No gene can be mapped when using enrichKEGG

[esaccenti]

I am using the enrichKEGG function, but my code, that was working last week does not work anymore. Also the example in the vignette doesn't work anymore:

data(geneList, package="DOSE") #Vignette example gene_names <- names(geneList)[abs(geneList) > 2] kegg_enrich <- enrichKEGG(gene = gene_names, organism = "hsa", pvalueCutoff= 0.05, qvalueCutoff= 0.2) --> No gene can be mapped.... --> Expected input gene ID: --> return NULL...

[afaranda]

I can confirm, I am experiencing the same issue with a script that I haven't touched for 9 months. The example in the help file for enrichKEGG is returning the same error.

>   data(geneList, package='DOSE')
>   de <- names(geneList)[1:100]
>   yy <- enrichKEGG(de, pvalueCutoff=0.01)
--> No gene can be mapped....
--> Expected input gene ID: 
--> return NULL...
>   head(yy)
NULL

Session Information:

> sessionInfo()
R version 4.2.2 (2022-10-31)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Ventura 13.1

Matrix products: default
LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] grid      stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] ggfortify_0.4.15      qvalue_2.28.0         pheatmap_1.0.12       ggVennDiagram_1.2.2   ROntoTools_2.24.0    
 [6] Rgraphviz_2.40.0      KEGGgraph_1.56.0      KEGGREST_1.36.3       boot_1.3-28.1         graph_1.74.0         
[11] BiocGenerics_0.42.0   clusterProfiler_4.4.4 venn_1.11             ggplot2_3.4.0         openxlsx_4.2.5.1     
[16] dplyr_1.0.10          tibble_3.1.8          tidyr_1.2.1           edgeR_3.38.4          limma_3.52.4         

loaded via a namespace (and not attached):
  [1] fgsea_1.22.0           colorspace_2.0-3       ggtree_3.4.4           XVector_0.36.0        
  [5] aplot_0.1.9            rstudioapi_0.14        farver_2.1.1           graphlayouts_0.8.4    
  [9] ggrepel_0.9.2          bit64_4.0.5            AnnotationDbi_1.58.0   fansi_1.0.3           
 [13] scatterpie_0.1.8       codetools_0.2-18       splines_4.2.2          cachem_1.0.6          
 [17] GOSemSim_2.22.0        polyclip_1.10-4        jsonlite_1.8.4         GO.db_3.15.0          
 [21] png_0.1-8              ggforce_0.4.1          compiler_4.2.2         httr_1.4.4            
 [25] assertthat_0.2.1       Matrix_1.5-3           fastmap_1.1.0          lazyeval_0.2.2        
 [29] cli_3.5.0              tweenr_2.0.2           admisc_0.30            tools_4.2.2           
 [33] igraph_1.3.5           gtable_0.3.1           glue_1.6.2             GenomeInfoDbData_1.2.8
 [37] reshape2_1.4.4         DO.db_2.9              fastmatch_1.1-3        Rcpp_1.0.9            
 [41] enrichplot_1.16.2      Biobase_2.56.0         vctrs_0.5.1            Biostrings_2.64.1     
 [45] ape_5.6-2              nlme_3.1-161           ggraph_2.1.0           stringr_1.5.0         
 [49] lifecycle_1.0.3        XML_3.99-0.13          DOSE_3.22.1            org.Hs.eg.db_3.15.0   
 [53] zlibbioc_1.42.0        MASS_7.3-58.1          scales_1.2.1           tidygraph_1.2.2       
 [57] parallel_4.2.2         RColorBrewer_1.1-3     memoise_2.0.1          gridExtra_2.3         
 [61] downloader_0.4         ggfun_0.0.9            yulab.utils_0.0.6      stringi_1.7.8         
 [65] RSQLite_2.2.20         S4Vectors_0.34.0       tidytree_0.4.2         zip_2.2.2             
 [69] BiocParallel_1.30.4    GenomeInfoDb_1.32.4    rlang_1.0.6            pkgconfig_2.0.3       
 [73] bitops_1.0-7           lattice_0.20-45        purrr_1.0.0            treeio_1.20.2         
 [77] patchwork_1.1.2        shadowtext_0.1.2       bit_4.0.5              tidyselect_1.2.0      
 [81] plyr_1.8.8             magrittr_2.0.3         R6_2.5.1               IRanges_2.30.1        
 [85] generics_0.1.3         DBI_1.1.3              pillar_1.8.1           withr_2.5.0           
 [89] RCurl_1.98-1.9         crayon_1.5.2           utf8_1.2.2             RVenn_1.1.0           
 [93] viridis_0.6.2          locfit_1.5-9.6         data.table_1.14.6      blob_1.2.3            
 [97] digest_0.6.31          gridGraphics_0.5-1     stats4_4.2.2           munsell_0.5.0         
[101] viridisLite_0.4.1      ggplotify_0.1.0       

[afaranda]

Converting the entrez ID's does not seem to help either.

> x <- paste0("hsa:",gcSample[[1]])
> x
  [1] "hsa:4597"   "hsa:7111"   "hsa:5266"   "hsa:2175"   "hsa:755"    "hsa:23046" 
  [7] "hsa:3931"   "hsa:6770"   "hsa:993" . . . . . . . . . . . . the rest of the KEGG ID's

enrichKEGG(
+   x,
+   organism = "hsa",
+   keyType = "kegg",
+   pvalueCutoff = 0.05,
+   pAdjustMethod = "BH",
+   universe,
+   minGSSize = 10,
+   maxGSSize = 500,
+   qvalueCutoff = 0.2,
+   use_internal_data = FALSE
+ )
--> No gene can be mapped....
--> Expected input gene ID: 
--> return NULL...
NULL

This is kinda stinky, I need this figure for my Dissertation which is supposed to go to comittee on Monday evening

I may try and revert to a previous version.

[afaranda]

Upgrading BioConductor to the most current version fixed this for me.

[esaccenti]

Had to reinstall R from scratch to the last version, and also Bioconductor and also all possible packages to the last versions and it worked

[Mengflz]

Same problem. Do I have to upgrade or downgrade the package to a certain version?

[esaccenti]

I updated all and hoped for the best (that other scripts I usevwill keep working)...also update/installed all packages required by the installation of clusterProfiler since I could not be sure of what is critical and what not

[PartickXu]

搞死人的，新版本要最新的DOSE，只能升级整个R，还要重装调整之前所有包的兼容性，真的醉了。

[esaccenti]

所以它看起來

:)

[Hartecky]

I updated Bioconductor, clusterProfiler and DOSE and still got error as mentioned before

enrichKEGG(as.character(GENES.ENTREZ$ENTREZID),
                            organism = "hsa",
                            keyType = "kegg",
                            pvalueCutoff = 0.05,
                            pAdjustMethod = "BH",
                            res.1$GeneSymbol,
                            minGSSize = 10,
                            maxGSSize = 500,
                            qvalueCutoff = 0.05,
                            use_internal_data = FALSE)

--> No gene can be mapped....
--> Expected input gene ID: 
--> return NULL...

DOSE v3.24.2 clusterProfiler 4.6.2

[dppss90008]

I got the same issue, even though the packages are updated to the latest version.

R 4.2.2 DOSE_3.24.2 clusterProfiler_4.6.2

[dppss90008]

FYI I had a quick solution here

1. Update the clusterprofilier to the latest Github version ( the lastest version is 4.7.1.3)

   remotes::install_github("YuLab-SMU/clusterProfiler")

2. Establish a local KEGG database

    # install the packages
    remotes::install_github("YuLab-SMU/createKEGGdb")
    # import the library and create a KEGG database locally 
    library(createKEGGdb)
    species <-c("ath","hsa","mmu", "rno","dre","dme","cel")
    createKEGGdb::create_kegg_db(species)
    # You will get KEGG.db_1.0.tar.gz file in your working directory

3. install the KEGG.db and import it

    install.packages("KEGG.db_1.0.tar.gz", repos=NULL,type="source")
    library(KEGG.db)

4. add use_internal_data=T in your enrichKEGG function

     data(gcSample)
     yy = enrichKEGG(gcSample[[5]], pvalueCutoff=0.01, use_internal_data=T)
     head(summary(yy))

[jordan841220]

@dppss90008 Sweet, this works!!

[kkleinoros]

the problem is the function OrganismMapper. "hsa" should be the input does not need to be mapped.

[yeroslaviz]

Sweet, this workaround did the trick.

Do we need to update everything to make it work without the local DB?

thanks

[HolmesCloud]

I debuged the code step by step, and located the bug:
clusterProfiler::enricher_internal
<=KEGG_DATA <=prepare_KEGG(species, "KEGG", keyType)
<=kegg <- download_KEGG(species, KEGG_Type, keyType)
<=if (use_cached).
when the cache detected, the program used cache instead of downloading the data from website.
the simple way to solve this problem here is to clean the cache, i.e. delete the .RData at worplace and force the program downloads the new data from web.
I reinstalled the clusterProfiler from Bioconductor, and the bug also did not appear.
----
System: MacOS arm, R-4.2.2,

[kkleinoros]

I debuged the code step by step, and located the bug: clusterProfiler::enricher_internal <=KEGG_DATA <=prepare_KEGG(species, "KEGG", keyType) <=kegg <- download_KEGG(species, KEGG_Type, keyType) <=if (use_cached). when the cache detected, the program used cache instead of downloading the data from website. the simple way to solve this problem here is to clean the cache, i.e. delete the .RData at worplace and force the program downloads the new data from web. I reinstalled the clusterProfiler from Bioconductor, and the bug also did not appear. ---- System: MacOS arm, R-4.2.2,

you missed a step see above, that is why it is going to cache.

[nshen7]

Hello, I tried to re-install with remotes::install_github("YuLab-SMU/clusterProfiler") but i got errors from it (copied to down below). Anyone got any advice?

The downloaded source packages are in ‘/tmp/RtmpGtkzzV/downloaded_packages’ ✔ checking for file ‘/tmp/RtmpGtkzzV/remotes378e41ddad11d/YuLab-SMU-clusterProfiler-127278c/DESCRIPTION’ (358ms) ─ preparing ‘clusterProfiler’: ✔ checking DESCRIPTION meta-information ... ─ checking for LF line-endings in source and make files and shell scripts ─ checking for empty or unneeded directories ─ looking to see if a ‘data/datalist’ file should be added ─ building ‘clusterProfiler_4.7.1.003.tar.gz’ Warning in sprintf(gettext(fmt, domain = domain, trim = trim), ...) : one argument not used by format 'invalid uid value replaced by that for user 'nobody'' Warning: invalid uid value replaced by that for user 'nobody' Warning in sprintf(gettext(fmt, domain = domain, trim = trim), ...) : one argument not used by format 'invalid gid value replaced by that for user 'nobody'' Warning: invalid gid value replaced by that for user 'nobody'

Installing package into ‘/home/nshen7/R/rstudio_4_2_0’ (as ‘lib’ is unspecified)

• installing source package ‘clusterProfiler’ ... using staged installation R data inst ** byte-compile and prepare package for lazy loading Error in loadNamespace(j <- i[[1L]], c(lib.loc, .libPaths()), versionCheck = vI[[j]]) : namespace ‘DOSE’ 3.22.1 is being loaded, but >= 3.23.2 is required Calls: ... namespaceImportFrom -> asNamespace -> loadNamespace Execution halted ERROR: lazy loading failed for package ‘clusterProfiler’
• removing ‘/home/nshen7/R/rstudio_4_2_0/clusterProfiler’ Warning message: In i.p(...) : installation of package ‘/tmp/RtmpGtkzzV/file378e46006fe62/clusterProfiler_4.7.1.003.tar.gz’ had non-zero exit status

[esaccenti]

HERE: DOSE’ 3.22.1 is being loaded, but >= 3.23.2 is required

You need to update the DOSE package\install version 3.23.2

O try some of the previously suggested solutions

[undo6411]

FYI I had a quick solution here

1. Update the clusterprofilier to the latest Github version ( the lastest version is 4.7.1.3) remotes::install_github("YuLab-SMU/clusterProfiler")
2. Establish a local KEGG database

   # install the packages
   remotes::install_github("YuLab-SMU/createKEGGdb")
   # import the library and create a KEGG database locally 
   library(createKEGGdb)
   species <-c("ath","hsa","mmu", "rno","dre","dme","cel")
   createKEGGdb::create_kegg_db(species)
   # You will get KEGG.db_1.0.tar.gz file in your working directory

3. install the KEGG.db and import it

   install.packages("KEGG.db_1.0.tar.gz", repos=NULL,type="source")
   library(KEGG.db)

4. add use_internal_data=T in your enrichKEGG function

   data(gcSample)
   yy = enrichKEGG(gcSample[[5]], pvalueCutoff=0.01, use_internal_data=T)
   head(summary(yy))

It's work for me! thank you

[wang-tan]

HERE: DOSE’ 3.22.1 is being loaded, but >= 3.23.2 is required

You need to update the DOSE package\install version 3.23.2

O try some of the previously suggested solutions

Please, how to update the DOSE package to version 3.23.2 ? I don't want to update my R to version 4.2.

[aspa94]

I have the same problem with "no genes mapped" when using the gseKEGG from ClusterProfiler. Everuthing worked fine a month ago

I have tried updating bioconducter to the newest version 3.16. I tired if I could update both "DOSE" and "ClusterProfiler" but that I am not sure went well.

How to check that?

Can someone guide me to a solution (I am relative new in R so it needs to be for dummies :D )

[aspa94]

I have the same problem with "no genes mapped" when using the gseKEGG from ClusterProfiler. Everuthing worked fine a month ago

I have tried updating bioconducter to the newest version 3.16. I tired if I could update both "DOSE" and "ClusterProfiler" but that I am not sure went well.

How to check that?

Can someone guide me to a solution (I am relative new in R so it needs to be for dummies :D )

Solved - I restarted R and then the updates were done and the Code works again

[DavideBrex]

Same problem, but if I use enrichMKEGG it works. What is the difference between the two functions?

[huerqiang]

@DavideBrex Please make sure you are using the latest version of clusterProfiler and please provide your sessioninfo.

[mostafaabuzaid25]

I am facing the same issue also with latest version

[DavideBrex]

@DavideBrex Please make sure you are using the latest version of clusterProfiler and please provide your sessioninfo.

I am using last version. Thank you for the support.

R version 4.2.2 Patched (2022-11-10 r83330)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.5 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=it_IT.UTF-8       
 [4] LC_COLLATE=en_US.UTF-8     LC_MONETARY=it_IT.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=it_IT.UTF-8       LC_NAME=C                  LC_ADDRESS=C              
[10] LC_TELEPHONE=C             LC_MEASUREMENT=it_IT.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats4    stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] openxlsx_4.2.5.1      org.Mm.eg.db_3.16.0   org.Hs.eg.db_3.16.0   AnnotationDbi_1.60.0 
 [5] IRanges_2.32.0        S4Vectors_0.36.0      Biobase_2.58.0        BiocGenerics_0.44.0  
 [9] ReactomePA_1.42.0     clusterProfiler_4.6.2 forcats_0.5.2         stringr_1.4.1        
[13] dplyr_1.0.10          purrr_0.3.5           readr_2.1.3           tidyr_1.2.1          
[17] tibble_3.1.8          ggplot2_3.4.0         tidyverse_1.3.2      

loaded via a namespace (and not attached):
  [1] readxl_1.4.1           shadowtext_0.1.2       backports_1.4.1        fastmatch_1.1-3       
  [5] plyr_1.8.7             igraph_1.3.5           lazyeval_0.2.2         splines_4.2.2         
  [9] BiocParallel_1.32.1    GenomeInfoDb_1.34.2    digest_0.6.30          yulab.utils_0.0.5     
 [13] GOSemSim_2.24.0        viridis_0.6.2          GO.db_3.16.0           fansi_1.0.3           
 [17] magrittr_2.0.3         memoise_2.0.1          googlesheets4_1.0.1    tzdb_0.3.0            
 [21] Biostrings_2.66.0      graphlayouts_0.8.3     modelr_0.1.9           timechange_0.1.1      
 [25] enrichplot_1.18.0      colorspace_2.0-3       blob_1.2.3             rvest_1.0.3           
 [29] rappdirs_0.3.3         ggrepel_0.9.2          haven_2.5.1            crayon_1.5.2          
 [33] RCurl_1.98-1.9         jsonlite_1.8.3         graph_1.76.0           scatterpie_0.1.8      
 [37] ape_5.6-2              glue_1.6.2             polyclip_1.10-4        gtable_0.3.1          
 [41] gargle_1.2.1           zlibbioc_1.44.0        XVector_0.38.0         graphite_1.44.0       
 [45] scales_1.2.1           DOSE_3.24.1            DBI_1.1.3              Rcpp_1.0.9            
 [49] viridisLite_0.4.1      gridGraphics_0.5-1     tidytree_0.4.1         bit_4.0.4             
 [53] reactome.db_1.82.0     httr_1.4.4             fgsea_1.24.0           RColorBrewer_1.1-3    
 [57] ellipsis_0.3.2         pkgconfig_2.0.3        farver_2.1.1           dbplyr_2.2.1          
 [61] utf8_1.2.2             ggplotify_0.1.0        tidyselect_1.2.0       rlang_1.0.6           
 [65] reshape2_1.4.4         munsell_0.5.0          cellranger_1.1.0       tools_4.2.2           
 [69] cachem_1.0.6           downloader_0.4         cli_3.4.1              generics_0.1.3        
 [73] RSQLite_2.2.18         gson_0.0.9             broom_1.0.1            fastmap_1.1.0         
 [77] ggtree_3.6.1           bit64_4.0.5            fs_1.5.2               tidygraph_1.2.2       
 [81] zip_2.2.2              KEGGREST_1.38.0        ggraph_2.1.0           nlme_3.1-160          
 [85] aplot_0.1.8            xml2_1.3.3             compiler_4.2.2         rstudioapi_0.14       
 [89] png_0.1-7              reprex_2.0.2           treeio_1.22.0          tweenr_2.0.2          
 [93] stringi_1.7.8          lattice_0.20-45        Matrix_1.5-1           vctrs_0.5.0           
 [97] pillar_1.8.1           lifecycle_1.0.3        BiocManager_1.30.19    data.table_1.14.4     
[101] cowplot_1.1.1          bitops_1.0-7           patchwork_1.1.2        qvalue_2.30.0         
[105] R6_2.5.1               gridExtra_2.3          codetools_0.2-18       MASS_7.3-58.1         
[109] assertthat_0.2.1       withr_2.5.0            GenomeInfoDbData_1.2.9 parallel_4.2.2        
[113] hms_1.1.2              grid_4.2.2             ggfun_0.0.8            HDO.db_0.99.1         
[117] googledrive_2.0.0      ggforce_0.4.1          lubridate_1.9.0       

[huerqiang]

@DavideBrex From the sessioninfo, I see your DOSE is not the release version, please update it. If you still report an error, you can try using the createKEGGdb: https://github.com/YuLab-SMU/clusterProfiler/issues/561#issuecomment-1467266614

[Wenjuan-ZHU]

@huerqiang @dppss90008

I copied and ran @dppss90008 above scripts. However, I got the below error. I believe there is something wrong with the createKEGGdb code. Can you please check it? Thanks!

library(createKEGGdb) species <- c("ath","hsa","mmu", "rno","dre","dme","cel") createKEGGdb::create_kegg_db(species)

Error in clusterProfiler:::kegg_list("pathway", i) : unused argument (i)

[huerqiang]

@Wenjuan-ZHU createKEGGdb is OK. Please update your clusterProfiler.

[Wenjuan-ZHU]

@huerqiang Thanks! it works after I reload clusterProfiler.

[Youpu-Chen]

FYI I had a quick solution here

1. Update the clusterprofilier to the latest Github version ( the lastest version is 4.7.1.3) remotes::install_github("YuLab-SMU/clusterProfiler")
2. Establish a local KEGG database

   # install the packages
   remotes::install_github("YuLab-SMU/createKEGGdb")
   # import the library and create a KEGG database locally 
   library(createKEGGdb)
   species <-c("ath","hsa","mmu", "rno","dre","dme","cel")
   createKEGGdb::create_kegg_db(species)
   # You will get KEGG.db_1.0.tar.gz file in your working directory

3. install the KEGG.db and import it

   install.packages("KEGG.db_1.0.tar.gz", repos=NULL,type="source")
   library(KEGG.db)

4. add use_internal_data=T in your enrichKEGG function

   data(gcSample)
   yy = enrichKEGG(gcSample[[5]], pvalueCutoff=0.01, use_internal_data=T)
   head(summary(yy))

   Cool, man. It is useful, but I ended up switching the R version, then using the latest clusterProfiler, it wored out.

[BirongZhang]

remotes::install_github("YuLab-SMU/clusterProfiler") 

Got error in the first step.

Execution halted
ERROR: lazy loading failed for package ‘clusterProfiler’
* removing ‘/Library/Frameworks/R.framework/Versions/4.1/Resources/library/clusterProfiler’
* restoring previous ‘/Library/Frameworks/R.framework/Versions/4.1/Resources/library/clusterProfiler’
Warning messages:
1: In readRDS(dest) : lzma decoder corrupt data
2: In i.p(...) :
  installation of package ‘RcppArmadillo’ had non-zero exit status
3: In i.p(...) : installation of package ‘igraph’ had non-zero exit status
4: In i.p(...) :
  installation of package ‘/var/folders/c5/6c_hbb551sq7qx62n_hqz5s40000gs/T//RtmpbNwS2t/filea3410e72417/clusterProfiler_4.9.2.tar.gz’ had non-zero exit status

Switched to this way:

## KEGG pathway
kegg  <- read.table("kegg.csv", sep=",", header=TRUE) # must have "entrezgene_id"
kegg  <- as_tibble(kegg[,2:3])
colnames(kegg) <-  c("gs_name" ,"entrez_gene")
head(kegg)

# kegg gene set enrichment analysis
em_kegg <- GSEA(geneList, TERM2GENE = kegg, pvalueCutof=1)
head(em_kegg)

result_kegg <- em_kegg@result

[fshahi]

Hi, Thanks all re: above as I thought I was losing the plot when I couldn't get gseKEGG to work!

However, I still can't- and this is likely a version issue for me as I am currently in the final write-up phase of my PhD and can't risk updating R beyond the one I'm running (v4.2.1)- therefore I think my clusterProfiler and DOSE packages are as up to date as they can be (clusterProfiler_4.4.4, DOSE_3.22.1).

Running the createKEGGdb::create_kegg_db(species) gives me the same error as Wenjuan-ZHU had.

Is there another work around I can use? I have downloaded the KEGG gmt file from the MSig database. Is there a way to read that in using createKEGGdb?

Thanks in advance.

[Cristinex]

@Wenjuan-ZHU createKEGGdb is OK. Please update your clusterProfiler.

I am using clusterProfiler 4.9.3.2 but still have the same issue: Error in clusterProfiler:::kegg_list("pathway", i) : unused argument (i)

[huerqiang]

@Cristinex Please show your code and seeioninfo.

[jin20020101]

Me SOLVED this problem by doing STEPS BELOW: makesure the error is NOT ABOUT the GENE u used to enrichKEGG function FIRST. 1.CHECK if your clusterProfiler PACKAGE VERSION is VALUABLE for ur R VERSION by USING unloadnamespace command to clusterProfiler package. if is, load it and go next step, if not, REINSTALL it by pointing the link of clusterProfiler packege that fits ur R version, i suggest u to install from github. 2.CHEAK/RELOAD the REFERENCE GENOME data: _use _bitr_kegg(gene, fromType = "kegg", toType = "Path", organism = "yourorganism") to check if the gene set u used is tranformed correctly, if not, check your service net connection is ABLE to connect KEGG DATABASE ONLINE, if no can do, fix and reconnect your net connection first, if u STILL get same ERROR, u have to use parameter _use_internaldata and set it TRUE to use KEGG.db package by using createKEGG solution from Yu LAB to build up ur custom referenced genome. FYI, here is the link https://github.com/YuLab-SMU/createKEGGdb.