From cda07d20c8d74c2814e0ae1ed3df4c00ced2081f Mon Sep 17 00:00:00 2001 From: jamesamcl Date: Tue, 2 Apr 2024 07:59:21 +0100 Subject: [PATCH 01/13] Update phenotype-data.md --- docs/reference/phenotype-data.md | 31 +++++++++++++------------------ 1 file changed, 13 insertions(+), 18 deletions(-) diff --git a/docs/reference/phenotype-data.md b/docs/reference/phenotype-data.md index 9a3850c3..c8603261 100644 --- a/docs/reference/phenotype-data.md +++ b/docs/reference/phenotype-data.md @@ -52,9 +52,7 @@ Phenotype data comes in many different shapes and forms. In the following, we wi #### Pre-coordinated -!!! example "Structured pre-coordinated phenotype data" - - Structured pre-coordinated phenotype data is data where the various [aspects of the phenotype term](../reference/core-concepts.md), such as the _bearer_ ("retinal blood vessels") and the _characteristic_ ("Attenuation", or "thinning/narrowing"), and the _modifier_ (in the case of HPO terms, simply _abnormal_), are combined ("coordinated") into a single term, e.g. "Attenuation of retinal blood vessels" (HP:0007843). +Structured pre-coordinated phenotype data is data where the various [aspects of the phenotype term](../reference/core-concepts.md), such as the _bearer_ ("retinal blood vessels") and the _characteristic_ ("Attenuation", or "thinning/narrowing"), and the _modifier_ (in the case of HPO terms, simply _abnormal_), are combined ("coordinated") into a single term, e.g. [`HP:0007843`](https://www.ebi.ac.uk/ols4/ontologies/hp/classes/http%253A%252F%252Fpurl.obolibrary.org%252Fobo%252FHP_0007843) "Attenuation of retinal blood vessels". Pre-coordinated phenotype data is popular in the clinical domain, where a lot of observations are taken by a clinician and recorded as "phenotypic abnormalities" with the goal of eventual diagnosis. @@ -343,9 +341,9 @@ _Trait + modifier_ pattern is used for example by databases such as the [Sacchar | 2006-05-05T00:05:00-00:00 | PMID:785224 | SGD:S000000854 | decreased resistance to chemicals | APO:0000003 | APO:0000087 | CHEBI:78661 | ZECO:0000111 | | 2010-07-07T00:07:00-00:00 | PMID:10545447 | SGD:S000000969 | decreased cell size | APO:0000003 | APO:0000052 | | | -- `APO:0000002` (abnormal) and `APO:0000003` (decreased) are modifiers. -- `APO:0000087` (resistance to chemicals), `APO:0000224` (RNA accumulation), `APO:0000052` (cell size) are biological attributes/traits. -- `CHEBI:78661` is recorded as an experimental condition, but should probably be interpreted as part of the bearer expression. +- [`APO:0000002`](https://www.ebi.ac.uk/ols4/ontologies/apo/classes/http%253A%252F%252Fpurl.obolibrary.org%252Fobo%252FAPO_0000002) (abnormal) and [`APO:0000003`](https://www.ebi.ac.uk/ols4/ontologies/apo/classes/http%253A%252F%252Fpurl.obolibrary.org%252Fobo%252FAPO_0000003) (decreased) are modifiers. +- [`APO:0000087`](https://www.ebi.ac.uk/ols4/ontologies/apo/classes/http%253A%252F%252Fpurl.obolibrary.org%252Fobo%252FAPO_0000087) (resistance to chemicals), [`APO:0000224`](https://www.ebi.ac.uk/ols4/ontologies/apo/classes/http%253A%252F%252Fpurl.obolibrary.org%252Fobo%252FAPO_0000224) (RNA accumulation), [`APO:0000052`](https://www.ebi.ac.uk/ols4/ontologies/apo/classes/http%253A%252F%252Fpurl.obolibrary.org%252Fobo%252FAPO_0000052) (cell size) are biological attributes/traits. +- [`CHEBI:78661`](https://www.ebi.ac.uk/ols4/ontologies/chebi/classes/http%253A%252F%252Fpurl.obolibrary.org%252Fobo%252FCHEBI_78661) (borrelidin) is recorded as an experimental condition, but should probably be interpreted as part of the bearer expression. - Note: SGD has different kinds of phenotype data, and it should be carefully evaluated which one it is. !!! info @@ -363,7 +361,7 @@ Instead of explicitly stating phenotypic modifiers (abnormal, morphology, change | 2024-01-05T11:54:24-05:00 | FB:FBrf0052655 | PMID:2385293 | FB:FBal0016988 | embryonic telson | FBbt:00000184 | | 2024-01-05T11:54:24-05:00 | FB:FBrf0058077 | PMID:8223248 | FB:FBal0001571 | larva | FBbt:00001727 | -- `FBbt:00000184` (embryonic telson) and `FBbt:00001727` (larva) are bearer terms. +- [`FBbt:00000184`](https://www.ebi.ac.uk/ols4/ontologies/fbbt/classes/http%253A%252F%252Fpurl.obolibrary.org%252Fobo%252FFBbt_00000184) (embryonic telson) and [`FBbt:00001727`](https://www.ebi.ac.uk/ols4/ontologies/fbbt/classes/http%253A%252F%252Fpurl.obolibrary.org%252Fobo%252FFBbt_00001727) (larva) are bearer terms. - The modifier is implicit in the data rather than explicitly stated. For example, [Flybase states on their website about the Dmel\torrv66 Allele (FBal0016988)](https://flybase.org/reports/FBal0016988) that the "phenotype manifests in the embryonic telson". - Note: FlyBase has different kinds of phenotype data (including pre-coordinated), and it should be carefully evaluated which one is which prior to integration. @@ -392,13 +390,13 @@ Examples: Lets break down the second to last row: -- ZFA:0009290 (glutamatergic neuron): The primary entity whose characteristic is being observed -- BFO:0000050 (part of): a relation used to connect the primary entity to the structure it is part of -- ZFA:0000008 (brain): the location of the primary entity being observed -- PATO:0040043 (increased proportionality to): the modified characteristic being observed +- [`ZFA:0009290`](https://www.ebi.ac.uk/ols4/ontologies/zfa/classes/http%253A%252F%252Fpurl.obolibrary.org%252Fobo%252FZFA_0009290) (glutamatergic neuron): The primary entity whose characteristic is being observed +- [`BFO:0000050`](https://www.ebi.ac.uk/ols4/search?q=BFO%3A0000050) (part of): a relation used to connect the primary entity to the structure it is part of +- [`ZFA:0000008`](https://www.ebi.ac.uk/ols4/ontologies/zfa/classes/http%253A%252F%252Fpurl.obolibrary.org%252Fobo%252FZFA_0000008) (brain): the location of the primary entity being observed +- [`PATO:0040043`](https://www.ebi.ac.uk/ols4/ontologies/pato/classes/http%253A%252F%252Fpurl.obolibrary.org%252Fobo%252FPATO_0040043) (increased proportionality to): the modified characteristic being observed - abnormal: the change modifier (note: not an ontology term) -- ZFA:0009276 (GABAergic neuron): the secondary entity being observed in relation to which the characteristic is measured -- ZFA:0000008 (brain): the location of the secondary entity +- [`ZFA:0009276`](https://www.ebi.ac.uk/ols4/ontologies/zfa/classes/http%253A%252F%252Fpurl.obolibrary.org%252Fobo%252FZFA_0009276) (GABAergic neuron): the secondary entity being observed in relation to which the characteristic is measured +- [`ZFA:0000008`](https://www.ebi.ac.uk/ols4/ontologies/zfa/classes/http%253A%252F%252Fpurl.obolibrary.org%252Fobo%252FZFA_0000008) (brain): the location of the secondary entity !!! example "Example: brain increased proportionality to glutamatergic neuron GABAergic neuron brain, abnormal" @@ -438,16 +436,13 @@ Here are some of the more "typical" kinds of data on the standardised/non-standa Qualitative and quantitative phenotype data represent two fundamental ways of describing characteristics or traits in biology, each providing different types of information: -Qualitative Phenotype Data: - -- Nature: This type of data describes qualities or characteristics that are observed but not measured with numbers. It often involves categorical or descriptive information. +**Qualitative Phenotype Data** describes qualities or characteristics that are observed but not measured with numbers. It often involves categorical or descriptive information. - Examples: The presence or absence of a specific physical trait (like eye color or wing shape in animals) or types of behavior (aggressive vs. passive). - Analysis: Qualitative data is analyzed by categorization and identification of patterns or variations. It is more about the 'type' or 'kind' of trait rather than its 'amount'. - Interpretation: Since it's descriptive, this data relies on subjective interpretation and classification. -Quantitative Phenotype Data: +**Quantitative Phenotype Data** is numerical and quantifies traits. It involves measurements of characteristics, often allowing for more precise and objective analysis. -- Nature: This data is numerical and quantifies traits. It involves measurements of characteristics, often allowing for more precise and objective analysis. - Examples: Height, weight, blood pressure, cholesterol levels, or the number of fruit produced by a plant. Quantitative traits can often be measured on a continuous scale, for example height of 35 cm, weight of 67 KG or blood pressure of 120/80. - Analysis: It involves statistical analysis, such as calculating mean, median, standard deviation, and applying various statistical tests. It allows for a more objective and replicable assessment. - Interpretation: Quantitative data provides a more concrete and measurable understanding of traits, making comparisons and statistical testing more straightforward. From 332c4e3f02bf4c743af94fb9c5ddb4bbadedbc33 Mon Sep 17 00:00:00 2001 From: jamesamcl Date: Tue, 2 Apr 2024 08:20:38 +0100 Subject: [PATCH 02/13] Update phenotype-data.md --- docs/reference/phenotype-data.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/reference/phenotype-data.md b/docs/reference/phenotype-data.md index c8603261..84c9dbbd 100644 --- a/docs/reference/phenotype-data.md +++ b/docs/reference/phenotype-data.md @@ -425,8 +425,8 @@ Here are some of the more "typical" kinds of data on the standardised/non-standa 1. Free text in clinical notes and scientific publications 1. Free text in specific database fields (for example a "height" column in a table about measurements of Giraffes) 1. Controlled but non-standardised vocabulary like enums in a datamodel (for example the keyword "abnormal" in the [ZFIN example above](#charmodbear)) -1. Controlled standardised vocabulary (like all the examples on this page) -1. Ontology terms (controlled vocabulary terms with well defined semantics - all the examples on this page) +1. Controlled standardised vocabulary (e.g. SNOMED CT) +1. Controlled vocabulary terms with well defined semantics (e.g. ontology terms from HP or MP) From 09435759a90b47f01009ccc0803156d9646c0674 Mon Sep 17 00:00:00 2001 From: jamesamcl Date: Tue, 2 Apr 2024 08:52:42 +0100 Subject: [PATCH 03/13] Update phenotype-data.md --- docs/reference/phenotype-data.md | 75 ++++++++++++++++---------------- 1 file changed, 38 insertions(+), 37 deletions(-) diff --git a/docs/reference/phenotype-data.md b/docs/reference/phenotype-data.md index 84c9dbbd..d6541816 100644 --- a/docs/reference/phenotype-data.md +++ b/docs/reference/phenotype-data.md @@ -43,10 +43,46 @@ is to convince how prevalent and diverse phenotype data is across the biomedical Phenotype data comes in many different shapes and forms. In the following, we will describe some of the most common features of such data: -- [Pre-coordinated](#precoordinated) -- [Post-coordinated](#postcoordinated) - [Standardised/non-standardized](#standardized) - [Quantitative/qualitative](#qual) +- [Pre-coordinated](#precoordinated) +- [Post-coordinated](#postcoordinated) + + + +#### Standardised/non-standardized + +Phenotype data can be standardised to varying degrees. It is not uncommon for data to be completely unstandardised. +Unfortunately, only a fraction of the available data is actually annotated using terms from controlled phenotype ontologies. +Here are some of the more "typical" kinds of data on the standardised/non-standardised spectrum: + +1. Free text in clinical notes and scientific publications +1. Free text in specific database fields (for example a "height" column in a table about measurements of Giraffes) +1. Controlled but non-standardised vocabulary like enums in a datamodel (for example the keyword "abnormal" in the [ZFIN example above](#charmodbear)) +1. Controlled standardised vocabulary (e.g. SNOMED CT) +1. Controlled vocabulary terms with well defined semantics (e.g. ontology terms from HP or MP) + + + + + +#### Quantitative/qualitative + +Qualitative and quantitative phenotype data represent two fundamental ways of describing characteristics or traits in biology, each providing different types of information: + +**Qualitative Phenotype Data** describes qualities or characteristics that are observed but not measured with numbers. It often involves categorical or descriptive information. +- Examples: The presence or absence of a specific physical trait (like eye color or wing shape in animals) or types of behavior (aggressive vs. passive). +- Analysis: Qualitative data is analyzed by categorization and identification of patterns or variations. It is more about the 'type' or 'kind' of trait rather than its 'amount'. +- Interpretation: Since it's descriptive, this data relies on subjective interpretation and classification. + +**Quantitative Phenotype Data** is numerical and quantifies traits. It involves measurements of characteristics, often allowing for more precise and objective analysis. + +- Examples: Height, weight, blood pressure, cholesterol levels, or the number of fruit produced by a plant. Quantitative traits can often be measured on a continuous scale, for example height of 35 cm, weight of 67 KG or blood pressure of 120/80. +- Analysis: It involves statistical analysis, such as calculating mean, median, standard deviation, and applying various statistical tests. It allows for a more objective and replicable assessment. +- Interpretation: Quantitative data provides a more concrete and measurable understanding of traits, making comparisons and statistical testing more straightforward. + +Qualitative data is descriptive and categorical, while quantitative data is numerical and measurable. Both types are essential for a comprehensive understanding of phenotypic traits, each offering unique insights into biological variation and complexity. + @@ -413,38 +449,3 @@ As one can see in the last example, bearers can be anything from simple atomic e - "lysine in heart muscle cells" (`lysine` part_of `cell` part_of (`muscle` part of `heart`)) - "lysine in the cytoplasm of heart muscle cells" (`lysine` part_of (`cytoplasm` part_of (`cell` part_of (`muscle` part of `heart`)))) - etc, etc - - - -#### Standardised/non-standardized - -Phenotype data can be standardised to varying degrees. It is not uncommon for data to be completely unstandardised. -Unfortunately, only a fraction of the available data is actually annotated using terms from controlled phenotype ontologies. -Here are some of the more "typical" kinds of data on the standardised/non-standardised spectrum: - -1. Free text in clinical notes and scientific publications -1. Free text in specific database fields (for example a "height" column in a table about measurements of Giraffes) -1. Controlled but non-standardised vocabulary like enums in a datamodel (for example the keyword "abnormal" in the [ZFIN example above](#charmodbear)) -1. Controlled standardised vocabulary (e.g. SNOMED CT) -1. Controlled vocabulary terms with well defined semantics (e.g. ontology terms from HP or MP) - - - - - -#### Quantitative/qualitative - -Qualitative and quantitative phenotype data represent two fundamental ways of describing characteristics or traits in biology, each providing different types of information: - -**Qualitative Phenotype Data** describes qualities or characteristics that are observed but not measured with numbers. It often involves categorical or descriptive information. -- Examples: The presence or absence of a specific physical trait (like eye color or wing shape in animals) or types of behavior (aggressive vs. passive). -- Analysis: Qualitative data is analyzed by categorization and identification of patterns or variations. It is more about the 'type' or 'kind' of trait rather than its 'amount'. -- Interpretation: Since it's descriptive, this data relies on subjective interpretation and classification. - -**Quantitative Phenotype Data** is numerical and quantifies traits. It involves measurements of characteristics, often allowing for more precise and objective analysis. - -- Examples: Height, weight, blood pressure, cholesterol levels, or the number of fruit produced by a plant. Quantitative traits can often be measured on a continuous scale, for example height of 35 cm, weight of 67 KG or blood pressure of 120/80. -- Analysis: It involves statistical analysis, such as calculating mean, median, standard deviation, and applying various statistical tests. It allows for a more objective and replicable assessment. -- Interpretation: Quantitative data provides a more concrete and measurable understanding of traits, making comparisons and statistical testing more straightforward. - -Qualitative data is descriptive and categorical, while quantitative data is numerical and measurable. Both types are essential for a comprehensive understanding of phenotypic traits, each offering unique insights into biological variation and complexity. From a95f144daa746222e7ed7bbbc1cead80e99b5947 Mon Sep 17 00:00:00 2001 From: jamesamcl Date: Tue, 2 Apr 2024 09:21:54 +0100 Subject: [PATCH 04/13] Add files via upload --- docs/images/semantics.svg | 1 + 1 file changed, 1 insertion(+) create mode 100644 docs/images/semantics.svg diff --git a/docs/images/semantics.svg b/docs/images/semantics.svg new file mode 100644 index 00000000..6fb22fb9 --- /dev/null +++ b/docs/images/semantics.svg @@ -0,0 +1 @@ + \ No newline at end of file From e933b99123e5ae263e01830c376a409b219d3a21 Mon Sep 17 00:00:00 2001 From: jamesamcl Date: Tue, 2 Apr 2024 09:23:11 +0100 Subject: [PATCH 05/13] Update phenotype-data.md --- docs/reference/phenotype-data.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/docs/reference/phenotype-data.md b/docs/reference/phenotype-data.md index d6541816..9a0db67d 100644 --- a/docs/reference/phenotype-data.md +++ b/docs/reference/phenotype-data.md @@ -52,6 +52,9 @@ Phenotype data comes in many different shapes and forms. In the following, we wi #### Standardised/non-standardized +![Spectrum of semantics](../images/semantics.svg) + + Phenotype data can be standardised to varying degrees. It is not uncommon for data to be completely unstandardised. Unfortunately, only a fraction of the available data is actually annotated using terms from controlled phenotype ontologies. 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z(o;(zedwXj$$ZHj%MVbkU70N8|98kf?5FqlD)q`@;M=URyt9+9x~j^;%S*@F+B!2m zeXNJCtN!)qNIWSiseLhCM@k{c_BHm|Z8VdZh-lHS6?o_+8%|7!06Tm8C9CF!eBcyP zAc^}^DDJ<)uYGK7Y{qa3+uM1jXJ#sX&;f^PF(#8uv{}G*8c8=e1_&j{N-1n?p5Y&e z6yz(E7M`7*{pDNM#x*vH_HZkl(5@tb`N4qI*r-h0ZS-F&KMQ*AXq5l(bW>S<@@cQh z$_bFcb`cN|_*b&`BW@nj_*uZQGpI(pu6iPcmPWf0jY;|TlaCr5Ca&@Q^K%)nu~$|! zfR|6g&flju){#R&9i)G!`rI)>ow)EeV#ySJzzq*L6J$6$Nq?N0l4IgH>}jJ%oF0Tk zZtV>tHj4YQtyXK-o#_}mATGg-x>;L{J%8 Date: Tue, 2 Apr 2024 10:16:04 +0100 Subject: [PATCH 07/13] Update phenotype-data.md --- docs/reference/phenotype-data.md | 256 +------------------------------ 1 file changed, 1 insertion(+), 255 deletions(-) diff --git a/docs/reference/phenotype-data.md b/docs/reference/phenotype-data.md index 9a0db67d..e2312adb 100644 --- a/docs/reference/phenotype-data.md +++ b/docs/reference/phenotype-data.md @@ -98,261 +98,7 @@ Pre-coordinated phenotype data is popular in the clinical domain, where a lot of [Phenopackets](http://phenopackets.org/) such as the one below are an emerging standard to capture and sharing disease and phenotype information about patients. Phenotypic features are captured in phenopackets as pre-coordinated HPO terms. -??? Phenopacket example - - ``` - { - "id": "PMID:23559858-Ajmal-2013-BBS1-IV-5/family_A", - "subject": { - "id": "IV-5/family A", - "timeAtLastEncounter": { - "age": { - "iso8601duration": "P26Y" - } - }, - "sex": "MALE", - "taxonomy": { - "id": "NCBITaxon:9606", - "label": "Homo sapiens" - } - }, - "phenotypicFeatures": [ - { - "type": { - "id": "HP:0007843", - "label": "Attenuation of retinal blood vessels" - }, - "evidence": [ - { - "evidenceCode": { - "id": "ECO:0000033", - "label": "author statement supported by traceable reference" - }, - "reference": { - "id": "PMID:23559858", - "description": "A family was reported in which two affected members had a splicing variant in BBS1, c.47+1G>T." - } - } - ] - }, - { - "type": { - "id": "HP:0001513", - "label": "Obesity" - }, - "evidence": [ - { - "evidenceCode": { - "id": "ECO:0000033", - "label": "author statement supported by traceable reference" - }, - "reference": { - "id": "PMID:23559858", - "description": "A family was reported in which two affected members had a splicing variant in BBS1, c.47+1G>T." - } - } - ] - }, - { - "type": { - "id": "HP:0000608", - "label": "Macular degeneration" - }, - "evidence": [ - { - "evidenceCode": { - "id": "ECO:0000033", - "label": "author statement supported by traceable reference" - }, - "reference": { - "id": "PMID:23559858", - "description": "A family was reported in which two affected members had a splicing variant in BBS1, c.47+1G>T." - } - } - ] - }, - { - "type": { - "id": "HP:0000486", - "label": "Strabismus" - }, - "evidence": [ - { - "evidenceCode": { - "id": "ECO:0000033", - "label": "author statement supported by traceable reference" - }, - "reference": { - "id": "PMID:23559858", - "description": "A family was reported in which two affected members had a splicing variant in BBS1, c.47+1G>T." - } - } - ] - }, - { - "type": { - "id": "HP:0001328", - "label": "Specific learning disability" - }, - "evidence": [ - { - "evidenceCode": { - "id": "ECO:0000033", - "label": "author statement supported by traceable reference" - }, - "reference": { - "id": "PMID:23559858", - "description": "A family was reported in which two affected members had a splicing variant in BBS1, c.47+1G>T." - } - } - ] - }, - { - "type": { - "id": "HP:0000510", - "label": "Rod-cone dystrophy" - }, - "evidence": [ - { - "evidenceCode": { - "id": "ECO:0000033", - "label": "author statement supported by traceable reference" - }, - "reference": { - "id": "PMID:23559858", - "description": "A family was reported in which two affected members had a splicing variant in BBS1, c.47+1G>T." - } - } - ] - }, - { - "type": { - "id": "HP:0001263", - "label": "Global developmental delay" - }, - "evidence": [ - { - "evidenceCode": { - "id": "ECO:0000033", - "label": "author statement supported by traceable reference" - }, - "reference": { - "id": "PMID:23559858", - "description": "A family was reported in which two affected members had a splicing variant in BBS1, c.47+1G>T." - } - } - ] - } - ], - "interpretations": [ - { - "id": "PMID:23559858-Ajmal-2013-BBS1-IV-5/family_A", - "progressStatus": "SOLVED", - "diagnosis": { - "disease": { - "id": "OMIM:209900", - "label": "BARDET-BIEDL SYNDROME 1; BBS1" - }, - "genomicInterpretations": [ - { - "subjectOrBiosampleId": "IV-5/family A", - "interpretationStatus": "CAUSATIVE", - "variantInterpretation": { - "variationDescriptor": { - "id": "clinvar:1324292", - "geneContext": { - "valueId": "ENSG00000174483", - "symbol": "BBS1", - "alternateIds": [ - "HGNC:966", - "entrez:582", - "ensembl:ENSG00000174483", - "symbol:BBS1" - ] - }, - "vcfRecord": { - "genomeAssembly": "GRCh37", - "chrom": "11", - "pos": "66278178", - "ref": "G", - "alt": "T" - }, - "allelicState": { - "id": "GENO:0000136", - "label": "homozygous" - } - } - } - } - ] - } - } - ], - "metaData": { - "created": "1970-01-01T00:00:00Z", - "submittedBy": "HPO:probinson", - "resources": [ - { - "id": "hp", - "name": "human phenotype ontology", - "url": "http://purl.obolibrary.org/obo/hp.owl", - "version": "2018-03-08", - "namespacePrefix": "HP", - "iriPrefix": "http://purl.obolibrary.org/obo/HP_" - }, - { - "id": "pato", - "name": "Phenotype And Trait Ontology", - "url": "http://purl.obolibrary.org/obo/pato.owl", - "version": "2018-03-28", - "namespacePrefix": "PATO", - "iriPrefix": "http://purl.obolibrary.org/obo/PATO_" - }, - { - "id": "geno", - "name": "Genotype Ontology", - "url": "http://purl.obolibrary.org/obo/geno.owl", - "version": "19-03-2018", - "namespacePrefix": "GENO", - "iriPrefix": "http://purl.obolibrary.org/obo/GENO_" - }, - { - "id": "ncbitaxon", - "name": "NCBI organismal classification", - "url": "http://purl.obolibrary.org/obo/ncbitaxon.owl", - "version": "2018-03-02", - "namespacePrefix": "NCBITaxon", - "iriPrefix": "http://purl.obolibrary.org/obo/NCBITaxon_" - }, - { - "id": "eco", - "name": "Evidence and Conclusion Ontology", - "url": "http://purl.obolibrary.org/obo/eco.owl", - "version": "2018-11-10", - "namespacePrefix": "ECO", - "iriPrefix": "http://purl.obolibrary.org/obo/ECO_" - }, - { - "id": "omim", - "name": "Online Mendelian Inheritance in Man", - "url": "https://www.omim.org", - "version": "2018-03-08", - "namespacePrefix": "OMIM", - "iriPrefix": "https://omim.org/entry/" - }, - { - "id": "clinvar", - "name": "Clinical Variation", - "url": "https://www.ncbi.nlm.nih.gov/clinvar/", - "version": "2023-04-06", - "namespacePrefix": "clinvar", - "iriPrefix": "https://www.ncbi.nlm.nih.gov/clinvar/variation/" - } - ], - "phenopacketSchemaVersion": "2.0.0" - } - } - ``` +![Spectrum of semantics](../images/phenopacket.png) Apart from clinical diagnostics, pre-coordinated phenotype terms are used in many other contexts such as model organism research (e.g. [IMPC](https://www.mousephenotype.org/)) or the curation of [Genome Wide Association Studies](https://www.ebi.ac.uk/gwas/). From 9cb71b2118e1db729ed04ddc1aac8a78a439bc50 Mon Sep 17 00:00:00 2001 From: jamesamcl Date: Tue, 2 Apr 2024 10:21:26 +0100 Subject: [PATCH 08/13] Update phenotype-data.md --- docs/reference/phenotype-data.md | 7 ++----- 1 file changed, 2 insertions(+), 5 deletions(-) diff --git a/docs/reference/phenotype-data.md b/docs/reference/phenotype-data.md index e2312adb..792961db 100644 --- a/docs/reference/phenotype-data.md +++ b/docs/reference/phenotype-data.md @@ -93,12 +93,9 @@ Qualitative data is descriptive and categorical, while quantitative data is nume Structured pre-coordinated phenotype data is data where the various [aspects of the phenotype term](../reference/core-concepts.md), such as the _bearer_ ("retinal blood vessels") and the _characteristic_ ("Attenuation", or "thinning/narrowing"), and the _modifier_ (in the case of HPO terms, simply _abnormal_), are combined ("coordinated") into a single term, e.g. [`HP:0007843`](https://www.ebi.ac.uk/ols4/ontologies/hp/classes/http%253A%252F%252Fpurl.obolibrary.org%252Fobo%252FHP_0007843) "Attenuation of retinal blood vessels". -Pre-coordinated phenotype data is popular in the clinical domain, where a lot of observations are taken by a clinician and recorded as "phenotypic abnormalities" with the goal of eventual diagnosis. +Pre-coordinated phenotype data is popular in the clinical domain, where a lot of observations are taken by a clinician and recorded as "phenotypic abnormalities" with the goal of eventual diagnosis. [Phenopackets](http://phenopackets.org/) such as the one below are an emerging standard to capture and sharing disease and phenotype information about patients. Phenotypic features are captured in phenopackets as pre-coordinated HPO terms. -[Phenopackets](http://phenopackets.org/) such as the one below are an emerging standard to capture and sharing disease and phenotype information about patients. -Phenotypic features are captured in phenopackets as pre-coordinated HPO terms. - -![Spectrum of semantics](../images/phenopacket.png) +![Phenopacket](../images/phenopacket.png) Apart from clinical diagnostics, pre-coordinated phenotype terms are used in many other contexts such as model organism research (e.g. [IMPC](https://www.mousephenotype.org/)) or the curation of [Genome Wide Association Studies](https://www.ebi.ac.uk/gwas/). From de7bec140e6fcc1caf8b2728b860635d8eb579c8 Mon Sep 17 00:00:00 2001 From: jamesamcl Date: Tue, 2 Apr 2024 10:24:05 +0100 Subject: [PATCH 09/13] Update phenotype-data.md --- docs/reference/phenotype-data.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/reference/phenotype-data.md b/docs/reference/phenotype-data.md index 792961db..6339b4d5 100644 --- a/docs/reference/phenotype-data.md +++ b/docs/reference/phenotype-data.md @@ -97,7 +97,7 @@ Pre-coordinated phenotype data is popular in the clinical domain, where a lot of ![Phenopacket](../images/phenopacket.png) -Apart from clinical diagnostics, pre-coordinated phenotype terms are used in many other contexts such as model organism research (e.g. [IMPC](https://www.mousephenotype.org/)) or the curation of [Genome Wide Association Studies](https://www.ebi.ac.uk/gwas/). +Apart from clinical diagnostics, pre-coordinated phenotype terms are used in many other contexts such as model organism research (e.g. [IMPC](https://www.mousephenotype.org/)) or the curation of [Genome Wide Association Studies](https://www.ebi.ac.uk/gwas/). For example, the GWAS Catalog can be browsed by the pre-coordinated term ["cardiac hypertrophy" (enlarged heart)](https://www.ebi.ac.uk/gwas/efotraits/EFO_0002503) to find gene assocations with the phenotype. From 5ab72680eda47ef2b4308d551a6613e4007f614f Mon Sep 17 00:00:00 2001 From: jamesamcl Date: Tue, 2 Apr 2024 12:58:53 +0100 Subject: [PATCH 10/13] Update phenotype-data.md --- docs/reference/phenotype-data.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/docs/reference/phenotype-data.md b/docs/reference/phenotype-data.md index 6339b4d5..4e055da7 100644 --- a/docs/reference/phenotype-data.md +++ b/docs/reference/phenotype-data.md @@ -59,11 +59,11 @@ Phenotype data can be standardised to varying degrees. It is not uncommon for da Unfortunately, only a fraction of the available data is actually annotated using terms from controlled phenotype ontologies. Here are some of the more "typical" kinds of data on the standardised/non-standardised spectrum: -1. Free text in clinical notes and scientific publications -1. Free text in specific database fields (for example a "height" column in a table about measurements of Giraffes) -1. Controlled but non-standardised vocabulary like enums in a datamodel (for example the keyword "abnormal" in the [ZFIN example above](#charmodbear)) -1. Controlled standardised vocabulary (e.g. SNOMED CT) -1. Controlled vocabulary terms with well defined semantics (e.g. ontology terms from HP or MP) +1. **Free text** in clinical notes and scientific publications. For example, the paper ["Rare genetic variants impact muscle strength" (Huang et al.)](https://www.nature.com/articles/s41467-023-39247-1) mentions phenotypic traits including muscle strength, hand grip strength, body size, body weight, BMI, and whole-body muscle mass. +1. **Free text in specific database fields** (for example a "height" column in a table about measurements of Giraffes) +1. **Controlled but non-standardised vocabulary** like enums in a datamodel (for example the keyword "abnormal" in the [ZFIN example above](#charmodbear)) +1. **Controlled standardised vocabulary** (e.g. SNOMED CT) +1. **Controlled vocabulary terms with well defined semantics** (e.g. ontology terms from HP or MP) From c2ad021a34291d80e4b909c2f1da3daf0aae328d Mon Sep 17 00:00:00 2001 From: jamesamcl Date: Tue, 2 Apr 2024 13:46:04 +0100 Subject: [PATCH 11/13] Add files via upload --- docs/images/pre_vs_post.svg | 1 + 1 file changed, 1 insertion(+) create mode 100644 docs/images/pre_vs_post.svg diff --git a/docs/images/pre_vs_post.svg b/docs/images/pre_vs_post.svg new file mode 100644 index 00000000..9b2312b3 --- /dev/null +++ b/docs/images/pre_vs_post.svg @@ -0,0 +1 @@ + \ No newline at end of file From 23873750a868f6cd9bde918618a83fccba6493df Mon Sep 17 00:00:00 2001 From: jamesamcl Date: Tue, 2 Apr 2024 13:47:59 +0100 Subject: [PATCH 12/13] Update phenotype-data.md --- docs/reference/phenotype-data.md | 20 ++++++++------------ 1 file changed, 8 insertions(+), 12 deletions(-) diff --git a/docs/reference/phenotype-data.md b/docs/reference/phenotype-data.md index 4e055da7..8bd35340 100644 --- a/docs/reference/phenotype-data.md +++ b/docs/reference/phenotype-data.md @@ -11,11 +11,10 @@ The goals of this document are: - [Some examples of phenotype data](#examples) - [Different shapes of phenotype data](#shape) - - [Pre-coordinated](#precoordinated) - - [Post-coordinated](#postcoordinated) - [Standardised/non-standardized](#standardized) - [Quantitative/qualitative](#qual) - + - [Pre-coordinated/post-coordinated](#precoordinated) + ### Some examples of phenotype data @@ -45,8 +44,7 @@ Phenotype data comes in many different shapes and forms. In the following, we wi - [Standardised/non-standardized](#standardized) - [Quantitative/qualitative](#qual) -- [Pre-coordinated](#precoordinated) -- [Post-coordinated](#postcoordinated) +- [Pre-coordinated/post-coordinated](#precoordinated) @@ -89,9 +87,11 @@ Qualitative data is descriptive and categorical, while quantitative data is nume -#### Pre-coordinated +#### Pre-coordinated vs. post-coordinated + +![Spectrum of semantics](../images/pre_vs_post.svg) -Structured pre-coordinated phenotype data is data where the various [aspects of the phenotype term](../reference/core-concepts.md), such as the _bearer_ ("retinal blood vessels") and the _characteristic_ ("Attenuation", or "thinning/narrowing"), and the _modifier_ (in the case of HPO terms, simply _abnormal_), are combined ("coordinated") into a single term, e.g. [`HP:0007843`](https://www.ebi.ac.uk/ols4/ontologies/hp/classes/http%253A%252F%252Fpurl.obolibrary.org%252Fobo%252FHP_0007843) "Attenuation of retinal blood vessels". +**Pre-coordinated** phenotype data is data where the various [aspects of the phenotype term](../reference/core-concepts.md), such as the _bearer_ ("retinal blood vessels") and the _characteristic_ ("Attenuation", or "thinning/narrowing"), and the _modifier_ (in the case of HPO terms, simply _abnormal_), are combined ("coordinated") into a single term, e.g. [`HP:0007843`](https://www.ebi.ac.uk/ols4/ontologies/hp/classes/http%253A%252F%252Fpurl.obolibrary.org%252Fobo%252FHP_0007843) "Attenuation of retinal blood vessels". Pre-coordinated phenotype data is popular in the clinical domain, where a lot of observations are taken by a clinician and recorded as "phenotypic abnormalities" with the goal of eventual diagnosis. [Phenopackets](http://phenopackets.org/) such as the one below are an emerging standard to capture and sharing disease and phenotype information about patients. Phenotypic features are captured in phenopackets as pre-coordinated HPO terms. @@ -99,11 +99,7 @@ Pre-coordinated phenotype data is popular in the clinical domain, where a lot of Apart from clinical diagnostics, pre-coordinated phenotype terms are used in many other contexts such as model organism research (e.g. [IMPC](https://www.mousephenotype.org/)) or the curation of [Genome Wide Association Studies](https://www.ebi.ac.uk/gwas/). For example, the GWAS Catalog can be browsed by the pre-coordinated term ["cardiac hypertrophy" (enlarged heart)](https://www.ebi.ac.uk/gwas/efotraits/EFO_0002503) to find gene assocations with the phenotype. - - -#### Post-coordinated - -Post-coordinated phenotype curation simply means that the different constituents of phenotype (characteristic, bearer, modifier etc) are captured individually. +**Post-coordinated** phenotype curation simply means that the different constituents of phenotype (characteristic, bearer, modifier etc) are captured individually. This has certain advantages. For example, the phenotype space is _enormous_, as you can measure variations in many observable charactertics from chemical entities present in the blood, the microbiome to a host of morphological and developmental abnormalities. Instead of having individual (controlled vocabulary) terms for `increased level of X`, `decreased level X`, `abnormal level of X`, `increased level of X in blood` for thousands of chemical compounds synthesized by the human body, you just have "increased level", "blood" and all the chemical compounds, and capture them separately. From 80e63a7ac8189d1be978e14b3c07c16a78b45d55 Mon Sep 17 00:00:00 2001 From: jamesamcl Date: Tue, 2 Apr 2024 15:49:13 +0100 Subject: [PATCH 13/13] Update phenotype-data.md --- docs/reference/phenotype-data.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/reference/phenotype-data.md b/docs/reference/phenotype-data.md index 8bd35340..5b341f6b 100644 --- a/docs/reference/phenotype-data.md +++ b/docs/reference/phenotype-data.md @@ -97,7 +97,7 @@ Pre-coordinated phenotype data is popular in the clinical domain, where a lot of ![Phenopacket](../images/phenopacket.png) -Apart from clinical diagnostics, pre-coordinated phenotype terms are used in many other contexts such as model organism research (e.g. [IMPC](https://www.mousephenotype.org/)) or the curation of [Genome Wide Association Studies](https://www.ebi.ac.uk/gwas/). For example, the GWAS Catalog can be browsed by the pre-coordinated term ["cardiac hypertrophy" (enlarged heart)](https://www.ebi.ac.uk/gwas/efotraits/EFO_0002503) to find gene assocations with the phenotype. +Apart from clinical diagnostics, pre-coordinated phenotype terms are used in many other contexts such as model organism research (e.g. [IMPC](https://www.mousephenotype.org/)) or the curation of [Genome Wide Association Studies](https://www.ebi.ac.uk/gwas/). For example, IMPC can be searched using the pre-coordinated term ["enlarged heart"](https://www.mousephenotype.org/data/phenotypes/MP:0000274) to find knockout mice with this phenotype, and similarly the GWAS Catalog can be browsed by the pre-coordinated term ["cardiac hypertrophy"](https://www.ebi.ac.uk/gwas/efotraits/EFO_0002503) to find gene assocations with the phenotype from GWAS studies. **Post-coordinated** phenotype curation simply means that the different constituents of phenotype (characteristic, bearer, modifier etc) are captured individually. This has certain advantages.