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Variant Effect Scores and Experimental Data

See the FAQ for dependencies and return conventions.

1) Retrieve AlphaMissense classifications across a protein

  • Purpose: Aggregate substitution classes, residue mean scores and thresholds across a UniProt protein; max_residues limits the queried positions.
  • Method: Use AlphaMissense to query or submit the supplied input and parse the response.
  • API： dnakit.databases.alpha_missense_get_protein_scores(*, uniprot_id, max_residues=None, verbose=True)
  • Input: Provide uniprot_id.
  • Output: Returns a mapping with uniprot_id, protein_length, scores, n_positions_returned and related fields.
  • Example code:
import dnakit

result = dnakit.databases.alpha_missense_get_protein_scores(
    uniprot_id='P01116',
    max_residues=20,
    verbose=False,
)
print(result)
  • Example output: The response has the following shape; records depend on the input and source.

Response-shape excerpt; <…> denotes type placeholders, not a captured query result.

{
  "uniprot_id": "<string>",
  "protein_length": "<integer>",
  "scores": [
    "<object>"
  ],
  "n_positions_returned": "<integer>",
  "n_positions_attempted": "<integer>",
  "max_residues_cap": "<integer>",
  "thresholds": "<object>",
  "pdb_download": "<string>"
}

Full parameters and usage notes

2) Retrieve substitution classes and mean scores at a residue

  • Purpose: Retrieve benign, ambiguous and pathogenic substitution classes plus residue mean scores; these are not numeric scores for each individual substitution.
  • Method: Use AlphaMissense to query or submit the supplied input and parse the response.
  • API： dnakit.databases.alpha_missense_get_residue_scores(*, uniprot_id, position, verbose=True)
  • Input: Provide uniprot_id, position.
  • Output: Returns a mapping with status, data, error and related fields.
  • Example code:
import dnakit

result = dnakit.databases.alpha_missense_get_residue_scores(
    uniprot_id='P15056',
    position=600,
    verbose=False,
)
print(result)
  • Example output: The response has the following shape; records depend on the input and source.

Response-shape excerpt; <…> denotes type placeholders, not a captured query result.

{
  "status": "<string>",
  "data": {
    "uniprot_id": "<string>",
    "position": "<integer>",
    "scores": "<object>",
    "thresholds": "<object>"
  },
  "error": "<string>"
}

Full parameters and usage notes

3) Classify a missense variant with AlphaMissense

  • Purpose: Retrieve an AlphaMissense classification for one missense variant. The upstream operation supplies no numeric substitution score: pathogenicity_score is null and score_available is false.
  • Method: Use AlphaMissense to query or submit the supplied input and parse the response.
  • API： dnakit.databases.alpha_missense_get_variant_score(*, uniprot_id, variant, verbose=True)
  • Input: Provide uniprot_id, variant.
  • Output: Returns a mapping with status, data, error and related fields.
  • Example code:
import dnakit

result = dnakit.databases.alpha_missense_get_variant_score(
    uniprot_id='P15056',
    variant='V600E',
    verbose=False,
)
print(result)
  • Example output: The response has the following shape; records depend on the input and source.

Response-shape excerpt; <…> denotes type placeholders, not a captured query result.

{
  "status": "<string>",
  "data": {
    "uniprot_id": "<string>",
    "variant": "<string>",
    "position": "<integer>",
    "reference_aa": "<string>",
    "variant_aa": "<string>",
    "pathogenicity_score": "<number>",
    "score_available": "<boolean>",
    "classification": "<string>"
  },
  "error": "<string>"
}

Full parameters and usage notes

4) Retrieve CADD scores for substitutions at a position

  • Purpose: Retrieve CADD scores for substitutions at a position.
  • Method: Use CADD to query or submit the supplied input and parse the response.
  • API： dnakit.databases.cadd_get_position_scores(*, chrom, pos, version=None, verbose=True)
  • Input: Provide chrom, pos.
  • Output: Returns a mapping with chrom, pos, variants, version and related fields.
  • Example code:
import dnakit

result = dnakit.databases.cadd_get_position_scores(
    chrom='7',
    pos=140753336,
    verbose=False,
)
print(result)
  • Example output: The response has the following shape; records depend on the input and source.

Response-shape excerpt; <…> denotes type placeholders, not a captured query result.

{
  "chrom": "<string>",
  "pos": "<integer>",
  "variants": [
    "<value>"
  ],
  "version": "<string>"
}

Full parameters and usage notes

5) Retrieve CADD scores across a genomic interval

  • Purpose: Retrieve CADD scores across a genomic interval.
  • Method: Use CADD to query or submit the supplied input and parse the response.
  • API： dnakit.databases.cadd_get_range_scores(*, chrom, start, end, version=None, verbose=True)
  • Input: Provide chrom, start, end.
  • Output: Returns a mapping with chrom, start, end, variants and related fields.
  • Example code:
import dnakit

result = dnakit.databases.cadd_get_range_scores(
    chrom='7',
    start=140753330,
    end=140753340,
    verbose=False,
)
print(result)
  • Example output: The response has the following shape; records depend on the input and source.

Response-shape excerpt; <…> denotes type placeholders, not a captured query result.

{
  "chrom": "<string>",
  "start": "<integer>",
  "end": "<integer>",
  "variants": [
    "<value>"
  ],
  "count": "<integer>",
  "version": "<string>"
}

Full parameters and usage notes

6) Retrieve indexed CADD annotations through MyVariant

  • Purpose: Retrieve indexed CADD annotations through MyVariant.
  • Method: Use API to query or submit the supplied input and parse the response.
  • API： dnakit.databases.cadd_get_scores(*, variant, assembly='hg38', verbose=True)
  • Input: Provide variant.
  • Output: Returns MyVariant query results; mapping results include dnakit_score_source identifying indexed CADD annotations. Missing scores remain missing; no new model prediction is performed.
  • Example code:
import dnakit

result = dnakit.databases.cadd_get_scores(
    variant='chr7:g.140753336A>T',
    assembly='hg38',
    verbose=False,
)
print(result)
  • Example output: Returns MyVariant query results; mapping results include dnakit_score_source identifying indexed CADD annotations. Missing scores remain missing; no new model prediction is performed.

Full parameters and usage notes

7) Retrieve a variant's CADD deleteriousness score

  • Purpose: Retrieve a variant's CADD deleteriousness score.
  • Method: Use CADD to query or submit the supplied input and parse the response.
  • API： dnakit.databases.cadd_get_variant_score(*, chrom, pos, ref, alt, version=None, include_annotations=None, verbose=True)
  • Input: Provide chrom, pos, ref, alt.
  • Output: Returns a mapping with chrom, pos, ref, alt and related fields.
  • Example code:
import dnakit

result = dnakit.databases.cadd_get_variant_score(
    chrom='7',
    pos=140753336,
    ref='A',
    alt='T',
    verbose=False,
)
print(result)
  • Example output: The response has the following shape; records depend on the input and source.

Response-shape excerpt; <…> denotes type placeholders, not a captured query result.

{
  "chrom": "<string>",
  "pos": "<integer>",
  "ref": "<string>",
  "alt": "<string>",
  "phred_score": "<number>",
  "raw_score": "<number>",
  "interpretation": "<string>",
  "version": "<string>"
}

Full parameters and usage notes

8) Check EVE score availability for a gene

  • Purpose: Check EVE score availability for a gene.
  • Method: Use EVE to query or submit the supplied input and parse the response.
  • API： dnakit.databases.eve_get_gene_info(*, gene_symbol, verbose=True)
  • Input: Provide gene_symbol.
  • Output: Returns a mapping with status, data, error and related fields.
  • Example code:
import dnakit

result = dnakit.databases.eve_get_gene_info(
    gene_symbol='TP53',
    verbose=False,
)
print(result)
  • Example output: The response has the following shape; records depend on the input and source.

Response-shape excerpt; <…> denotes type placeholders, not a captured query result.

{
  "status": "<string>",
  "data": {
    "gene_symbol": "<string>",
    "ensembl_id": "<string>",
    "eve_website": "<string>"
  },
  "error": "<string>"
}

Full parameters and usage notes

9) Retrieve an EVE variant pathogenicity score

  • Purpose: Retrieve an EVE variant pathogenicity score.
  • Method: Use EVE to query or submit the supplied input and parse the response.
  • API： dnakit.databases.eve_get_variant_score(*, variant=None, chrom=None, pos=None, ref=None, alt=None, species=None, verbose=True)
  • Input: Choose filters as shown in the example; omitted options use the operation defaults.
  • Output: Returns a mapping with status, data, error and related fields.
  • Example code:
import dnakit

result = dnakit.databases.eve_get_variant_score(
    chrom='17',
    pos=7674220,
    ref='G',
    alt='A',
    verbose=False,
)
print(result)
  • Example output: The response has the following shape; records depend on the input and source.

Response-shape excerpt; <…> denotes type placeholders, not a captured query result.

{
  "status": "<string>",
  "data": {
    "variant": "<string>",
    "eve_scores": [
      "<value>"
    ],
    "threshold": "<object>"
  },
  "error": "<string>"
}

Full parameters and usage notes

10) Build an amino acid effect matrix from MaveDB scores

  • Purpose: Build an amino acid effect matrix from MaveDB scores.
  • Method: Use MaveDB to query or submit the supplied input and parse the response.
  • API： dnakit.databases.mave_db_get_effect_matrix(*, urn, uniprot_id=None, score_field=None, verbose=True)
  • Input: Provide urn.
  • Output: Returns a mapping with urn, matrix, positions, amino_acid_order and related fields.
  • Example code:
import dnakit

result = dnakit.databases.mave_db_get_effect_matrix(
    urn='urn:mavedb:00000115-a-7',
    uniprot_id='P01116',
    verbose=False,
)
print(result)
  • Example output: The response has the following shape; records depend on the input and source.

Response-shape excerpt; <…> denotes type placeholders, not a captured query result.

{
  "urn": "<string>",
  "matrix": [
    [
      "<number>"
    ]
  ],
  "positions": [
    "<integer>"
  ],
  "amino_acid_order": "<string>",
  "shape": [
    "<integer>"
  ],
  "n_parsed_single_missense": "<integer>",
  "n_dropped": "<integer>",
  "n_total_rows": "<integer>"
}

Full parameters and usage notes

11) Retrieve multiple variant pathogenicity prediction scores

  • Purpose: Retrieve multiple variant pathogenicity prediction scores.
  • Method: Use MyVariant to query or submit the supplied input and parse the response.
  • API： dnakit.databases.my_variant_get_pathogenicity_scores(*, variant_id, fields=None, assembly=None, verbose=True)
  • Input: Provide variant_id.
  • Output: Returns existing dbNSFP variant scores such as REVEL, CADD and SIFT where available; missing scores are not calculated.
  • Example code:
import dnakit

result = dnakit.databases.my_variant_get_pathogenicity_scores(
    variant_id='rs45478192',
    verbose=False,
)
print(result)
  • Example output: Returns existing dbNSFP variant scores such as REVEL, CADD and SIFT where available; missing scores are not calculated.

Full parameters and usage notes