Provides access to the GeneNetwork database and analysis functions using the GeneNetwork REST API.
Karl Broman wrote the GNapi R package for providing access to GeneNetwork from R. This package follows th structure and function of that package closely.
GeneNetwork collects data on genetically segregating populations (called groups) in a number of species including humans. Most of the phenotype data is "omic" data which are organized as datasets.
To check if the website is responding properly:
julia> check_gn()
GeneNetwork is alive.
200
Which species have data on them?
julia> list_species()
11×4 DataFrame
Row │ FullName Id Name TaxonomyId
│ String Int64 String Int64
─────┼───────────────────────────────────────────────────────────────────────
1 │ Mus musculus 1 mouse 10090
2 │ Rattus norvegicus 2 rat 10116
3 │ Arabidopsis thaliana 3 arabidopsis 3702
4 │ Homo sapiens 4 human 9606
5 │ Hordeum vulgare 5 barley 4513
6 │ Drosophila melanogaster 6 drosophila 7227
7 │ Macaca mulatta 7 macaque monkey 9544
8 │ Glycine max 8 soybean 3847
9 │ Solanum lycopersicum 9 tomato 4081
10 │ Populus trichocarpa 10 poplar 3689
11 │ Oryzias latipes (Japanese medaka) 11 Oryzias latipes 8090
To get information on a single species:
julia> list_species("rat")
1×4 DataFrame
Row │ FullName Id Name TaxonomyId
│ String Int64 String Int64
─────┼──────────────────────────────────────────────
1 │ Rattus norvegicus 2 rat 10116
You could also subset (safer):
julia> subset(list_species(), :Name => x->x.=="rat")
1×4 DataFrame
Row │ FullName Id Name TaxonomyId
│ String Int64 String Int64
─────┼──────────────────────────────────────────────
1 │ Rattus norvegicus 2 rat 10116
Since the information is organized by segregating population ("group"), it is useful to get a list for a preticular species you might be interested in.
julia> list_groups("rat")
7×8 DataFrame
Row │ DisplayName FullName GeneticType Id MappingMethodId Name SpeciesId public
│ String String String Int64 String String Int64 Int64
─────┼───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
1 │ Hybrid Rat Diversity Panel (Incl… Hybrid Rat Diversity Panel (Incl… None 10 1 HXBBXH 2 2
2 │ UIOWA SRxSHRSP F2 UIOWA SRxSHRSP F2 intercross 24 1 SRxSHRSPF2 2 2
3 │ NIH Heterogeneous Stock (RGSMC 2… NIH Heterogeneous Stock (RGSMC 2… None 42 1 HSNIH-RGSMC 2 2
4 │ NIH Heterogeneous Stock (Palmer) NIH Heterogeneous Stock (Palmer) None 55 1 HSNIH-Palmer 2 2
5 │ NWU WKYxF344 F2 Behavior NWU WKYxF344 F2 Behavior intercross 82 3 NWU_WKYxF344_F2 2 2
6 │ HIV-1Tg and Control HIV-1Tg and Control None 83 1 HIV-1Tg 2 2
7 │ HRDP-HXB/BXH Brain Proteome HRDP-HXB/BXH Brain Proteome None 87 1 HRDP_HXB-BXH-BP 2 2
You can see the type of population it is. Note the short name
(Name) as that will be used in queries involving that population
(group).
To get the genotypes of a group:
julia> get_geno("BXD") |> (x->first(x,10))
10×240 DataFrame
Row │ Chr Locus cM Mb BXD1 BXD2 BXD5 BXD6 BXD8 BXD9 BXD11 BXD12 BXD13 BXD14 BXD15 BXD16 BXD18 ⋯
│ String3 String31 Float64 Float64 String1 String1 String1 String1 String1 String1 String1 String1 String1 String1 String1 String1 Strin ⋯
─────┼────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
1 │ 1 rs31443144 1.5 3.01027 B B D D D B B D B B D D B ⋯
2 │ 1 rs6269442 1.5 3.4922 B B D D D B B D B B D D B
3 │ 1 rs32285189 1.63 3.5112 B B D D D B B D B B D D B
4 │ 1 rs258367496 1.63 3.6598 B B D D D B B D B B D D B
5 │ 1 rs32430919 1.75 3.77702 B B D D D B B D B B D D B ⋯
6 │ 1 rs36251697 1.88 3.81227 B B D D D B B D B B D D B
7 │ 1 rs30658298 2.01 4.43062 B B D D D B B D B B D D B
8 │ 1 rs51852623 2.01 4.44674 B B D D D B B D B B D D B
9 │ 1 rs31879829 2.14 4.51871 B B D D D B B D B B D D B ⋯
10 │ 1 rs36742481 2.14 4.77632 B B D D D B B D B B D D B
224 columns omitted
Currently, we only support the .geno format which returns a data
frame of genotypes with rows as marker and colyumns as individuals.
To list the (omic) datasets available for a group, you have to use the name as listed in the group list for a species:
julia> list_datasets("HSNIH-Palmer")
10×11 DataFrame
Row │ AvgID CreateTime DataScale FullName Id Long_Abbreviation ProbeFreezeId ShortName ⋯
│ Int64 String String String Int64 String Int64 String ⋯
─────┼────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
1 │ 24 Mon, 27 Aug 2018 00:00:00 GMT log2 HSNIH-Palmer Nucleus Accumbens C… 860 HSNIH-Rat-Acbc-RSeq-Aug18 347 HSNIH-Palmer Nuc ⋯
2 │ 24 Sun, 26 Aug 2018 00:00:00 GMT log2 HSNIH-Palmer Infralimbic Cortex … 861 HSNIH-Rat-IL-RSeq-Aug18 348 HSNIH-Palmer Inf
3 │ 24 Sat, 25 Aug 2018 00:00:00 GMT log2 HSNIH-Palmer Lateral Habenula RN… 862 HSNIH-Rat-LHB-RSeq-Aug18 349 HSNIH-Palmer Lat
4 │ 24 Fri, 24 Aug 2018 00:00:00 GMT log2 HSNIH-Palmer Prelimbic Cortex RN… 863 HSNIH-Rat-PL-RSeq-Aug18 350 HSNIH-Palmer Pre
5 │ 24 Thu, 23 Aug 2018 00:00:00 GMT log2 HSNIH-Palmer Orbitofrontal Corte… 864 HSNIH-Rat-VoLo-RSeq-Aug18 351 HSNIH-Palmer Orb ⋯
6 │ 24 Fri, 14 Sep 2018 00:00:00 GMT log2 HSNIH-Palmer Nucleus Accumbens C… 868 HSNIH-Rat-Acbc-RSeqlog2-Aug18 347 HSNIH-Palmer Nuc
7 │ 24 Fri, 14 Sep 2018 00:00:00 GMT log2 HSNIH-Palmer Infralimbic Cortex … 869 HSNIH-Rat-IL-RSeqlog2-Aug18 348 HSNIH-Palmer Inf
8 │ 24 Fri, 14 Sep 2018 00:00:00 GMT log2 HSNIH-Palmer Lateral Habenula RN… 870 HSNIH-Rat-LHB-RSeqlog2-Aug18 349 HSNIH-Palmer Lat
9 │ 24 Fri, 14 Sep 2018 00:00:00 GMT log2 HSNIH-Palmer Prelimbic Cortex RN… 871 HSNIH-Rat-PL-RSeqlog2-Aug18 350 HSNIH-Palmer Pre ⋯
10 │ 24 Fri, 14 Sep 2018 00:00:00 GMT log2 HSNIH-Palmer Orbitofrontal Corte… 872 HSNIH-Rat-VoLo-RSeqlog2-Aug18 351 HSNIH-Palmer Orb
4 columns omitted
This gives you a matrix with rows as individuals/samples/strains and
columns and "clinical" (non-omic) phenotypes. The number after the
underscore is the phenotype number (to be used later). Missing data
is denoted by x.
julia> get_pheno("HSNIH-Palmer") |> (x->first(x,10))
10×509 DataFrame
Row │ id HSR_10308 HSR_10309 HSR_10310 HSR_10311 HSR_10312 HSR_10313 HSR_10314 HSR_10315 HSR_10316 HSR_10317 HSR_10318 HSR_10319 HSR_10320 H ⋯
│ String15 String7 String7 String7 String7 String7 String7 String7 String15 String15 String15 String15 String15 String15 S ⋯
─────┼───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
1 │ 00071F4FAF x x x x x x x x x x x x x x ⋯
2 │ 00071F6771 x x x x x x x x x x x x x x
3 │ 00071F768E x x x x x x x x x x x x x x
4 │ 00071F95F9 x x x x x x x x x x x x x x
5 │ 00071FB160 x x x x x x x x x x x x x x ⋯
6 │ 00071FB747 x x x x x x x x x x x x x x
7 │ 00072069AD x x x x x x x x x x x x x x
8 │ 0007207A73 x x x x x x x x x x x x x x
9 │ 0007207BE7 x x x x x x x x x x x x x x ⋯
10 │ 00072126F3 x x x x x x x x x x x x x x
495 columns omitted
To get information on a particular (non-omic) trait use the group name and the trait number:
julia> info_dataset("HSNIH-Palmer","10308")
1×4 DataFrame
Row │ dataset_type description id name
│ String String Int64 String
─────┼───────────────────────────────────────────────────────────────────────────────
1 │ phenotype Central nervous system, behavior… 10308 reaction_time_pint1_5
To get information on a dataset (of omic traits) for a group, use:
julia> info_dataset("HSNIH-Rat-Acbc-RSeq-Aug18")
1×10 DataFrame
Row │ confidential data_scale dataset_type full_name id name public short_name ti ⋯
│ Int64 String String String Int64 String Int64 String St ⋯
─────┼───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
1 │ 0 log2 mRNA expression HSNIH-Palmer Nucleus Accumbens C… 860 HSNIH-Rat-Acbc-RSeq-0818 1 HSNIH-Palmer Nucleus Accumbens C… Nu ⋯
2 columns omitted
Get a list of the maximum LRS for each trait and position.
julia> info_pheno("HXBBXH") |> (x->first(x,10))
10×7 DataFrame
Row │ Additive Id LRS Locus PhenotypeId PublicationId Sequence
│ Float64? Int64 Float64? String? Int64 Int64 Int64
─────┼─────────────────────────────────────────────────────────────────────────────────
1 │ 0.0499968 10001 16.2831 rs106114574 1449 319 1
2 │ -0.0926364 10002 10.9777 rs63915446 1450 319 1
3 │ 0.60189 10003 13.6515 rs107486115 1451 319 1
4 │ -0.543799 10004 8.43965 D5Rat147 1452 319 1
5 │ 0.00854221 10005 18.5895 rs106114574 1453 319 1
6 │ -0.0142273 10006 11.9965 rs63915446 1454 319 1
7 │ 0.427167 10007 10.541 rs13452609 1455 319 1
8 │ -0.936806 10008 13.2494 rs8143630 1456 319 1
9 │ -0.635833 10009 9.97609 rs107549352 1457 319 1
10 │ -0.681451 10010 9.59226 D7Mit13 1458 319 1
You could also specify a group and a trait number or a dataset and a probename.
julia> info_pheno("BXD","10001")
1×4 DataFrame
Row │ additive id locus lrs
│ Float64 Int64 String Float64
─────┼──────────────────────────────────────
1 │ 2.39444 4 rs48756159 13.4975
julia> info_pheno("HC_M2_0606_P","1436869_at")
1×13 DataFrame
Row │ additive alias chr description id locus lrs mb mean name p_value se ⋯
│ Float64 String String String Int64 String Float64 Float64 Float64 String Float64 Nothing ⋯
─────┼───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
1 │ -0.214088 HHG1; HLP3; HPE3; SMMCI; Dsh; Hh… 5 sonic hedgehog (hedgehog) 99602 rs8253327 12.7711 28.4572 9.27909 1436869_at 0.306 ⋯
1 column omitted
julia> run_gemma("BXDPublish","10015",use_loco=true) |> (x->first(x,10))
10×6 DataFrame
Row │ Mb additive chr lod_score name p_value
│ Float64 Float64 Any Float64 String Float64
─────┼───────────────────────────────────────────────────────────
1 │ 3.01027 -0.906398 1 0.448914 rs31443144 0.355702
2 │ 3.4922 -0.906398 1 0.448914 rs6269442 0.355702
3 │ 3.5112 -0.906398 1 0.448914 rs32285189 0.355702
4 │ 3.6598 -0.906398 1 0.448914 rs258367496 0.355702
5 │ 3.77702 -0.906398 1 0.448914 rs32430919 0.355702
6 │ 3.81227 -0.906398 1 0.448914 rs36251697 0.355702
7 │ 4.43062 -0.906398 1 0.448914 rs30658298 0.355702
8 │ 4.44674 -0.906398 1 0.448914 rs51852623 0.355702
9 │ 4.51871 -0.906398 1 0.448914 rs31879829 0.355702
10 │ 4.77632 -0.906398 1 0.448914 rs36742481 0.355702
This function correlates a trait in a dataset against all traits in a target database.
- trait_id (required) - ID for trait used for correlation
- db (required) - DB name for the trait above (this is the Short_Abbreviation listed when you query for datasets)
- target_db (required) - Target DB name to be correlated against
- type - sample (default) | tissue
- method - pearson (default) | spearman
- return - Number of results to return (default = 500)
julia> run_correlation("HC_M2_0606_P","BXDPublish","1427571_at") |> (x->first(x,10))
10×4 DataFrame
Row │ #_strains p_value sample_r trait
│ Int64 Float64 Float64 String
─────┼───────────────────────────────────────────
1 │ 6 0.00480466 -0.942857 20511
2 │ 6 0.00480466 -0.942857 20724
3 │ 12 1.82889e-5 -0.923362 13536
4 │ 7 0.00680719 0.892857 10157
5 │ 7 0.00680719 -0.892857 20392
6 │ 6 0.0188455 0.885714 20479
7 │ 12 0.000189298 -0.875658 12762
8 │ 12 0.000245942 0.868653 12760
9 │ 7 0.0136973 -0.857143 20559
10 │ 10 0.00222003 -0.842424 10925