Technical interview guide

Biological Databases & Ontologies

The major public repositories a computational biologist works against daily — NCBI/GenBank, Ensembl, UniProt, and Gene Ontology — what each holds and how they cross-reference each other.

Read: 49 min
Practice MCQs: 25
Interview QA: 25
Edition: v3
Editorial status: Reviewed
Relevant for: Computational Biologist

Scope: Current NCBI E-utilities and data policies; Ensembl release 116; UniProt, RCSB PDB, Gene Ontology, OBO Foundry, Sequence Ontology, HGNC, Identifiers.org and OLS guidance reviewed 2026-09-04.

Interview QA

Treat each question like a live interview question: answer out loud first (structure, assumptions, tradeoffs), then open the model answer to spot gaps and rehearse a tighter follow-up.

Curated: 2026-10-05 · Written: 2026-10-05 · Reviewed: 2026-10-04

QA-1

Design and defend a knowledge-data workflow for archive and knowledgebase.

QA-2

Design and defend a knowledge-data workflow for namespaced accessions.

QA-3

Design and defend a knowledge-data workflow for record versions.

QA-4

Design and defend a knowledge-data workflow for identifier mapping.

QA-5

Design and defend a knowledge-data workflow for gene symbols.

QA-6

How do you distinguish between and apply experimental vs. computational evidence codes when modeling gene and protein functional annotations?

QA-7

How should CURIEs, URIs, and persistent identifiers be structured to prevent namespace collisions and maintain stable entity resolution across biological databases?

QA-8

How do asserted and inferred class hierarchies differ in OWL DL, and how do reasoners like ELK and HermiT validate subsumption and prevent cycles in biomedical ontologies?

QA-9

When modeling complex biological relationships like drug-target or phenotype-disease associations, how do you decide between reification, named graphs, and W3C PROV-O for statement-level provenance?

QA-10

How do you handle ontology versioning, class obsolescence, and term merges using replaced_by and consider tags without breaking downstream annotation pipelines?

QA-11

Design and defend a knowledge-data workflow for relation semantics.

QA-12

Design and defend a knowledge-data workflow for ontology DAGs.

QA-13

Design and defend a knowledge-data workflow for true-path propagation.

QA-14

Design and defend a knowledge-data workflow for obsolete terms.

QA-15

Design and defend a knowledge-data workflow for synonym scope.

QA-16

Design and defend a knowledge-data workflow for cross-ontology mappings.

QA-17

Design and defend a knowledge-data workflow for annotation qualifiers.

QA-18

Design and defend a knowledge-data workflow for annotation propagation.

QA-19

Design and defend a knowledge-data workflow for semantic similarity.

QA-20

Design and defend a knowledge-data workflow for API pagination.

QA-21

Design and defend a knowledge-data workflow for rate limits.

QA-22

Design and defend a knowledge-data workflow for bulk snapshots.

QA-23

Design and defend a knowledge-data workflow for data licensing.

QA-24

Design and defend a knowledge-data workflow for update validation.

QA-25