# Work with other organisms

#### Zuguang Gu ( z.gu@dkfz.de )

#### 2023-05-15

## Use TxDb packages

In the Bioconductor annotation ecosystem, there are TxDb.* packages which provide data for Gene Ontology gene sets. The TxDb.* packages supported in rGREAT are:

    library(rGREAT)
    rGREAT:::BIOC_ANNO_PKGS$txdb

    ##  [1] "TxDb.Hsapiens.UCSC.hg18.knownGene"
    ##  [2] "TxDb.Hsapiens.UCSC.hg19.knownGene"
    ##  [3] "TxDb.Hsapiens.UCSC.hg38.knownGene"
    ##  [4] "TxDb.Hsapiens.UCSC.hg38.refGene"
    ##  [5] "TxDb.Mmusculus.UCSC.mm10.knownGene"
    ##  [6] "TxDb.Mmusculus.UCSC.mm10.ensGene"
    ##  [7] "TxDb.Mmusculus.UCSC.mm39.refGene"
    ##  [8] "TxDb.Mmusculus.UCSC.mm9.knownGene"
    ##  [9] "TxDb.Rnorvegicus.UCSC.rn4.ensGene"
    ## [10] "TxDb.Rnorvegicus.UCSC.rn5.refGene"
    ## [11] "TxDb.Rnorvegicus.UCSC.rn6.refGene"
    ## [12] "TxDb.Rnorvegicus.UCSC.rn7.refGene"
    ## [13] "TxDb.Ggallus.UCSC.galGal4.refGene"
    ## [14] "TxDb.Ggallus.UCSC.galGal5.refGene"
    ## [15] "TxDb.Ggallus.UCSC.galGal6.refGene"
    ## [16] "TxDb.Mmulatta.UCSC.rheMac10.refGene"
    ## [17] "TxDb.Mmulatta.UCSC.rheMac3.refGene"
    ## [18] "TxDb.Mmulatta.UCSC.rheMac8.refGene"
    ## [19] "TxDb.Celegans.UCSC.ce11.refGene"
    ## [20] "TxDb.Celegans.UCSC.ce11.ensGene"
    ## [21] "TxDb.Cfamiliaris.UCSC.canFam3.refGene"
    ## [22] "TxDb.Cfamiliaris.UCSC.canFam4.refGene"
    ## [23] "TxDb.Cfamiliaris.UCSC.canFam5.refGene"
    ## [24] "TxDb.Sscrofa.UCSC.susScr11.refGene"
    ## [25] "TxDb.Sscrofa.UCSC.susScr3.refGene"
    ## [26] "TxDb.Scerevisiae.UCSC.sacCer2.sgdGene"
    ## [27] "TxDb.Scerevisiae.UCSC.sacCer3.sgdGene"
    ## [28] "TxDb.Ptroglodytes.UCSC.panTro4.refGene"
    ## [29] "TxDb.Ptroglodytes.UCSC.panTro5.refGene"
    ## [30] "TxDb.Ptroglodytes.UCSC.panTro6.refGene"
    ## [31] "TxDb.Dmelanogaster.UCSC.dm3.ensGene"
    ## [32] "TxDb.Dmelanogaster.UCSC.dm6.ensGene"
    ## [33] "TxDb.Drerio.UCSC.danRer10.refGene"
    ## [34] "TxDb.Drerio.UCSC.danRer11.refGene"
    ## [35] "TxDb.Btaurus.UCSC.bosTau8.refGene"
    ## [36] "TxDb.Btaurus.UCSC.bosTau9.refGene"
    ## [37] "TxDb.Athaliana.BioMart.plantsmart51"
    ## [38] "TxDb.Athaliana.BioMart.plantsmart22"
    ## [39] "TxDb.Athaliana.BioMart.plantsmart25"
    ## [40] "TxDb.Athaliana.BioMart.plantsmart28"

To perform GREAT anlaysis with GO gene sets for other organisms, you can either specify the genome version:

    great(gr, "GO:BP", "galGal6")

or with the full name of the corresponding TxDb package:

    great(gr, "GO:BP", "TxDb.Ggallus.UCSC.galGal6.refGene")

These two are internally the same.

## Use BioMart GO gene sets

You can specify a BioMart dataset (which corresponds to a specific organism), e.g.:

    # Giant panda
    great(gr, "GO:BP", biomart_dataset = "amelanoleuca_gene_ensembl")

Make sure the genome version to be fit with your data.

A full list of supported BioMart datasets (organisms) as well as the genomo versions can be found with the function `BioMartGOGeneSets::supportedOrganisms()`.

## Use MSigDB gene sets

MSigDB contains gene sets only for human, but it can be extended to other organisms by mapping to the homologues genes. The package msigdbr has already mapped genes to many other organisms. A full list of supported organisms in msigdbr can be obtained by：

    library(msigdbr)
    msigdbr_species()

    ## # A tibble: 20 × 2
    ##    species_name                    species_common_name
    ##    <chr>                           <chr>
    ##  1 Anolis carolinensis             Carolina anole, green anole
    ##  2 Bos taurus                      bovine, cattle, cow, dairy cow, domestic cat…
    ##  3 Caenorhabditis elegans          <NA>
    ##  4 Canis lupus familiaris          dog, dogs
    ##  5 Danio rerio                     leopard danio, zebra danio, zebra fish, zebr…
    ##  6 Drosophila melanogaster         fruit fly
    ##  7 Equus caballus                  domestic horse, equine, horse
    ##  8 Felis catus                     cat, cats, domestic cat
    ##  9 Gallus gallus                   bantam, chicken, chickens, Gallus domesticus
    ## 10 Homo sapiens                    human
    ## 11 Macaca mulatta                  rhesus macaque, rhesus macaques, Rhesus monk…
    ## 12 Monodelphis domestica           gray short-tailed opossum
    ## 13 Mus musculus                    house mouse, mouse
    ## 14 Ornithorhynchus anatinus        duck-billed platypus, duckbill platypus, pla…
    ## 15 Pan troglodytes                 chimpanzee
    ## 16 Rattus norvegicus               brown rat, Norway rat, rat, rats
    ## 17 Saccharomyces cerevisiae        baker's yeast, brewer's yeast, S. cerevisiae
    ## 18 Schizosaccharomyces pombe 972h- <NA>
    ## 19 Sus scrofa                      pig, pigs, swine, wild boar
    ## 20 Xenopus tropicalis              tropical clawed frog, western clawed frog

To obtain gene sets for non-human organisms, e.g.:

    h_gene_sets = msigdbr(species = "chimpanzee", category = "H")
    head(h_gene_sets)

    ## # A tibble: 6 × 18
    ##   gs_cat gs_subcat gs_name               gene_symbol entrez_gene ensembl_gene
    ##   <chr>  <chr>     <chr>                 <chr>             <int> <chr>
    ## 1 H      ""        HALLMARK_ADIPOGENESIS ABCA1            464630 ENSPTRG0000002…
    ## 2 H      ""        HALLMARK_ADIPOGENESIS ABCB8            463892 ENSPTRG0000001…
    ## 3 H      ""        HALLMARK_ADIPOGENESIS ACAA2            455414 ENSPTRG0000001…
    ## 4 H      ""        HALLMARK_ADIPOGENESIS ACADL            459914 ENSPTRG0000001…
    ## 5 H      ""        HALLMARK_ADIPOGENESIS ACADM            469356 ENSPTRG0000000…
    ## 6 H      ""        HALLMARK_ADIPOGENESIS ACADS            742921 ENSPTRG0000000…
    ## # ℹ 12 more variables: human_gene_symbol <chr>, human_entrez_gene <int>,
    ## #   human_ensembl_gene <chr>, gs_id <chr>, gs_pmid <chr>, gs_geoid <chr>,
    ## #   gs_exact_source <chr>, gs_url <chr>, gs_description <chr>, taxon_id <int>,
    ## #   ortholog_sources <chr>, num_ortholog_sources <dbl>

If the organism you selected has a corresponding TxDb package available which provides TSS information, you need to make sure the gene sets use Entrez gene ID as the identifier (Most TxDb packages use Entrez ID as primary ID, you can check the variable `rGREAT:::BIOC_ANNO_PKGS`).

    # convert to a list of gene sets
    h_gene_sets = split(h_gene_sets$entrez_gene, h_gene_sets$gs_name)
    h_gene_sets = lapply(h_gene_sets, as.character)  # just to make sure gene IDs are all in character.
    h_gene_sets[1:2]

    ## $HALLMARK_ADIPOGENESIS
    ##   [1] "464630"    "463892"    "455414"    "459914"    "469356"    "742921"
    ##   [7] "454672"    "104003784" "454895"    "451866"    "737339"    "471032"
    ##  [13] "451742"    "737305"    "100615914" "456723"    "107967644" "454362"
    ##  [19] "464334"    "743667"    "741867"    "449586"    "100614256" "741708"
    ##  [25] "459164"    "746692"    "473976"    "452433"    "468889"    "745443"
    ##  [31] "460926"    "455644"    "451116"    "454684"    "744890"    "461229"
    ##  [37] "740513"    "104005232" "463949"    "469319"    "748673"    "450673"
    ##  [43] "468605"    "471455"    "456837"    "464611"    "452659"    "472079"
    ##  [49] "452307"    "454118"    "100616508" "465727"    "742828"    "737945"
    ##  [55] "107976794" "107976794" "746229"    "472893"    "456557"    "457056"
    ##  [61] "747265"    "736777"    "464460"    "451393"    "745691"    "454512"
    ##  [67] "466780"    "463861"    "744984"    "452566"    "457117"    NA
    ##  [73] "747936"    "459360"    "461436"    "464353"    "464074"    "466651"
    ##  [79] "451984"    "456243"    "464255"    "467738"    "466732"    "461244"
    ##  [85] "456929"    "460520"    "450562"    "450738"    "464140"    "459670"
    ##  [91] "452976"    "471703"    "741876"    "471135"    "461424"    "459828"
    ##  [97] "452295"    "460113"    "453565"    "741179"    "747276"    "470423"
    ## [103] "451967"    "450290"    "473975"    "473975"    "460157"    "462946"
    ## [109] "449638"    "738797"    "456076"    "451807"    "464031"    "739986"
    ## [115] "459173"    "460872"    "463484"    "462853"    "739167"    "457477"
    ## [121] "742027"    "746245"    "472764"    "747387"    "744096"    "101057233"
    ## [127] "744811"    "463686"    "744435"    "468748"    "451175"    "460227"
    ## [133] "454744"    "739996"    NA          "450735"    "454478"    "457929"
    ## [139] "738397"    "458602"    "456908"    "451591"    "450310"    "107970333"
    ## [145] "465012"    "463481"    "463481"    "460178"    "470365"    "742092"
    ## [151] "741184"    "459094"    "459374"    "456940"    "745779"    "454531"
    ## [157] "737918"    "107973114" "742100"    "470420"    "468499"    "467657"
    ## [163] "100608935" "462416"    "451281"    "470281"    "470281"    "452359"
    ## [169] "456862"    "456526"    "747462"    "474051"    "456155"    "458647"
    ## [175] "744390"    "455841"    "459096"    "459031"    "450574"    "449637"
    ## [181] "450628"    "470477"    "471247"    "453405"    "739128"    "454681"
    ## [187] "464707"    "470417"    "450933"    "459685"    "460443"    "468406"
    ## [193] "458803"    "467151"    "464550"    "745004"    "451416"    "735808"
    ## [199] "743144"    "460348"    "107974864" "471631"    "741897"    "463489"
    ##
    ## $HALLMARK_ALLOGRAFT_REJECTION
    ##   [1] "454210"    "461523"    "450363"    "100609296" "459646"    "740898"
    ##   [7] "466415"    "450170"    "465345"    "456984"    "744209"    "449497"
    ##  [13] "100608992" "459361"    "741390"    "468208"    "748142"    "473220"
    ##  [19] "748205"    "736543"    "454593"    "747004"    "454579"    "747123"
    ##  [25] "462689"    "460323"    "740071"    "450128"    "469524"    "449512"
    ##  [31] "748272"    "470617"    "451584"    "742330"    "451585"    "450124"
    ##  [37] "100615583" "473802"    "460569"    "745293"    "462191"    "740560"
    ##  [43] "470892"    "470900"    "735755"    "470426"    "460577"    "465021"
    ##  [49] "465607"    "736196"    "457127"    "453745"    "457277"    "738275"
    ##  [55] "471200"    NA          "459634"    "457770"    "469142"    "463415"
    ##  [61] "466216"    "458797"    "453714"    "469204"    "750603"    NA
    ##  [67] "100615835" "740028"    "451695"    "451158"    "471510"    "738331"
    ##  [73] "469584"    "739516"    "465940"    "461906"    "468521"    "457003"
    ##  [79] "472959"    "457020"    "467610"    "461873"    "452825"    "460623"
    ##  [85] "463280"    "746195"    "750725"    "100608816" "449592"    "471979"
    ##  [91] "471977"    "471974"    "462591"    "462540"    "494187"    "450196"
    ##  [97] "474132"    "473965"    "449517"    "747276"    "470077"    "743102"
    ## [103] "472749"    "469657"    "736204"    "460816"    "471723"    "455851"
    ## [109] "449564"    "737808"    "449644"    "739011"    "744277"    "450200"
    ## [115] "461472"    "456370"    "450884"    "470203"    "101059843" "449565"
    ## [121] "454005"    "463288"    "464245"    "745517"    "463371"    "470524"
    ## [127] "462386"    "450927"    "454294"    "454045"    "458607"    "735556"
    ## [133] "464979"    "450156"    "738375"    "456715"    "471734"    "736309"
    ## [139] "744486"    "459682"    "745667"    "472771"    "462888"    "748652"
    ## [145] "449582"    "458294"    "460699"    "459239"    "741196"    "460720"
    ## [151] "100322885" "469743"    "455026"    "740704"    "740477"    "450512"
    ## [157] "453993"    "456276"    "743176"    "748032"    "457607"    "462249"
    ## [163] "464277"    "737451"    "746600"    "737526"    "456065"    "461536"
    ## [169] "107966305" "746721"    "737070"    "459209"    "451169"    "450503"
    ## [175] "461971"    "461023"    "459834"    "100610925" "471978"    "746399"
    ## [181] "746814"    "456060"    "457742"    "451611"    "107971092" "461167"
    ## [187] "471325"    "471374"    "471167"    "494186"    "748737"    "464876"
    ## [193] "741922"    "745141"    "452125"    "453161"    "743187"    "459427"

Now we can perform the local GREAT analysis.

    great(gr, h_gene_sets, "panTro6")

## Use NCBI genomes

NCBI provides genome data for a huge number of organisms. It is quite easy to obtain the GO gene sets for a specific organism with the AnnotationHub package where NCBI is also the data source. Users please go to the documentation of AnnotationHub to find out how to get GO gene sets with it.

It is relatively more difficult to obtain gene coordinates for a specific organism. Here the function `getGenomeDataFromNCBI()` accepts a RefSeq accession number for a given genome assembly and returns information of the genome as well as the genes.

The RefSeq accession number can be obtained from https://www.ncbi.nlm.nih.gov/data-hub/genome/. Here we take dolphin as an example. Its accession number is “GCF_011762595.1” and its genome page is at https://www.ncbi.nlm.nih.gov/data-hub/genome/GCF_011762595.1/. We can get its genes with the command:

    genes = getGenomeDataFromNCBI("GCF_011762595.1", return_granges = TRUE)
    genes

    ## GRanges object with 19081 ranges and 1 metadata column:
    ##           seqnames        ranges strand |     gene_id
    ##              <Rle>     <IRanges>  <Rle> | <character>
    ##       [1]        1 234018-239154      + |   101335869
    ##       [2]        1 241768-255021      + |   101336161
    ##       [3]        1 259970-287776      + |   101336448
    ##       [4]        1 288745-298452      + |   101336738
    ##       [5]        1 325391-327082      - |   101321052
    ##       ...      ...           ...    ... .         ...
    ##   [19077]       MT    9921-10217      + |     7412053
    ##   [19078]       MT   10211-11588      + |     7412054
    ##   [19079]       MT   11790-13610      + |     7412055
    ##   [19080]       MT   13594-14121      - |     7412056
    ##   [19081]       MT   14195-15334      + |     7412057
    ##   -------
    ##   seqinfo: 23 sequences from an unspecified genome

The chromosome length information is also included in `genes`:

    seqlengths(genes)

    ##         1         2         3         4         5         6         7         8
    ## 183742880 176472748 171421769 144183200 137487549 115599616 114015307 108430135
    ##         9        10        11        12        13        14        15        16
    ## 105335178 102812509 102167733  89330967  88485069  88483154  86293990  83508544
    ##        17        18        19        20        21         X        MT
    ##  78442772  78151963  58636127  58467291  35682248 128693445     16388

Now you can send `genes` to `great()` to perform the enrichment analysis.

    great(gr, your_go_gene_sets, tss_source = genes, ...)

The `GRanges` object can only be constructd if the genome is assembled on the “chromosome-level”. If it is not, e.g. on the scaffold or on the contig level. `getGenomeDataFromNCBI()` returns the raw output which includes two data frames, one for the genome, and one for the genes. Let’s still take “GCF_011762595.1” as an example, but set `return_granges` to `FALSE` (which is the default value in the function):

    lt = getGenomeDataFromNCBI("GCF_011762595.1", return_granges = FALSE)
    names(lt)

    ## [1] "genome" "gene"

The first several rows in the two data frames.

    head(lt$genome)

    ##   genbankAccession refseqAccession chrName ucscStyleName    length
    ## 1       CM022274.1     NC_047034.1       1          <NA> 183742880
    ## 2       CM022275.1     NC_047035.1       2          <NA> 176472748
    ## 3       CM022276.1     NC_047036.1       3          <NA> 171421769
    ## 4       CM022277.1     NC_047037.1       4          <NA> 144183200
    ## 5       CM022278.1     NC_047038.1       5          <NA> 137487549
    ## 6       CM022279.1     NC_047039.1       6          <NA> 115599616

    head(lt$gene)

    ##   RefSeq Contig Accession  Start    End Strand EntreZ ID
    ## 1             NC_047034.1 234018 239154      + 101335869
    ## 2             NC_047034.1 241768 255021      + 101336161
    ## 3             NC_047034.1 259970 287776      + 101336448
    ## 4             NC_047034.1 288745 298452      + 101336738
    ## 5             NC_047034.1 325391 327082      - 101321052
    ## 6             NC_047034.1 337571 414483      + 101327235

In the original data from NCBI, genes are associated to contigs, and that is why the first column in `lt$gene` contains RefSeq contig IDs. When the genome is assembled on the chromosome level, the contigs are chromosomes. For some organisms, there is no official chromosome name, then a specific contig ID from `lt$genome` can be used to map to the contig names.

In the following code, we assume in users’ input regions, the Genbank accession IDs are used for contigs, then we need to construct a gene `GRanges` object which also takes Genback accession IDs. This can be easily done as follows:

First we construct a mapping vector between Genbank IDs and RefSeq IDs:

    map = structure(lt$genome$genbankAccession, names = lt$genome$refseqAccession)
    head(map)

    ##  NC_047034.1  NC_047035.1  NC_047036.1  NC_047037.1  NC_047038.1  NC_047039.1
    ## "CM022274.1" "CM022275.1" "CM022276.1" "CM022277.1" "CM022278.1" "CM022279.1"

Next apply `map` on the first column of `lt$gene` and then convert it into a `GRanges` object. Just make sure in the ID convertion, `NA` may be procudes and need to be removed.

    genes = lt$gene
    genes[, 1] = map[ genes[, 1] ]
    genes = genes[!is.na(genes[, 1]), ]
    genes = GRanges(seqnames = genes[, 1], ranges = IRanges(genes[, 2], genes[, 3]),
                    strand = genes[, 4], gene_id = genes[, 5])

In `lt$genome` there is also contig length information. Simply construct it and set it to the `seqlengths` of `genes`.

    sl = structure(lt$genome$length, names = lt$genome$genbankAccession)
    sl = sl[!is.na(names(sl))]
    seqlengths(genes) = sl
    genes

    ## GRanges object with 19068 ranges and 1 metadata column:
    ##             seqnames              ranges strand |     gene_id
    ##                <Rle>           <IRanges>  <Rle> | <character>
    ##       [1] CM022274.1       234018-239154      + |   101335869
    ##       [2] CM022274.1       241768-255021      + |   101336161
    ##       [3] CM022274.1       259970-287776      + |   101336448
    ##       [4] CM022274.1       288745-298452      + |   101336738
    ##       [5] CM022274.1       325391-327082      - |   101321052
    ##       ...        ...                 ...    ... .         ...
    ##   [19064] CM022295.1 127947078-127950172      + |   117310419
    ##   [19065] CM022295.1 127949692-127981307      - |   117310418
    ##   [19066] CM022295.1 127993720-128016426      - |   101335459
    ##   [19067] CM022295.1 128036576-128056592      + |   117310242
    ##   [19068] CM022295.1 128438626-128448418      - |   101333861
    ##   -------
    ##   seqinfo: 22 sequences from an unspecified genome

And similarly, `genes` can be directly sent to enrichment analysis.

    great(gr, your_go_gene_sets, tss_source = genes, ...)

## Use KEGG gene sets

KEGG supports a huge number of organisms. How to obtain the pathway gene sets for an organism has been introduced in the vignette “Work with other geneset databases”. Now the task is to obtain the gene/TSS definitions of that organism. On the KEGG database, each organism has a RefSeq accession ID associated and this ID can be found on the webpage of the organism. For example, the web page of turkey is https://www.genome.jp/kegg-bin/show_organism?org=mgp where the RefSeq assembly access ID is “GCF_000146605.3”. This ID can be used later with `getGenomeDataFromNCBI()` to get the gene coordinates of that genome assembly.

rGREAT has a function `getKEGGGenome()` which automatically extracts the RefSeq accession ID for an organism:

    getKEGGGenome("mgp")

    ## [1] "GCF_000146605.3"

Then, the following code performs GREAT analysis for any organism supported on KEGG, e.g. turkey:

    genes = getGenomeDataFromNCBI(getKEGGGenome("mgp"))
    gene_sets = getKEGGPathways("mgp")

    great(..., gene_sets, genes)

## Self-define TSS and gene sets

Since `great()` allows both self-defined TSS and gene sets, this means `great()` can be independent to organisms. Please refer to the vignette “Analyze with local GREAT” to find out how to manuallly set both TSS and gene sets.