Hi all,

I am using clusterProfiler to perform KEGG enrichment. I have a list of gene Symbol (Rat genes). First I need to translate the symbols to EntrezID.

I installed the Genome wide annotation for Rat:

    source("https://bioconductor.org/biocLite.R")
    biocLite("org.Rn.eg.db")

Then I tried to run the command:

    x <- c("GPX3",  "GLRX",   "LBP",   "CRYAB", "DEFB1", "HCLS1",   "SOD2",   "HSPA2",
           "ORM1",  "IGFBP1", "PTHLH", "GPC3",  "IGFBP3","TOB1",    "MITF",   "NDRG1",
           "NR1H4", "FGFR3",  "PVR",   "IL6",   "PTPRM", "ERBB2",   "NID2",   "LAMB1",
           "COMP",  "PLS3",   "MCAM",  "SPP1",  "LAMC1", "COL4A2",  "COL4A1", "MYOC",
           "ANXA4", "TFPI2",  "CST6",  "SLPI",  "TIMP2", "CPM",     "GGT1",   "NNMT",
           "MAL",   "EEF1A2", "HGD",   "TCN2",  "CDA",   "PCCA",    "CRYM",   "PDXK",
           "STC1",  "WARS",  "HMOX1", "FXYD2", "RBP4",   "SLC6A12", "KDELR3", "ITM2B")

    eg = bitr(x, fromType="SYMBOL", toType="ENTREZID", OrgDb="org.Rn.eg.db")

Then I got the warning message:

> 'select()' returned 1:1 mapping between keys and columns Warning
> message: In bitr(x4, fromType = "SYMBOL", toType = "ENTREZID", OrgDb =
> Rat) :
>   98.21% of input gene IDs are fail to map...

Could anyone tell me what happened and how should I do?

Many thanks,
Stanley
Hi Stanley,

Please try something like this, using the biomaRt package:

    require(biomaRt)
    mart <- useMart("ENSEMBL_MART_ENSEMBL")
    mart <- useDataset("hsapiens_gene_ensembl", mart)
    getBM(mart=mart, attributes=c("hgnc_symbol","entrezgene_id"),
      filter="hgnc_symbol", values=x, uniqueRows=TRUE)
       hgnc_symbol entrezgene_id
    1        ANXA4        307
    2          CDA        978
    3       COL4A1       1282
    4       COL4A2       1284
    5         COMP       1311
    6          CPM       1368
    7        CRYAB       1410
    8         CRYM       1428
    9         CST6       1474
    10       DEFB1       1672
    11      EEF1A2       1917
    12       ERBB2       2064
    13       FGFR3       2261
    14       FXYD2        486
    15        GGT1       2678
    16        GGT1     728441
    17        GGT1  102724197
    18        GLRX       2745
    19        GPC3       2719
    20        GPX3       2878
    21       HCLS1       3059
    22         HGD       3081
    23       HMOX1       3162
    24       HSPA2       3306
    25      IGFBP1       3484
    26      IGFBP3       3486
    27         IL6       3569
    28       ITM2B       9445
    29      KDELR3      11015
    30       LAMB1       3912
    31       LAMC1       3915
    32         LBP       3929
    33         MAL       4118
    34        MCAM       4162
    35        MITF       4286
    36        MYOC       4653
    37       NDRG1      10397
    38        NID2      22795
    39        NNMT       4837
    40       NR1H4       9971
    41        ORM1       5004
    42        PCCA       5095
    43        PDXK       8566
    44        PDXK  105372824
    45        PLS3       5358
    46       PTHLH       5744
    47       PTPRM       5797
    48         PVR       5817
    49        RBP4       5950
    50     SLC6A12       6539
    51        SLPI       6590
    52        SOD2       6648
    53        SOD2  100129518
    54        SPP1       6696
    55        STC1       6781
    56        TCN2       6948
    57       TFPI2       7980
    58       TIMP2       7077
    59        TOB1      10140
    60        WARS       7453

Regarding annotation conversions, there are usually one-to-many mappings, which can complicate things Also important to be aware that many genomic loci are still not well annotated, whilst at others transcripts overlap each other (having different promoters and transcription start sites).

Kevin