misannotation

Misannotation refers to the assignment of an incorrect label, tag, or annotation to data, documents, images, or biological sequences, leading to misleading or inaccurate information. It is a persistent issue across many domains, including genomics, proteomics, textual corpora, and image datasets, where erroneous labels can affect data interpretation, analyses, and downstream research.

In biological databases, misannotation often arises when computational predictions or homology-based inferences are accepted without sufficient experimental validation, or when outdated or erroneous reference data are propagated through curation pipelines. In microbiology and taxonomy, misannotation can involve incorrect species or functional assignments for sequences. In natural language processing and computer vision, human error, ambiguous labeling, or systematic biases in automated tagging can produce mislabeled training sets that degrade model performance.

Consequences of misannotation include biased results, reduced reproducibility, faulty functional inferences, and degraded model quality. Because many databases interlink information through provenance and versioned records, misannotations can propagate across resources and persist until detected and corrected.

Detection and mitigation rely on multiple strategies: cross-validation with independent evidence, manual or expert curation, reannotation using updated reference standards, and provenance tracking to document data lineage. Automated quality checks, consistency audits, and uncertainty estimates help flag potential errors. Ongoing community curation and transparent versioning of annotations are central to reducing the impact of misannotation and maintaining data reliability.