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Legality check

Checks a DNA sequence, record, or data set for compliance with rules such as alphabet, length, gap, quality, and metadata, and returns a structured list of questions.

Whenever an error is found, the is_valid of the returned result is False; all issues are still stored in the structured issues.

1) Validate sequences and datasets

  • Purpose: Check alphabet, illegal symbols, empty sequences, length, gap, ambiguous base ratio, record metadata, PHRED quality, empty set and duplicate ID in one go, and return an itemized issue report to filter out or correct invalid data before analysis.

  • Method: Evaluate each configured rule and collect issues; errors set is_valid=False while warnings alone do not.

  • API: dnakit.validate(value[required], config[optional]); single input returns ValidationReport, collective input returns DatasetValidationReport. Configuration type is dnakit.ValidationConfig or dnakit.DatasetValidationConfig.

  • Input: Ordinary users can directly input DNA. When there is only one record, use ValidationConfig and return a single report; when it contains multiple records, you can use DatasetValidationConfig, or you can directly pass ValidationConfig as the rule for each record. Old core objects are still compatible.
  • Output: A single input returns ValidationReport; a collection returns DatasetValidationReport. Both expose validity and issues.
  • Example code:
from dnakit import DNA, ValidationConfig, validate

single = validate(
    DNA("ACGN", alphabet="iupac"),
    config=ValidationConfig(alphabet="strict"),
)
print(single.is_valid)
print([issue.code for issue in single.issues])

records = DNA(
    [
        {"sequence": "ACGT", "id": "valid"},
        {"sequence": "AC", "id": "short"},
    ]
)
collection = validate(records, config=ValidationConfig(min_length=3))
print(collection.is_valid)
print([issue.code for issue in collection.issues])
  • Example output:
False
['STD_INVALID_SYMBOL']
False
['STD_INVALID_RECORDS']

FAQ: details