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Function Repository Resource:

 EnsemblPhenotype

Source Notebook

Retrieve phenotype information associated with genomic variants

Contributed by: Keiko Hirayama

ResourceFunction["EnsemblPhenotype"][phenotype, "GenomicFeatures"]

gives genomic features associated with a given phenotype description.

ResourceFunction["EnsemblPhenotype"][gene, "Phenotypes"]

gives phenotype annotations associated with a given gene.

ResourceFunction["EnsemblPhenotype"][{chromosome,{start,end}},"PhenotypeAnnotations"]]

gives genomics features and associated phenotypes found for a given chromosome region specified by start and end positions.

Details and Options

EnsemblPhenotype is based on Ensembl, which provides genomics information including phenotype annotations. Phenotypes are traits that are associated with the genomic variants.
Selected phenotype descriptions, such as the names of common genetic diseases or traits, or phenotype ontology accessions, as defined by the Human Phenotype Ontology, Experimental Factor Ontology, Orphanet, or Mammalian Phenotype Ontology can be used for the input phenotype to retrieve "GenomicFeatures".
To retrieve "PhenotypeAnnotations", specify the chromosome argument by a chromosome name such as "chromosome 1" or simply just "1" and the start and end positions of the genome sequence by the integers.
The following option can be given:
"Species""human"species for which to query; should be one of selected TaxonomicSpecies entities or names of species
Supported species include: human (Homo sapiens), cat (Felis catus), chicken (Gallus gallus), cow (Bos taurus), dog (Canis lupus_familiaris), goat (Capra hircus), horse (Equus caballus), mouse (Mus musculus), pig (Sus scrofa), rat (Rattus norvegicus), sheep (Ovis aries) and zebrafish (Danio rerio).
ResourceFunction["EnsemblPhenotype"][phenotype] is equivalent to ResourceFunction["EnsemblPhenotype"][phenotype,"GenomicFeatures"].

Examples

Basic Examples (3) 

Find genomic features associated with hypercholesterolemia:

In[1]:=
ResourceFunction[ "EnsemblPhenotype", ResourceSystemBase -> "https://www.wolframcloud.com/obj/resourcesystem/api/1.0"]["hypercholesterolemia", "GenomicFeatures"]
Out[1]=

Find phenotype annotations associated with the homeostatic iron (Fe) regulator (HFE) gene:

In[2]:=
ResourceFunction[ "EnsemblPhenotype", ResourceSystemBase -> "https://www.wolframcloud.com/obj/resourcesystem/api/1.0"]["HFE", "Phenotypes"]
Out[2]=

Find genomic features and phenotypes associated with the specified region of the human chromosome 19:

In[3]:=
ResourceFunction[ "EnsemblPhenotype", ResourceSystemBase -> "https://www.wolframcloud.com/obj/resourcesystem/api/1.0"][{"19", {44900000, 44910000}}, "PhenotypeAnnotations"]
Out[3]=

Scope (8) 

Find single-nucleotide polymorphisms (SNPs) associated with coffee consumption:

In[4]:=
snps = ResourceFunction[ "EnsemblPhenotype", ResourceSystemBase -> "https://www.wolframcloud.com/obj/resourcesystem/api/1.0"]["coffee consumption", "GenomicFeatures"]
Out[4]=

Analyze SNPs affecting the coffee consumption rate:

In[5]:=
coffeConsumptionRateAffectingSNP = snps[Select[! MissingQ[#BetaCoefficient] && ! StringFreeQ[#BetaCoefficient, "unit" | "cups"] &]][ All, {#ID[ "ExternalID"], (StringTrim[ StringSplit[#BetaCoefficient, i : "increase" | "decrease" :> i], " unit" | " NR" | " cups" ~~ __] /. {{d_, "increase"} :> ToExpression@d, {d_, "decrease"} :> -ToExpression@d})} &][ GroupBy[First -> Last], MeanAround][ ReverseSortBy[# /. a_Around :> a[[1]] &]]
Out[5]=

Plot SNPs associated with the change in coffee consumption rate:

In[6]:=
ListPlot[coffeConsumptionRateAffectingSNP, PlotRange -> Full, Ticks -> {None, Automatic}, PlotLabel -> "change in coffee consumption rate (cups per day)"]
Out[6]=

Find population frequency information for one of the notable SNPs:

In[7]:=
ResourceFunction[ ResourceObject[<|"Name" -> "NCBIGenomicSNPData", "ShortName" -> "NCBIGenomicSNPData", "UUID" -> "6f2d5756-cc3b-42f3-932f-bc6d163c3291", "ResourceType" -> "Function", "Version" -> "1.1.0", "Description" -> "Retrieve information on reference SNPs from the NCBI database", "RepositoryLocation" -> URL[ "https://www.wolframcloud.com/obj/resourcesystem/api/1.0"], "SymbolName" -> "FunctionRepository`$e00201f1a4fc41339cdf633e9bc60ebe`NCBIGenomicSNPData", "FunctionLocation" -> CloudObject[ "https://www.wolframcloud.com/obj/e9678ccc-563b-485b-aa0d-3c4f4cd9de18"]|>, ResourceSystemBase -> Automatic]][ Keys[coffeConsumptionRateSNP][1], "FrequencyData"]
Out[7]=

Find genes associated with the coffee consumption rate:

In[8]:=
coffeConsumptionRateAffectingGene = Dataset[GroupBy[ Flatten@Normal@ snps[Select[! MissingQ[#AssociatedGene] && ! MissingQ[#BetaCoefficient] && ! StringFreeQ[#BetaCoefficient, "unit" | "cups"] &]][All, Thread[(StringTrim[ StringSplit[#BetaCoefficient, i : "increase" | "decrease" :> i], " unit" | " NR" | " cups" ~~ __] /. {{d_, "increase"} :> ToExpression@d, {d_, "decrease"} :> -ToExpression@d}) -> #AssociatedGene] &][ ReverseSortBy[Mean[#] &]], Last -> First, MeanAround]]
Out[8]=

Plot genes associated with the change in coffee consumption rate:

In[9]:=
ListPlot[coffeConsumptionRateAffectingGene, PlotRange -> Full, Ticks -> {None, Automatic}, PlotLabel -> "change in coffee consumption rate (cups per day)"]
Out[9]=

Find the pathways associated with caffeine metabolism:

In[10]:=
caffinePathways = ResourceFunction[ ResourceObject[<|"Name" -> "WikiPathways", "ShortName" -> "WikiPathways", "UUID" -> "ab81ab85-9317-4fea-bb15-b96c7609c487", "ResourceType" -> "Function", "Version" -> "1.2.0", "Description" -> "Get pathways and additional information from the WikiPathways database", "RepositoryLocation" -> URL[ "https://www.wolframcloud.com/obj/resourcesystem/api/1.0"], "SymbolName" -> "FunctionRepository`$ee47149bfa5547ac9c3e8a4e782aaf9b`WikiPathways", "FunctionLocation" -> CloudObject[ "https://www.wolframcloud.com/obj/f13d5633-a6a7-4b04-a013-dffe85f4f453"]|>, ResourceSystemBase -> Automatic]]["caffeine", "SearchPaths"]
Out[10]=

Draw a pathway associated with the caffeine and theobromine metabolism:

In[11]:=
ResourceFunction[ ResourceObject[<|"Name" -> "WikiPathways", "ShortName" -> "WikiPathways", "UUID" -> "ab81ab85-9317-4fea-bb15-b96c7609c487", "ResourceType" -> "Function", "Version" -> "1.2.0", "Description" -> "Get pathways and additional information from the WikiPathways database", "RepositoryLocation" -> URL[ "https://www.wolframcloud.com/obj/resourcesystem/api/1.0"], "SymbolName" -> "FunctionRepository`$ee47149bfa5547ac9c3e8a4e782aaf9b`WikiPathways", "FunctionLocation" -> CloudObject[ "https://www.wolframcloud.com/obj/f13d5633-a6a7-4b04-a013-dffe85f4f453"]|>, ResourceSystemBase -> Automatic]][ caffinePathways[1, "Pathcode"], "Graphics", ImageSize -> 800]
Out[11]=

Options (1) 

Find genomic features associated with hyperactivity in mice:

In[12]:=
ResourceFunction[ "EnsemblPhenotype", ResourceSystemBase -> "https://www.wolframcloud.com/obj/resourcesystem/api/1.0"]["hyperactivity", "GenomicFeatures", "Species" -> Entity["TaxonomicSpecies", "MusMusculus::y84t7"]]
Out[12]=

Requirements

Wolfram Language 13.0 (December 2021) or above

Version History

  • 1.0.2 – 21 April 2025
  • 1.0.1 – 12 March 2025
  • 1.0.0 – 19 February 2025

Source Metadata

  • Citation:
    • Peter W Harrison, M Ridwan Amode, Olanrewaju Austine-Orimoloye, Andrey G Azov, Matthieu Barba, If Barnes, Arne Becker, Ruth Bennett, Andrew Berry, Jyothish Bhai, Simarpreet Kaur Bhurji, Sanjay Boddu, Paulo R Branco Lins, Lucy Brooks, Shashank Budhanuru Ramaraju, Lahcen I Campbell, Manuel Carbajo Martinez, Mehrnaz Charkhchi, Kapeel Chougule, Alexander Cockburn, Claire Davidson, Nishadi H De Silva, Kamalkumar Dodiya, Sarah Donaldson, Bilal El Houdaigui, Tamara El Naboulsi, Reham Fatima, Carlos Garcia Giron, Thiago Genez, Dionysios Grigoriadis, Gurpreet S Ghattaoraya, Jose Gonzalez Martinez, Tatiana A Gurbich, Matthew Hardy, Zoe Hollis, Thibaut Hourlier, Toby Hunt, Mike Kay, Vinay Kaykala, Tuan Le, Diana Lemos, Disha Lodha, Diego Marques-Coelho, Gareth Maslen, Gabriela Alejandra Merino, Louisse Paola Mirabueno, Aleena Mushtaq, Syed Nakib Hossain, Denye N Ogeh, Manoj Pandian Sakthivel, Anne Parker, Malcolm Perry, Ivana Piližota, Daniel Poppleton, Irina Prosovetskaia, Shriya Raj, José G Pérez-Silva, Ahamed Imran Abdul Salam, Shradha Saraf, Nuno Saraiva-Agostinho, Dan Sheppard, Swati Sinha, Botond Sipos, Vasily Sitnik, William Stark, Emily Steed, Marie-Marthe Suner, Likhitha Surapaneni, Kyösti Sutinen, Francesca Floriana Tricomi, David Urbina-Gómez, Andres Veidenberg, Thomas A Walsh, Doreen Ware, Elizabeth Wass, Natalie L Willhoft, Jamie Allen, Jorge Alvarez-Jarreta, Marc Chakiachvili, Bethany Flint, Stefano Giorgetti, Leanne Haggerty, Garth R Ilsley, Jon Keatley, Jane E Loveland, Benjamin Moore, Jonathan M Mudge, Guy Naamati, John Tate, Stephen J Trevanion, Andrea Winterbottom, Adam Frankish, Sarah E Hunt, Fiona Cunningham, Sarah Dyer, Robert D Finn, Fergal J Martin, and Andrew D Yates
      Ensembl 2024 Nucleic Acids Res. 2024, 52(D1):D891–D899 PMID: 37953337

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