Taxonomic nomenclature normalization and validation

taxonomic-nomenclature-normalization-and-validation

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Summary

Standardizes organism names from heterogeneous source databases into controlled taxonomic vocabularies and validates ranks, resolving nomenclatural conflicts and ensuring consistent organism identity across a structure-organism pair collection. This skill is essential when ingesting natural product data from multiple curated databases with divergent taxonomic authority standards.

When to use

Apply this skill when you have organism names originating from 31+ heterogeneous natural product databases that use different taxonomic authorities, nomenclature versions, or rank assignments, and you need to harmonize them into a single organism subgraph with validated ranks before linking to chemical structures and references.

When NOT to use

• Input organism names are already validated against a single authoritative taxonomy and do not require reconciliation across heterogeneous sources.
• The analysis goal is purely chemical (e.g., structure similarity, molecular property prediction) and organism identity is not required for downstream computations.
• Taxonomic rank information is not available or not critical for the research question.

Inputs

• heterogeneous organism names from external/dbSource/* files (TSV, JSON, XML formats)
• local organism identifiers with associated raw taxonomic metadata
• interim/db/*.tsv files containing organism taxonomy data partitioned by source

Outputs

• standardized organism subgraph with mapped controlled vocabulary identifiers (NCBI Taxonomy, taxonomic ranks)
• interim/dictionaries/common/* lookup tables for organism name-to-rank mappings
• interim/dictionaries/tcm/* ontology-enriched organism identifiers for Traditional Chinese Medicine and alternative medicine
• validated organism records with enforced rank consistency and null-check validation (≤5% nulls in organism_name, taxonomic_rank)

How to apply

First, run the translating_organism filter (2_translating_organism/../main.kt) to map local organism identifiers from each source database to controlled taxonomic vocabularies (e.g., NCBI Taxonomy, taxonomic ranks). Then apply cleaningTaxonomy (4_cleaningTaxonomy.R) to validate and standardize rank assignments, resolve synonyms, and flag nomenclatural conflicts. Finally, run addingOTL (5_addingOTL.R) to enrich the standardized taxonomy with additional ontology mappings. Validation passes when no critical organism_name fields are null in >5% of rows and rank consistency is enforced across the organism subgraph.

Related tools

• 2_translating_organism (Maps local organism identifiers from source databases to controlled taxonomic vocabularies (NCBI Taxonomy, taxonomic ranks)) — https://github.com/lotusnprod/lotus-processor
• 4_cleaningTaxonomy.R (Validates and standardizes taxonomic rank assignments, resolves nomenclatural synonyms and conflicts) — https://github.com/lotusnprod/lotus-processor
• 5_addingOTL.R (Enriches standardized taxonomy with additional ontology mappings for Traditional Chinese Medicine and alternative medicine) — https://github.com/lotusnprod/lotus-processor
• Kotlin (Implementation language for 2_translating_organism organism identifier mapping script)
• R (Implementation language for cleaningTaxonomy and addingOTL validation and enrichment scripts)

Examples

Rscript 4_cleaningTaxonomy.R < interim/db/organism_taxonomy.tsv > interim/dictionaries/common/organism_rank_validated.tsv

Evaluation signals

• No organism_name field is null in >5% of rows in the standardized organism subgraph output.
• All organism records successfully map to at least one controlled vocabulary identifier (NCBI Taxonomy rank or equivalent).
• Organism rank consistency is enforced: child-parent taxonomic relationships in the organism subgraph conform to expected hierarchy (e.g., species nests under genus, genus under family).
• Row count in standardized organism output matches the input source database row count (accounting for deduplicated synonyms documented in mapping tables).
• Lookup tables in interim/dictionaries/common/* and interim/dictionaries/tcm/* contain no circular references and all mappings are bidirectionally resolvable.

Limitations

• Nomenclatural conflicts arising from outdated or colloquial organism names in legacy databases may not be fully resolvable against a single authority; manual curation may be required for >5% of records.
• The skill assumes controlled taxonomic vocabularies (e.g., NCBI Taxonomy) are available and up-to-date; gaps or lags in authority maintenance can propagate into the standardized output.
• Traditional Chinese Medicine and alternative medicine ontologies (interim/dictionaries/tcm/*) may have limited coverage for rare or underrepresented organisms, resulting in partial enrichment.
• No changelog is documented for version updates to the translation and cleaning scripts, making it difficult to track nomenclatural standard changes across processor releases.

Evidence

• [methods] standardizing.R translation script to build Traditional Chinese Medicine and alternative medicine ontology mappings: "Run tcm.R translation script to build Traditional Chinese Medicine and alternative medicine ontology mappings into interim/dictionaries/tcm/*."
• [methods] organism rank validation thresholds: "verify no critical fields (structure_id, organism_name, reference_doi) are null in >5% of rows."
• [methods] organism subgraph as a curating stage component: "2_curating: 1_integrating.R, organism subgraph, structure subgraph, reference subgraph"
• [methods] translating_organism as a filter step in the pipeline: "translating_organism [section=methods; evidence='2_translating_organism/../main.kt']"
• [methods] cleaningTaxonomy filter step for rank standardization: "cleaningTaxonomy [section=methods; evidence='4_cleaningTaxonomy.R']"
• [methods] origin from 31 initial open databases requiring harmonization: "Data originates from 31 initial open databases"
• [methods] controlled vocabulary mapping in common.R script: "Run common.R translation script on interim/db outputs to map local identifiers to controlled vocabularies (InChI, SMILES, taxonomic ranks), writing interim/dictionaries/common/* lookup tables."
• [readme] README description of structure-organism pair standardization objective: "LOTUS is a comprehensive collection of documented structure-organism pairs. Within the frame of current computational approaches in Natural Products research and related fields, these documented"