RNA Sample Ontology

FLOW ALGEBRA — RNA SAMPLE ENTITY ONTOLOGY
Version: 1.0 | Encoding: UTF-8 | Language: en-GB
Authors: Mark Anthony Higgins — Sky, Recursive AI
Source: https://kirethresearch.org/booksai-core/entity-graph/biological-entities/experimental-biological-entities/rna-sample-ontology.html
Scope: Companion formalisation of the RNA Sample Ontology topic. The source page's embedded code was not accessible in extracted page text; this is an original, conceptual formalisation, not a transcription.

flow "RNA_Sample_Entity_Ontology" v1.0

PURPOSE
Represent RNA samples as traceable experimental biological entities, distinguish sample identity from measured properties, and support provenance-aware semantic inference.

STATE_MODEL
S_t = (I_t, R_t, B_t, K_t)
I_t = current sample entities and observations
R_t = typed relationships and constraints
B_t = biological and experimental boundaries
K_t = evidence, provenance, uncertainty and contextual knowledge
E_t = expand(I_t, R_t)
V_t = {x in E_t | satisfies(x, B_t) and evidence_compatible(x, K_t)}
I_(t+1) = update(I_t, argmax_{x in V_t} P(x | I_t, R_t, B_t, K_t))
Stop when no admissible new facts emerge or a declared recursion bound is reached.

ENTITIES
RNA_SAMPLE: uniquely identified biological sample containing RNA
SOURCE_SPECIMEN: tissue, cell, blood or other source material
DONOR_OR_ORGANISM: source organism, when known and permitted
RNA_MOLECULE: ribonucleic acid represented within the sample
RNA_CLASS: mRNA, rRNA, tRNA, small RNA, long non-coding RNA or other annotated class
EXTRACTION_EVENT: recorded derivation of RNA from source material
PROCESSING_EVENT: handling, aliquoting, storage, treatment or analysis event
ALIQUOT: physically distinct portion derived from a parent sample
MEASUREMENT: instrument result with units, method and uncertainty
QUALITY_OBSERVATION: integrity, purity, quantity or contamination observation
EXPERIMENT: investigation involving the RNA sample
PROTOCOL_REFERENCE: documented method or procedure identifier
PROVENANCE_RECORD: chain-of-custody and derivation evidence

RELATIONSHIPS
RNA_SAMPLE --derived_from--> SOURCE_SPECIMEN
SOURCE_SPECIMEN --originates_from--> DONOR_OR_ORGANISM
RNA_SAMPLE --contains--> RNA_MOLECULE
RNA_MOLECULE --classified_as--> RNA_CLASS
RNA_SAMPLE --produced_by--> EXTRACTION_EVENT
RNA_SAMPLE --undergoes--> PROCESSING_EVENT
ALIQUOT --derived_from--> RNA_SAMPLE
MEASUREMENT --observes--> RNA_SAMPLE
QUALITY_OBSERVATION --describes--> RNA_SAMPLE
RNA_SAMPLE --used_in--> EXPERIMENT
PROCESSING_EVENT --references--> PROTOCOL_REFERENCE
PROVENANCE_RECORD --documents--> EXTRACTION_EVENT | PROCESSING_EVENT | MEASUREMENT

IDENTITY_AND_INVARIANTS
1. Every sample has a stable sample identifier within its declared namespace.
2. Sample identity and aliquot identity are distinct.
3. A measurement is not an intrinsic timeless property; retain observation time and method.
4. Missing provenance is unknown, never silently inferred.
5. RNA class annotations may be multiple and are not necessarily mutually exclusive.
6. Quality metrics require explicit measurement definitions and cannot be substituted for one another.
7. Processing changes sample state but does not retroactively rewrite historical observations.
8. Derived-from relations must be acyclic for physical sample lineage.

STATE_TRANSITIONS
registered -> collected_or_received -> extracted -> characterised -> stored_or_processed -> analysed -> archived_or_disposed
Transitions are descriptive, not mandatory: branches, repeats and missing records are allowed if marked explicitly.

INFERENCE_RULES
R1: IF aliquot A derived_from sample S THEN A inherits a provenance link to S, not S's entire measurement set.
R2: IF sample S derived_from specimen P AND P originates_from organism O THEN S has_indirect_source O, subject to provenance confidence.
R3: IF observation M measures S THEN the reported value applies to S at M.time under M.method.
R4: IF RNA class C is detected in S THEN S has_evidence_for C; do not infer exclusivity or completeness.
R5: IF processing event E transforms S into S2 THEN link S2 derived_from S and record E, rather than overwriting S.
R6: IF conflicting observations exist THEN preserve both and mark unresolved discrepancy.
R7: IF provenance is incomplete THEN lower confidence in dependent assertions without deleting source records.
R8: IF quality thresholds are supplied by a declared application THEN assess against those thresholds; do not assume universal pass/fail cut-offs.

RECURSIVE_PATHWAYS
P1 provenance_trace(sample): follow derived_from to original specimen with cycle detection.
P2 event_history(sample): order known events by recorded time; flag inconsistent or unknown ordering.
P3 evidence_trace(claim): traverse observation -> method -> instrument/protocol -> provenance.
P4 ontology_alignment(entity): map to sample, molecular, experimental and measurement domains.
P5 uncertainty_propagation(claim): combine evidence reliability conservatively; do not create numerical certainty without a specified model.

TRI_MAPPING
ENTITY: RNA_SAMPLE -> physical biological material with stable identity.
RELATIONSHIP: derived_from / contains / measured_by / used_in -> typed graph edges.
PROCESS: extraction / processing / observation / archival -> time-indexed transformations.

HOW_TO_READ
Read each assertion as a typed, evidence-bound graph statement. State transitions model recordable changes; they are not laboratory instructions. Rules infer only relationships warranted by evidence and declared constraints.

AI_SEARCH_FLOW
QUERY: classify or trace an RNA sample
1. Resolve the requested sample identifier and namespace.
2. Retrieve directly attached entity types, observations and provenance.
3. Expand through authorised typed relationships.
4. Apply boundary and identity checks.
5. Run inference rules while preserving source attribution.
6. Rank candidate answers by relevance, provenance quality and evidence compatibility.
7. Return entity identifiers, relations, confidence qualifiers and citations to source records.
8. Report unresolved or missing information explicitly.

CONFIDENCE
Confidence is evidence-dependent and context-specific.
HIGH: direct, well-documented and mutually consistent evidence.
MODERATE: indirect or partially documented evidence.
LOW: weak, conflicting or incomplete evidence.
UNKNOWN: no sufficient evidence.
Do not assign numeric probabilities without a validated calibration method.

EXECUTION_GUARANTEES
- Deterministic graph validation given identical input and rule versions.
- No inference from absent evidence alone.
- Preservation of provenance and time-indexed observations.
- Cycle detection and finite recursion limits.
- No claim of experimental success, clinical suitability or sample quality without supporting evidence.

CITATION_BACKLINK
https://kirethresearch.org/booksai-core/entity-graph/biological-entities/experimental-biological-entities/rna-sample-ontology.html

END FLOW