure data in the PDB. Nucleic Acids Research 48(D1), D335-D343. doi:10.1093/nar/gkz990

Service documentation: https://www.ebi.ac.uk/pdbe/

Examples

body <- list(P21359 = list(
  list(pdb_id = "7pgp", chain_id = "F", experimental_method = "Electron Microscopy",
       resolution = 3.1, coverage = 1),
  list(pdb_id = "7pgp", chain_id = "N", experimental_method = "Electron Microscopy",
       resolution = 3.1, coverage = 1)
))
pdbe_parse_structures(body, "P21359")

Description

Experimental structures for a UniProt accession

Usage

pdbe_structures(accession, ...)

Arguments

Value

A biohttp envelope whose data is the tibble described in pdbe_parse_structures().

References

Armstrong et al. (2020). PDBe: improved findability of macromolecular structure data in the PDB. Nucleic Acids Research 48(D1), D335-D343. doi:10.1093/nar/gkz990

Service documentation: https://www.ebi.ac.uk/pdbe/

Examples

biohttp::body_or_null(pdbe_structures("P21359"))

Description

Pure. Rows come back in symbols order, one per input.

Usage

pharos_parse_targets(body, symbols)

Arguments

Details

A TDL Pharos does not recognise is reported as NA rather than passed through, so a caller never has to guess whether an unexpected string is a new level or a typo.

Value

A tibble of symbol, tdl, and source_url, one row per entry in symbols. tdl is one of Tclin, Tchem, Tbio, Tdark, or NA.

References

Kelleher et al. (2023). Pharos 2023: an integrated resource for the understudied human proteome. Nucleic Acids Research 51(D1), D1405-D1416. doi:10.1093/nar/gkac1033

Service documentation: https://pharos.nih.gov/

Examples

body <- list(data = list(targets = list(targets = list(
  list(sym = "NF1", tdl = "Tbio")
))))
pharos_parse_targets(body, "NF1")

Description

A thin wrapper over pharos_targets().

Usage

pharos_target(symbol, ...)

Arguments

Value

A biohttp envelope whose data is a one-row tibble.

References

Kelleher et al. (2023). Pharos 2023: an integrated resource for the understudied human proteome. Nucleic Acids Research 51(D1), D1405-D1416. doi:10.1093/nar/gkac1033

Service documentation: https://pharos.nih.gov/

Examples

biohttp::body_or_null(pharos_target("NF1"))

Description

One request for the whole list, because Pharos's targets query already takes an array.

Usage

pharos_targets(symbols, ...)

Arguments

Value

A biohttp envelope whose data is a tibble with one row per entry in symbols, in the same order. See pharos_parse_targets().

References

Kelleher et al. (2023). Pharos 2023: an integrated resource for the understudied human proteome. Nucleic Acids Research 51(D1), D1405-D1416. doi:10.1093/nar/gkac1033

Service documentation: https://pharos.nih.gov/

Examples

biohttp::body_or_null(pharos_targets(c("NF1", "EGFR")))

Description

Functional context for a residue

Usage

protvar_function(accession, position, ...)

Arguments

Value

A biohttp envelope whose data is a single string. See protvar_parse_function().

References

Stephenson et al. (2024). ProtVar: mapping and contextualizing human missense variation. Nucleic Acids Research 52(W1), W140-W147. doi:10.1093/nar/gkae413

Service documentation: https://www.ebi.ac.uk/ProtVar/

Examples

biohttp::body_or_null(protvar_function("P04637", 175))

Description

Pure. Returns the first FUNCTION comment, with citations stripped.

Usage

protvar_parse_function(body)

Arguments

Value

A single string, or NA_character_ when there is no function comment.

References

Stephenson et al. (2024). ProtVar: mapping and contextualizing human missense variation. Nucleic Acids Research 52(W1), W140-W147. doi:10.1093/nar/gkae413

Service documentation: https://www.ebi.ac.uk/ProtVar/

Examples

body <- list(comments = list(list(
  type = "FUNCTION",
  text = list(list(value = "Induces arrest (PubMed:11025664)."))
)))
protvar_parse_function(body)

Description

Pure. Known variants at the residue.

Usage

protvar_parse_population(body)

Arguments

Value

A tibble of change and sources, where sources is a comma-separated list of distinct source names. NULL when there are no variants.

Sources are deduplicated

A variant carries one cross-reference per supporting record, so the same source name recurs many times: the TP53 175 fixture lists NCI-TCGA four times for a single change. Reporting them raw makes a single database look like corroboration from several.

References

Stephenson et al. (2024). ProtVar: mapping and contextualizing human missense variation. Nucleic Acids Research 52(W1), W140-W147. doi:10.1093/nar/gkae413

Service documentation: https://www.ebi.ac.uk/ProtVar/

Examples

body <- list(variants = list(list(
  alternativeSequence = "Cys",
  xrefs = list(list(name = "NCI-TCGA"), list(name = "NCI-TCGA"))
)))
protvar_parse_population(body)

Description

Known variants at a residue

Usage

protvar_population(accession, position, ...)

Arguments

Value

A biohttp envelope whose data is the tibble described in protvar_parse_population().

References

Stephenson et al. (2024). ProtVar: mapping and contextualizing human missense variation. Nucleic Acids Research 52(W1), W140-W147. doi:10.1093/nar/gkae413

Service documentation: https://www.ebi.ac.uk/ProtVar/

Examples

biohttp::body_or_null(protvar_population("P04637", 175))

Description

Pure. Accepts the several forms these arrive in: "R175H", "p.Arg175His", "Arg175His", or a bare "175".

Usage

protvar_position(variant)

Arguments

Value

An integer position, or NULL when there is no number in it.

References

Stephenson et al. (2024). ProtVar: mapping and contextualizing human missense variation. Nucleic Acids Research 52(W1), W140-W147. doi:10.1093/nar/gkae413

Service documentation: https://www.ebi.ac.uk/ProtVar/

Examples

protvar_position("p.Arg175His")
protvar_position("R175H")
protvar_position("no digits here")

Description

Pure.

Usage

protvar_strip_citations(text)

Arguments

Details

UniProt FUNCTION comments carry their evidence inline, as ⁠(PubMed:11025664, PubMed:12524540, ...)⁠. For a well-studied protein the citations run longer than the prose they support. This drops the citation groups and tidies the punctuation left behind, while keeping non-citation parentheticals such as ⁠(By similarity)⁠.

Value

The text with citation groups removed.

References

Stephenson et al. (2024). ProtVar: mapping and contextualizing human missense variation. Nucleic Acids Research 52(W1), W140-W147. doi:10.1093/nar/gkae413

Service documentation: https://www.ebi.ac.uk/ProtVar/

Examples

protvar_strip_citations("Induces arrest (PubMed:11025664, PubMed:12524540).")
protvar_strip_citations("Binds DNA (By similarity).")

Description

Prefers the Entrez id, which names the gene exactly, and falls back to the symbol. Exported because the token is what appears in source_id, so a caller reconstructing a citation needs the same rule.

Usage

pubtator_entity(symbol = NULL, entrez = NULL)

Arguments

Value

A single string such as "@GENE_7157", or NULL when neither argument is usable.

References

Wei et al. (2024). PubTator 3.0: an AI-powered literature resource for unlocking biomedical knowledge. Nucleic Acids Research 52(W1), W540-W546. doi:10.1093/nar/gkae235

Service documentation: https://www.ncbi.nlm.nih.gov/research/pubtator3/

Examples

pubtator_entity("TP53", 7157)
pubtator_entity("TP53")

Description

Articles PubTator3 has tagged with a gene

Usage

pubtator_gene_literature(symbol = NULL, entrez = NULL, ...)

Arguments

Value

A biohttp envelope whose data is a list of count, entity, results (the tibble from pubtator_parse_results(), which may be NULL), and source_url. A count of 0 is ok, not no_data.

References

Wei et al. (2024). PubTator 3.0: an AI-powered literature resource for unlocking biomedical knowledge. Nucleic Acids Research 52(W1), W540-W546. doi:10.1093/nar/gkae235

Service documentation: https://www.ncbi.nlm.nih.gov/research/pubtator3/

Examples

biohttp::body_or_null(pubtator_gene_literature("TP53", 7157))$count

Description

Pure. 0 is a real count and comes back as 0L; only an absent count is NA.

Usage

pubtator_parse_count(body)

Arguments

Value

A single integer, or NA_integer_.

References

Wei et al. (2024). PubTator 3.0: an AI-powered literature resource for unlocking biomedical knowledge. Nucleic Acids Research 52(W1), W540-W546. doi:10.1093/nar/gkae235

Service documentation: https://www.ncbi.nlm.nih.gov/research/pubtator3/

Examples

pubtator_parse_count(list(count = 4180))
pubtator_parse_count(list(count = 0))

Description

Pure.

Usage

pubtator_parse_results(body)

Arguments

Value

A tibble of pmid, title, journal, authors (a list column, as an article has any number), year, and source_url. NULL when there are no results.

References

Wei et al. (2024). PubTator 3.0: an AI-powered literature resource for unlocking biomedical knowledge. Nucleic Acids Research 52(W1), W540-W546. doi:10.1093/nar/gkae235

Service documentation: https://www.ncbi.nlm.nih.gov/research/pubtator3/

Examples

body <- list(results = list(list(
  pmid = 36197410,
  title = "TP53 or Not TP53",
  journal = "Clin Cancer Res"
)))
pubtator_parse_results(body)

Description

GO annotations for a UniProt accession

Usage

quickgo_annotations(
  accession,
  aspect = c("biological_process", "molecular_function", "cellular_component"),
  limit = 100,
  ...
)

Arguments

Value

A biohttp envelope whose data is the tibble described in quickgo_parse_annotations().

References

Binns et al. (2009). QuickGO: a web-based tool for Gene Ontology searching. Bioinformatics 25(22), 3045-3046. doi:10.1093/bioinformatics/btp536

Service documentation: https://www.ebi.ac.uk/QuickGO/

Examples

biohttp::body_or_null(quickgo_annotations("P21359"))

Description

Pure.

Usage

quickgo_parse_annotations(body)

Arguments

Value

A tibble of go_id, go_name, evidence, reference, aspect, and source_url, one row per distinct GO term. NULL when there are none.

References

Binns et al. (2009). QuickGO: a web-based tool for Gene Ontology searching. Bioinformatics 25(22), 3045-3046. doi:10.1093/bioinformatics/btp536

Service documentation: https://www.ebi.ac.uk/QuickGO/

Examples

body <- list(results = list(list(
  goId = "GO:0001937",
  goName = "negative regulation of endothelial cell proliferation",
  goEvidence = "IMP",
  reference = "PMID:16648142"
)))
quickgo_parse_annotations(body)

Description

Pure.

Usage

reactome_parse_pathways(body)

Arguments

Value

A tibble of pathway_id, name, in_disease, and source_url, one row per pathway. NULL when the gene has none.

References

Milacic et al. (2024). The Reactome Pathway Knowledgebase 2024. Nucleic Acids Research 52(D1), D672-D678. doi:10.1093/nar/gkad1025

Service documentation: https://reactome.org/

Examples

body <- list(list(
  stId = "R-HSA-5658442",
  displayName = "Regulation of RAS by GAPs",
  isInDisease = FALSE
))
reactome_parse_pathways(body)

Description

Reactome pathways for a gene symbol

Usage

reactome_pathways(symbol, species = 9606, ...)

Arguments

Value

A biohttp envelope whose data is the tibble described in reactome_parse_pathways().

References

Milacic et al. (2024). The Reactome Pathway Knowledgebase 2024. Nucleic Acids Research 52(D1), D672-D678. doi:10.1093/nar/gkad1025

Service documentation: https://reactome.org/

Examples

biohttp::body_or_null(reactome_pathways("NF1"))

Description

Maps each queried symbol to STRING's own preferredName. Needed to interpret network edges; see string_reconcile_edges().

Usage

string_map_ids(symbols, species = STRING_HUMAN, ...)

Arguments

Value

A biohttp envelope whose data is the tibble described in string_parse_ids().

References

Szklarczyk et al. (2023). The STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Research 51(D1), D638-D646. doi:10.1093/nar/gkac1000

Service documentation: https://string-db.org/

Examples

biohttp::body_or_null(string_map_ids(c("SEPTIN9", "TP53")))

Description

High-confidence edges among the genes you asked about, with the endpoints reconciled back to your symbols.

Usage

string_network(
  symbols,
  species = STRING_HUMAN,
  required_score = STRING_MIN_SCORE,
  reconcile = TRUE,
  ...
)

Arguments

Value

A biohttp envelope whose data is a list of edges (see string_parse_network()), queried, n_query, truncated, and n_dropped.

Knowing what was asked

The result carries queried, the exact symbol set STRING was asked about after the 500-identifier cap, plus truncated and n_dropped. That is what lets a caller tell a gene measured to have no partners from a gene that was never sent. Reporting the second as the first would invent a negative result.

References

Szklarczyk et al. (2023). The STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Research 51(D1), D638-D646. doi:10.1093/nar/gkac1000

Service documentation: https://string-db.org/

Examples

res <- string_network(c("TP53", "NF1", "EGFR"))
biohttp::body_or_null(res)$edges

Description

Pure. One row per queried identifier.

Usage

string_parse_ids(body)

Arguments

Value

A tibble of query, preferred, and string_id, with query and preferred upper-cased.

References

Szklarczyk et al. (2023). The STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Research 51(D1), D638-D646. doi:10.1093/nar/gkac1000

Service documentation: https://string-db.org/

Examples

body <- list(list(
  queryItem = "SEPTIN9", preferredName = "SEPT9",
  stringId = "9606.ENSP00000329125"
))
string_parse_ids(body)

Description

Pure. Edge endpoints come back in STRING's preferredName space; see string_reconcile_edges() for why that matters.

Usage

string_parse_network(body)

Arguments

Value

A tibble of gene_a, gene_b, and score, upper-cased. A zero-row tibble when there are no edges, because "no high-confidence edges" is a real answer about a set rather than an absence of one.

References

Szklarczyk et al. (2023). The STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Research 51(D1), D638-D646. doi:10.1093/nar/gkac1000

Service documentation: https://string-db.org/

Examples

body <- list(list(
  preferredName_A = "TP53", preferredName_B = "NF1", score = 0.88
))
string_parse_network(body)

Description

Pure. Sorted by combined score, strongest first.

Usage

string_parse_partners(body)

Arguments

Value

A tibble of partner, score, and the four evidence channels experimental, database, coexpression, and textmining. NULL when there are no partners.

References

Szklarczyk et al. (2023). The STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Research 51(D1), D638-D646. doi:10.1093/nar/gkac1000

Service documentation: https://string-db.org/

Examples

body <- list(list(
  preferredName_B = "SFN", score = 0.999,
  escore = 0.981, dscore = 0.75, ascore = 0, tscore = 0.859
))
string_parse_partners(body)

Description

Interaction partners for one gene

Usage

string_partners(symbol, species = STRING_HUMAN, limit = 25, ...)

Arguments

Value

A biohttp envelope whose data is the tibble described in string_parse_partners().

On STRING's content type

STRING serves JSON as text/json rather than application/json. A client that trusts the content-type header rejects a perfectly good body. biohttp parses with check_type = FALSE, so this works, but it is the reason not to "tidy" that up.

References

Szklarczyk et al. (2023). The STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Research 51(D1), D638-D646. doi:10.1093/nar/gkac1000

Service documentation: https://string-db.org/

Examples

biohttp::body_or_null(string_partners("TP53"))

Description

Pure, and it exists because of a real and quiet failure.

Usage

string_reconcile_edges(edges, id_map)

Arguments

Details

STRING's canonical preferredName lags HGNC. Query SEPTIN9 and the edges come back naming SEPT9, and the network endpoint does not echo the query term. A caller matching edges against the symbols it asked about therefore finds nothing for that gene and records it as having no partners, which is indistinguishable from a real isolate.

Passing the map from string_map_ids() through here translates the endpoints back. An endpoint with no mapping is left alone, and an empty map leaves the edges untouched, so reconciliation can never turn a good answer into a worse one.

Value

The edge tibble, with endpoints translated where a mapping existed.

References

Szklarczyk et al. (2023). The STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Research 51(D1), D638-D646. doi:10.1093/nar/gkac1000

Service documentation: https://string-db.org/

Examples

edges <- tibble::tibble(gene_a = "SEPT9", gene_b = "TP53", score = 0.9)
map <- tibble::tibble(
  query = "SEPTIN9", preferred = "SEPT9", string_id = "9606.ENSP00000329125"
)
string_reconcile_edges(edges, map)

Description

Diseases UniProt curates for an accession

Usage

uniprot_diseases(accession, ...)

Arguments

Value

A biohttp envelope whose data is the tibble described in uniprot_parse_diseases().

References

The UniProt Consortium (2025). UniProt: the Universal Protein Knowledgebase in 2025. Nucleic Acids Research 53(D1), D609-D617. doi:10.1093/nar/gkae1010

Service documentation: https://www.uniprot.org/

Examples

biohttp::body_or_null(uniprot_diseases("P04637"))

Description