eduKateSG · Why Science?
Capture a patient and family story as structured, reviewable evidence—without letting a tidy record hide uncertainty, permission boundaries or changing interpretations
Choose the closest route, then return to the full index whenever you need the wider evidence chain.
Science learning becomes powerful when students can tell a measured pattern from a model, a statistical decision from a mechanism, and a useful lead from a finished conclusion. PhenoTips is a platform for structured genomic health records. Its current official documentation covers patient records, family records, pedigree drawing, teams, ownership, collaboration and APIs. Records can organise demographics, family history, measurements, clinical findings mapped to HPO, genotype information, diagnoses and files. The 2013 original paper described PhenoTips as open-source software designed around clinician workflow and standardised phenotype data; current deployment, licensing and service terms should be checked separately. Structured capture can improve consistency and reuse, but it does not replace clinical judgement, consent, security or verification of the underlying observations. This guide supports Primary Science, PSLE Science, Secondary Science, O-Level Science and STEM education as a longform evidence-reading route; it is not medical advice, a laboratory protocol, an admission promise or a career guarantee.
Related eduKate reading: Human Phenotype Ontology and structured clinical phenotypes; Exomiser and phenotype-driven variant prioritisation; Matchmaker Exchange and federated rare-disease matching; Science Learning Hub; Education Hub; STEM Education; Careers by Subject Capability.
Primary and official sources checked on 12 October 2026: Official PhenoTips documentation; Official patient-record documentation; Official family-history and pedigree documentation; Original PhenoTips platform paper; 2026 Singapore–Cambridge O-Level Biology syllabus; MOE G2/G3 Lower Secondary Science teaching and learning syllabus; 2026 MOE G2 Computing syllabus. Resource interfaces and recommendations can change, so preserve the release, access date and documentation used.
| Stage | Question | Evidence | Responsible output |
|---|---|---|---|
| Capture | What was observed, excluded, measured or diagnosed? | Patient record, HPO features, measurements and notes | A structured clinical profile |
| Relate | How do family members and inheritance patterns connect? | Family record, relationships and pedigree | A reviewable family model |
| Collaborate | Who may view or edit which records? | Ownership, teams, permissions and audit practices | Controlled multidisciplinary work |
| Exchange | How can records support genomic analysis? | APIs, identifiers, exports and validation | A governed interoperable workflow |
Inside this guide
1–12 · Foundations and design
- 1. Start with the clinical workflow
- 2. Understand the platform scope
- 3. Keep historical and current claims separate
- 4. Create governed patient identifiers
- 5. Capture demographics carefully
- 6. Map clinical findings to HPO
- 7. Separate observed and not observed
- 8. Represent not assessed explicitly
- 9. Record onset and temporal context
- 10. Capture measurements with units
- 11. Record medical and prenatal history
- 12. Record diagnoses as assertions
13–24 · Models and estimation
- 13. Capture genotype information
- 14. Attach files responsibly
- 15. Create family records
- 16. Draw the pedigree as a model
- 17. Represent relationship complexity
- 18. Protect relatives who are not patients
- 19. Use ownership deliberately
- 20. Use teams for durable collaboration
- 21. Grant view and edit separately
- 22. Audit changes and exports
- 23. Use APIs with the same governance
- 24. Validate imports and exports
25–36 · Diagnostics and interpretation
- 25. Connect to Phenopackets
- 26. Connect to analysis tools
- 27. Plan for recontact and reanalysis
- 28. Connect to Primary Science
- 29. Connect to Secondary Science
- 30. Connect to mathematics
- 31. Connect to computing
- 32. Connect to pathways
- 33. Did you know?
- 34. Write a reader-ready record summary
- 35. Build a validation ladder
- 36. Finish with a reproducible bundle
Section 1 of 36
1. Start with the clinical workflow
A structured record helps when it follows how evidence is collected, reviewed and shared rather than forcing every case into one rigid story.
Working checkpoint — PhenoTips: Map the real workflow and permission boundaries first. Keep the source record, phenotype mappings, pedigree review, genomic assertions, permissions and export version together.
For a classroom model, use fictional families and harmless invented traits rather than student data.
Section 2 of 36
2. Understand the platform scope
Current PhenoTips documentation covers patient and family records, pedigrees, collaboration, teams and programmatic interfaces.
Working checkpoint — patient record: Check the installed version and enabled modules. Read structured fields beside clinical notes, dates and who verified them.
For future study, connect records to biology, medicine, security and informatics while checking official pathways separately.
Section 3 of 36
3. Keep historical and current claims separate
The 2013 paper described the original software as open source; current products and hosted services have their own licensing and terms.
Working checkpoint — family record: Do not transfer an old licensing statement to a present deployment. Preserve unknown, unassessed and uncertain states; a completed form is not a complete person.
For scientific writing, distinguish the historical 2013 platform description from current product terms.
Section 4 of 36
4. Create governed patient identifiers
A patient record needs a stable local identity without exposing unnecessary direct identifiers to every collaborator.
Working checkpoint — pedigree: Use approved identifier and matching policies. Separate recorded observations from diagnosis, genomic interpretation and later reanalysis.
For students, ask which pedigree statements are observed, reported or inferred.
Section 5 of 36
5. Capture demographics carefully
Age, sex and other demographics can affect interpretation and identifiability.
Working checkpoint — HPO phenotype: Collect only fields needed for the care or research purpose. Treat patient, family, sample and variant identifiers as scientific data: a silent mismatch can expose or misassign information.
For parents, ask who can view, edit and export each record and why.
Section 6 of 36
6. Map clinical findings to HPO
Structured HPO terms make phenotypic observations searchable and computable while the original narrative preserves nuance.
Working checkpoint — record permissions: Keep source wording, mapper and review status. Keep the source record, phenotype mappings, pedigree review, genomic assertions, permissions and export version together.
For a classroom model, use fictional families and harmless invented traits rather than student data.
Section 7 of 36
7. Separate observed and not observed
The interface distinguishes findings observed in the patient from those specifically evaluated and not observed.
Working checkpoint — PhenoTips: Do not convert a blank field into a negative finding. Read structured fields beside clinical notes, dates and who verified them.
For future study, connect records to biology, medicine, security and informatics while checking official pathways separately.
Section 8 of 36
8. Represent not assessed explicitly
Unknown, unassessed and age-dependent states prevent false certainty in a phenotype profile.
Working checkpoint — patient record: Use the platform’s state distinctions consistently. Preserve unknown, unassessed and uncertain states; a completed form is not a complete person.
For scientific writing, distinguish the historical 2013 platform description from current product terms.
Section 9 of 36
9. Record onset and temporal context
The same phenotype can mean different things depending on age of onset, duration and examination time.
Working checkpoint — family record: Attach time context where supported. Separate recorded observations from diagnosis, genomic interpretation and later reanalysis.
For students, ask which pedigree statements are observed, reported or inferred.
Section 10 of 36
10. Capture measurements with units
Growth, laboratory or other quantitative observations need values, units, methods and dates.
Working checkpoint — pedigree: Reject impossible units and ranges at entry. Treat patient, family, sample and variant identifiers as scientific data: a silent mismatch can expose or misassign information.
For parents, ask who can view, edit and export each record and why.
Section 11 of 36
11. Record medical and prenatal history
Structured sections can organise prenatal, perinatal and medical history relevant to genomic interpretation.
Working checkpoint — HPO phenotype: Separate reported history from directly observed evidence. Keep the source record, phenotype mappings, pedigree review, genomic assertions, permissions and export version together.
For a classroom model, use fictional families and harmless invented traits rather than student data.
Section 12 of 36
12. Record diagnoses as assertions
A diagnosis field records an assertion that can be provisional, differential or confirmed depending on workflow.
Working checkpoint — record permissions: Make status and supporting evidence visible. Read structured fields beside clinical notes, dates and who verified them.
For future study, connect records to biology, medicine, security and informatics while checking official pathways separately.
Section 13 of 36
13. Capture genotype information
Genotype sections can connect candidate genes or variants with the patient record.
Working checkpoint — PhenoTips: Preserve assembly, transcript, allele and interpretation provenance. Preserve unknown, unassessed and uncertain states; a completed form is not a complete person.
For scientific writing, distinguish the historical 2013 platform description from current product terms.
Section 14 of 36
14. Attach files responsibly
Reports and images can support review but may contain identifiers and information not represented in structured fields.
Working checkpoint — patient record: Apply file-specific access, malware and retention controls. Separate recorded observations from diagnosis, genomic interpretation and later reanalysis.
For students, ask which pedigree statements are observed, reported or inferred.
Section 15 of 36
15. Create family records
Family records connect related patient records and support shared history without duplicating every relationship independently.
Working checkpoint — family record: Verify identity before linking records. Treat patient, family, sample and variant identifiers as scientific data: a silent mismatch can expose or misassign information.
For parents, ask who can view, edit and export each record and why.
Section 16 of 36
16. Draw the pedigree as a model
The pedigree editor represents people, relationships, affected status and other attributes used in genetic reasoning.
Working checkpoint — pedigree: Treat the diagram as a model that needs confirmation. Keep the source record, phenotype mappings, pedigree review, genomic assertions, permissions and export version together.
For a classroom model, use fictional families and harmless invented traits rather than student data.
Section 17 of 36
17. Represent relationship complexity
Adoption, donor conception, twins, consanguinity and uncertain parentage require careful, respectful representation.
Working checkpoint — HPO phenotype: Use supported relationship types and document uncertainty. Read structured fields beside clinical notes, dates and who verified them.
For future study, connect records to biology, medicine, security and informatics while checking official pathways separately.
Section 18 of 36
18. Protect relatives who are not patients
A pedigree can reveal sensitive information about family members who never entered the clinic or study.
Working checkpoint — record permissions: Minimise detail and follow approved family-data policy. Preserve unknown, unassessed and uncertain states; a completed form is not a complete person.
For scientific writing, distinguish the historical 2013 platform description from current product terms.
Section 19 of 36
19. Use ownership deliberately
Record ownership determines primary control and can affect who manages collaboration.
Working checkpoint — PhenoTips: Assign ownership to the correct user or team. Separate recorded observations from diagnosis, genomic interpretation and later reanalysis.
For students, ask which pedigree statements are observed, reported or inferred.
Section 20 of 36
20. Use teams for durable collaboration
Teams can support clinics, studies and multidisciplinary groups more reliably than ad hoc sharing with individuals.
Working checkpoint — patient record: Review membership and roles regularly. Treat patient, family, sample and variant identifiers as scientific data: a silent mismatch can expose or misassign information.
For parents, ask who can view, edit and export each record and why.
Section 21 of 36
21. Grant view and edit separately
A collaborator who needs to read a record may not need permission to change it.
Working checkpoint — family record: Apply least privilege and test access. Keep the source record, phenotype mappings, pedigree review, genomic assertions, permissions and export version together.
For a classroom model, use fictional families and harmless invented traits rather than student data.
Section 22 of 36
22. Audit changes and exports
Clinical corrections, phenotype remapping and exports can alter downstream analysis.
Working checkpoint — pedigree: Retain who changed what, when and why. Read structured fields beside clinical notes, dates and who verified them.
For future study, connect records to biology, medicine, security and informatics while checking official pathways separately.
Section 23 of 36
23. Use APIs with the same governance
Programmatic access can scale integration and also scale mistakes or exposure.
Working checkpoint — HPO phenotype: Use scoped credentials, validation and rate controls. Preserve unknown, unassessed and uncertain states; a completed form is not a complete person.
For scientific writing, distinguish the historical 2013 platform description from current product terms.
Section 24 of 36
24. Validate imports and exports
Round trips can lose field distinctions, ontology versions or pedigree relationships.
Working checkpoint — record permissions: Compare counts and meaning before and after exchange. Separate recorded observations from diagnosis, genomic interpretation and later reanalysis.
For students, ask which pedigree statements are observed, reported or inferred.
Section 25 of 36
25. Connect to Phenopackets
A structured patient profile can support exchange through standards such as Phenopackets when the chosen fields and mappings are validated.
Working checkpoint — PhenoTips: Test the exact export and schema version. Treat patient, family, sample and variant identifiers as scientific data: a silent mismatch can expose or misassign information.
For parents, ask who can view, edit and export each record and why.
Section 26 of 36
26. Connect to analysis tools
HPO profiles and genomic findings can inform tools such as Exomiser, but a ranking remains decision support.
Working checkpoint — patient record: Keep source record, exported profile and result version-linked. Keep the source record, phenotype mappings, pedigree review, genomic assertions, permissions and export version together.
For a classroom model, use fictional families and harmless invented traits rather than student data.
Section 27 of 36
27. Plan for recontact and reanalysis
New phenotypes, family information and variant knowledge can change interpretation over time.
Working checkpoint — family record: Version updates and follow the approved recontact policy. Read structured fields beside clinical notes, dates and who verified them.
For future study, connect records to biology, medicine, security and informatics while checking official pathways separately.
Section 28 of 36
28. Connect to Primary Science
A fictional family-trait chart can teach careful observation and relationship notation without using student health information.
Working checkpoint — pedigree: Choose harmless invented traits. Preserve unknown, unassessed and uncertain states; a completed form is not a complete person.
For scientific writing, distinguish the historical 2013 platform description from current product terms.
Section 29 of 36
29. Connect to Secondary Science
Inheritance, variation, phenotype and pedigree reasoning become connected to evidence quality and uncertainty.
Working checkpoint — HPO phenotype: Include unknown and unassessed states in the exercise. Separate recorded observations from diagnosis, genomic interpretation and later reanalysis.
For students, ask which pedigree statements are observed, reported or inferred.
Section 30 of 36
30. Connect to mathematics
Pedigree proportions and measurement trends show why denominators, uncertainty and sample size matter.
Working checkpoint — record permissions: Avoid predicting an individual outcome from a simple class ratio. Treat patient, family, sample and variant identifiers as scientific data: a silent mismatch can expose or misassign information.
For parents, ask who can view, edit and export each record and why.
Section 31 of 36
31. Connect to computing
Forms, controlled vocabularies, access control, APIs and audit logs show how software architecture affects scientific quality.
Working checkpoint — PhenoTips: Test permissions and invalid inputs, not only happy paths. Keep the source record, phenotype mappings, pedigree review, genomic assertions, permissions and export version together.
For a classroom model, use fictional families and harmless invented traits rather than student data.
Section 32 of 36
32. Connect to pathways
Genomic health records connect biology, medicine, counselling, informatics, security and data stewardship and can illuminate STEM pathways.
Working checkpoint — patient record: Check current official study and professional requirements separately. Read structured fields beside clinical notes, dates and who verified them.
For future study, connect records to biology, medicine, security and informatics while checking official pathways separately.
Section 33 of 36
33. Did you know?
The original PhenoTips paper emphasised a clinician-friendly interface backed by standardised data so structured capture could fit clinical workflow.
Working checkpoint — family record: Ease of entry still requires review and governance. Preserve unknown, unassessed and uncertain states; a completed form is not a complete person.
For scientific writing, distinguish the historical 2013 platform description from current product terms.
Section 34 of 36
34. Write a reader-ready record summary
Name the record version, phenotype states, time context, pedigree scope, genomic assertions, permissions, exports and unresolved uncertainties.
Working checkpoint — pedigree: Avoid copying more sensitive detail than the audience needs. Separate recorded observations from diagnosis, genomic interpretation and later reanalysis.
For students, ask which pedigree statements are observed, reported or inferred.
Section 35 of 36
35. Build a validation ladder
Verify identity and consent, review phenotype mappings and pedigree relationships, validate genomic representations, then test permissions and exports.
Working checkpoint — HPO phenotype: Correct the source record before downstream analysis. Treat patient, family, sample and variant identifiers as scientific data: a silent mismatch can expose or misassign information.
For parents, ask who can view, edit and export each record and why.
Section 36 of 36
36. Finish with a reproducible bundle
Deliver permitted structured exports, mapping and version reports, pedigree review notes, access matrix, audit record, checksums and downstream analysis links.
Working checkpoint — record permissions: Make the workflow traceable without redistributing unnecessary personal data. Keep the source record, phenotype mappings, pedigree review, genomic assertions, permissions and export version together.
For a classroom model, use fictional families and harmless invented traits rather than student data.
Contents · Previous section · Continue to the Science Learning Hub