bitr_kegg

clusterProfiler can convert biological IDs using OrgDb object via the bitr function. Now I implemented another function, bitr_kegg for converting IDs through KEGG API.

library(clusterProfiler)
data(gcSample)
hg <- gcSample[[1]]
head(hg)

## [1] "4597"  "7111"  "5266"  "2175"  "755"   "23046"

eg2np <- bitr_kegg(hg, fromType='kegg', toType='ncbi-proteinid', organism='hsa')

## Warning in bitr_kegg(hg, fromType = "kegg", toType = "ncbi-proteinid",
## organism = "hsa"): 3.7% of input gene IDs are fail to map...

head(eg2np)

##     kegg ncbi-proteinid
## 1   8326      NP_003499
## 2  58487   NP_001034707
## 3 139081      NP_619647
## 4  59272      NP_068576
## 5    993      NP_001780
## 6   2676      NP_001487

np2up <- bitr_kegg(eg2np[,2], fromType='ncbi-proteinid', toType='uniprot', organism='hsa')

head(np2up)

##   ncbi-proteinid uniprot
## 1      NP_005457  O75586
## 2      NP_005792  P41567
## 3      NP_005792  Q6IAV3
## 4      NP_037536  Q13421
## 5      NP_006054  O60662
## 6   NP_001092002  O95398

The ID type (both fromType & toType) should be one of ‘kegg’, ‘ncbi-geneid’, ‘ncbi-proteinid’ or ‘uniprot’. The ‘kegg’ is the primary ID used in KEGG database. The data source of KEGG was from NCBI. A rule of thumb for the ‘kegg’ ID is entrezgene ID for eukaryote species and Locus ID for prokaryotes.

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KEGG MODULE is a collection of manually defined functional units, called KEGG modules and identified by the M numbers, used for annotation and biological interpretation of sequenced genomes. There are four types of KEGG modules:

• pathway modules – representing tight functional units in KEGG metabolic pathway maps, such as M00002 (Glycolysis, core module involving three-carbon compounds)
• structural complexes – often forming molecular machineries, such as M00072 (Oligosaccharyltransferase)
• functional sets – for other types of essential sets, such as M00360 (Aminoacyl-tRNA synthases, prokaryotes)
• signature modules – as markers of phenotypes, such as M00363 (EHEC pathogenicity signature, Shiga toxin)

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KEGG.db is not updated since 2012. The data is now pretty old, but many of the Bioconductor packages still using it for KEGG annotation and enrichment analysis. As pointed out in ‘Are there too many biological databases’, there is a problem that many out of date biological databases often don’t get offline. This issue also exists in web-server or software that using out-of-date data. For example, the WEGO web-server stopped updating GO annotation data since 2009, and WEGO still online with many people using it. The biological story may changed totally if using a recently updated data. Seriously, We should keep an eye on this issue.

Now enrichKEGG function is reloaded with a new parameter use_internal_data. This parameter is by default setting to FALSE, and enrichKEGG function will download the latest KEGG data for enrichment analysis. If the parameter use_internal_data is explicitly setting to TRUE, it will use the KEGG.db which is still supported but not recommended. With this new feature, supported species is unlimited if only there are KEGG annotations available in KEGG database. You can access the full list of species supported by KEGG via: http://www.genome.jp/kegg/catalog/org_list.html Now the organism parameter in enrichKEGG should be abbreviation of academic name, for example ‘hsa’ for human and ‘mmu’ for mouse. It accepts any species listed in http://www.genome.jp/kegg/catalog/org_list.html. In the current release version of clusterProfiler (in Bioconductor 3.0), enrichKEGG supports about 20 species, and the organism parameter accept common name of species, for instance “human” and “mouse”. For these previously supported species, common name is also supported. So that you script is still working with new version of clusterProfiler. For other species, common name is not supported, since I don’t want to maintain such a long mapping list with many species have no common name available and it may also introduce unexpected bugs.

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