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 the 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> GeneNetworkAPI.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 particular 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 columns 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 as "clinical" (non-omic) phenotypes. The number after the underscore is the phenotype number (to be used later). Some data may be missing.
julia> get_pheno("HSNIH-Palmer") |> (x->x[81:100,:]) |> show
20×509 DataFrame
Row │ id HSR_10308 HSR_10309 HSR_10310 HSR_10311 HSR_10312 HSR_10313 HSR_10314 HSR_10315 HSR_10316 HSR_10317 ⋯
│ String15 Float64? Float64? Float64? Float64? Float64? Float64? Float64? Float64? Float64? Float64? ⋯
─────┼──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
1 │ 000721E489 missing missing missing missing missing missing missing missing missing missing ⋯
2 │ 00072AAC0D missing missing missing missing missing missing missing missing missing missing
3 │ 00072AC972 missing missing missing missing missing missing missing missing missing missing
4 │ 00077E61DC missing missing missing missing missing missing missing missing missing missing
5 │ 00077E61EC missing missing missing missing missing missing missing missing missing missing ⋯
6 │ 00077E61F3 18.0 43.0 25.0 42.0 36.0 8.0 43.0 -0.514286 1.14667 1.125
7 │ 00077E61F5 missing missing missing missing missing missing missing missing missing missing
8 │ 00077E6204 missing missing missing missing missing missing missing missing missing missing
9 │ 00077E6207 22.0 63.0 54.0 77.0 54.0 42.0 77.0 0.914286 1.07959 1.0 ⋯
10 │ 00077E6299 missing missing missing missing missing missing missing missing missing missing
11 │ 00077E62CD missing missing missing missing missing missing missing missing missing missing
12 │ 00077E62D2 55.0 54.0 31.0 16.0 25.0 18.0 55.0 -2.73333 0.780392 1.22222
13 │ 00077E633D 25.0 47.0 58.0 35.0 27.0 35.0 58.0 -0.314286 1.19474 0.925926 ⋯
14 │ 00077E634B missing missing missing missing missing missing missing missing missing missing
15 │ 00077E63D9 missing missing missing missing missing missing missing missing missing missing
16 │ 00077E641E missing missing missing missing missing missing missing missing missing missing
17 │ 00077E6433 112.0 131.0 117.0 60.0 82.0 70.0 131.0 -3.94286 1.95222 2.54546 ⋯
18 │ 00077E64B3 missing missing missing missing missing missing missing missing missing missing
19 │ 00077E64BA 135.0 154.0 188.0 267.0 98.0 76.0 267.0 -3.65714 4.19178 4.35484
20 │ 00077E64C1 missing missing missing missing missing missing missing missing missing missing
498 columns omitted
To obtain omics phenotypes, you can utilize the get_omics() function, which provides a matrix with individuals/samples/strains as rows and omic phenotypes as columns. This function requires the input of a short abbreviation representing the available (omic) datasets for a particular group. To obtain the short abbreviation, you can refer to the section titled "List datasets for a group" and use the list_dataset() function.
For instance, if you want to acquire the phenotype matrix corresponding to "HSNIH-Palmer Infralimbic Cortex RNA-Seq (Aug18) rlog," you would use its respective short abbreviation.
julia> get_omics("HSNIH-Rat-IL-RSeq-0818")
6171×32624 DataFrame
Row │ id ENSRNOG00000000001 ENSRNOG00000000007 ENSRNOG00000000008 ENSRNOG00000000009 ENSRNOG00000000010 ENSRNOG00000000012 ENSRNO ⋯
│ String15 Float64? Float64? Float64? Float64? Float64? Float64? Float6 ⋯
──────┼─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
1 │ 00071F4FAF missing missing missing missing missing missing ⋯
2 │ 00071F6771 missing missing missing missing missing missing
3 │ 00071F768E missing missing missing missing missing missing
4 │ 00071F95F9 missing missing missing missing missing missing
5 │ 00071FB160 missing missing missing missing missing missing ⋯
6 │ 00071FB747 missing missing missing missing missing missing
7 │ 00072069AD missing missing missing missing missing missing
⋮ │ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋱
6165 │ 0007929918 missing missing missing missing missing missing
6166 │ 0007929945 missing missing missing missing missing missing ⋯
6167 │ 00077E840E missing missing missing missing missing missing
6168 │ 00077E9879 missing missing missing missing missing missing
6169 │ 00077E9920 missing missing missing missing missing missing
6170 │ 00077E9D84 missing missing missing missing missing missing ⋯
6171 │ 00077E949D missing missing missing missing missing missing
32617 columns and 6157 rows omitted
underscore is the phenotype number (to be used later). Some data may be missing.
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 performs a one-dimensional genome scan. The arguments are
- db (required) - DB name for trait above (Short_Abbreviation listed when you query for datasets)
- trait (required) - ID for trait being mapped
- method - hk (default) | ehk | em | imp | mr | mr-imp | mr-argmax ; Corresponds to the "method" option for the R/qtl scanone function.
- model - normal (default) | binary | 2-part | np ; corresponds to the "model" option for the R/qtl scanone function
- n_perm - number of permutations; 0 by default
- control_marker - Name of marker to use as control; this relies on the user knowing the name of the marker they want to use as a covariate
- interval_mapping - Whether to use interval mapping; "false" by default
julia> run_rqtl("BXDPublish", "10015") |> (x->first(x,10))
10×5 DataFrame
Row │ Mb cM chr lod_score name
│ Float64 Float64 Any Float64 String
─────┼───────────────────────────────────────────────
1 │ 3.01027 3.01027 1 0.116927 rs31443144
2 │ 3.4922 3.4922 1 0.117404 rs6269442
3 │ 3.5112 3.5112 1 0.117424 rs32285189
4 │ 3.6598 3.6598 1 0.117573 rs258367496
5 │ 3.77702 3.77702 1 0.117691 rs32430919
6 │ 3.81227 3.81227 1 0.117727 rs36251697
7 │ 4.43062 4.43062 1 0.118356 rs30658298
8 │ 4.44674 4.44674 1 0.118372 rs51852623
9 │ 4.51871 4.51871 1 0.118447 rs31879829
10 │ 4.77632 4.77632 1 0.118714 rs36742481
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("1427571_at","HC_M2_0606_P","BXDPublish") |> (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