Annotables: R data package for annotating/converting Gene IDs

November 13, 2015

[This article was first published on Getting Genetics Done, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

I work with gene lists on a nearly daily basis. Lists of genes near ChIP-seq peaks, lists of genes closest to a GWAS hit, lists of differentially expressed genes or transcripts from an RNA-seq experiment, lists of genes involved in certain pathways, etc. And lots of times I’ll need to convert these gene IDs from one identifier to another. There’s no shortage of tools to do this. I use Ensembl Biomart. But I do this so often that I got tired of hammering Ensembl’s servers whenever I wanted to convert from Ensembl to Entrez gene IDs for pathway mapping, get the chromosomal location for some BEDTools-y kinds of genomic arithmetic, or get the gene symbol and full description for reporting. So I used Biomart to retrieve the data that I use most often, cleaned up the column names, and saved this data as an R data package called annotables.
This package has basic annotation information from Ensembl release 82 for:
  • Human (grch38)
  • Mouse (grcm38)
  • Rat (rnor6)
  • Chicken (galgal4)
  • Worm (wbcel235)
  • Fly (bdgp6)
Where each table contains:
  • ensgene: Ensembl gene ID
  • entrez: Entrez gene ID
  • symbol: Gene symbol
  • chr: Chromosome
  • start: Start
  • end: End
  • strand: Strand
  • biotype: Protein coding, pseudogene, mitochondrial tRNA, etc.
  • description: Full gene name/description.
Additionally, there are tables for human and mouse (grch38_gt and grcm38_gt, respectively) that link ensembl gene IDs to ensembl transcript IDs.


The package isn’t on CRAN, so you’ll need devtools to install it.
# If you haven't already installed devtools...

# Use devtools to install the package
It isn’t necessary to load dplyr, but the tables are tbl_df and will print nicely if you have dplyr loaded.
Look at the human genes table (note the description column gets cut off because the table becomes too wide to print nicely):
## Source: local data frame [66,531 x 9]
## ensgene entrez symbol chr start end strand biotype
## (chr) (int) (chr) (chr) (int) (int) (int) (chr)
## 1 ENSG00000210049 NA MT-TF MT 577 647 1 Mt_tRNA
## 2 ENSG00000211459 NA MT-RNR1 MT 648 1601 1 Mt_rRNA
## 3 ENSG00000210077 NA MT-TV MT 1602 1670 1 Mt_tRNA
## 4 ENSG00000210082 NA MT-RNR2 MT 1671 3229 1 Mt_rRNA
## 5 ENSG00000209082 NA MT-TL1 MT 3230 3304 1 Mt_tRNA
## 6 ENSG00000198888 4535 MT-ND1 MT 3307 4262 1 protein_coding
## 7 ENSG00000210100 NA MT-TI MT 4263 4331 1 Mt_tRNA
## 8 ENSG00000210107 NA MT-TQ MT 4329 4400 -1 Mt_tRNA
## 9 ENSG00000210112 NA MT-TM MT 4402 4469 1 Mt_tRNA
## 10 ENSG00000198763 4536 MT-ND2 MT 4470 5511 1 protein_coding
## .. ... ... ... ... ... ... ... ...
## Variables not shown: description (chr)
Look at the human genes-to-transcripts table:
## Source: local data frame [216,133 x 2]
## ensgene enstxp
## (chr) (chr)
## 1 ENSG00000210049 ENST00000387314
## 2 ENSG00000211459 ENST00000389680
## 3 ENSG00000210077 ENST00000387342
## 4 ENSG00000210082 ENST00000387347
## 5 ENSG00000209082 ENST00000386347
## 6 ENSG00000198888 ENST00000361390
## 7 ENSG00000210100 ENST00000387365
## 8 ENSG00000210107 ENST00000387372
## 9 ENSG00000210112 ENST00000387377
## 10 ENSG00000198763 ENST00000361453
## .. ... ...
Tables are tbl_df, pipe-able with dplyr:
grch38 %>% 
filter(biotype=="protein_coding" & chr=="1") %>%
select(ensgene, symbol, chr, start, end, description) %>%
head %>%
pander::pandoc.table(split.table=100, justify="llllll", style="rmarkdown")
ensgene symbol chr start end
ENSG00000158014 SLC30A2 1 26037252 26046133
ENSG00000173673 HES3 1 6244192 6245578
ENSG00000243749 ZMYM6NB 1 34981535 34985353
ENSG00000189410 SH2D5 1 20719732 20732837
ENSG00000116863 ADPRHL2 1 36088875 36093932
ENSG00000188643 S100A16 1 153606886 153613145
Table: Table continues below
solute carrier family 30 (zinc transporter), member 2 [Source:HGNC Symbol;Acc:HGNC:11013]
hes family bHLH transcription factor 3 [Source:HGNC Symbol;Acc:HGNC:26226]
ZMYM6 neighbor [Source:HGNC Symbol;Acc:HGNC:40021]
SH2 domain containing 5 [Source:HGNC Symbol;Acc:HGNC:28819]
ADP-ribosylhydrolase like 2 [Source:HGNC Symbol;Acc:HGNC:21304]
S100 calcium binding protein A16 [Source:HGNC Symbol;Acc:HGNC:20441]

Example with RNA-seq data

Here’s an example with RNA-seq data. Specifically, DESeq2 results from the airway package, made tidy with biobroom:
# Load libraries (install with Bioconductor if you don't have them)

# Load the data and do the RNA-seq data analysis
airway = DESeqDataSet(airway, design = ~cell + dex)
airway = DESeq(airway)
res = results(airway)

# tidy results with biobroom
res_tidy = tidy.DESeqResults(res)
## Source: local data frame [6 x 7]
## gene baseMean estimate stderror statistic
## (chr) (dbl) (dbl) (dbl) (dbl)
## 1 ENSG00000000003 708.6021697 0.37424998 0.09873107 3.7906000
## 2 ENSG00000000005 0.0000000 NA NA NA
## 3 ENSG00000000419 520.2979006 -0.20215550 0.10929899 -1.8495642
## 4 ENSG00000000457 237.1630368 -0.03624826 0.13684258 -0.2648902
## 5 ENSG00000000460 57.9326331 0.08523370 0.24654400 0.3457140
## 6 ENSG00000000938 0.3180984 0.11555962 0.14630523 0.7898530
## Variables not shown: p.value (dbl), p.adjusted (dbl)
Now, make a table with the results (unfortunately, it’ll be split in this display, but you can write this to file to see all the columns in a single row):
res_tidy %>% 
arrange(p.adjusted) %>%
head(20) %>%
inner_join(grch38, by=c("gene"="ensgene")) %>%
select(gene, estimate, p.adjusted, symbol, description) %>%
pander::pandoc.table(split.table=100, justify="lrrll", style="rmarkdown")
gene estimate p.adjusted symbol
ENSG00000152583 -4.316 4.753e-134 SPARCL1
ENSG00000165995 -3.189 1.44e-133 CACNB2
ENSG00000101347 -3.618 6.619e-125 SAMHD1
ENSG00000120129 -2.871 6.619e-125 DUSP1
ENSG00000189221 -3.231 9.468e-119 MAOA
ENSG00000211445 -3.553 3.94e-107 GPX3
ENSG00000157214 -1.949 8.74e-102 STEAP2
ENSG00000162614 -2.003 3.052e-98 NEXN
ENSG00000125148 -2.167 1.783e-92 MT2A
ENSG00000154734 -2.286 4.522e-86 ADAMTS1
ENSG00000139132 -2.181 2.501e-83 FGD4
ENSG00000162493 -1.858 4.215e-83 PDPN
ENSG00000162692 3.453 3.563e-82 VCAM1
ENSG00000179094 -3.044 1.199e-81 PER1
ENSG00000134243 -2.149 2.73e-81 SORT1
ENSG00000163884 -4.079 1.073e-80 KLF15
ENSG00000178695 2.446 6.275e-75 KCTD12
ENSG00000146250 2.64 1.143e-69 PRSS35
ENSG00000198624 -2.784 1.707e-69 CCDC69
ENSG00000148848 1.783 1.762e-69 ADAM12
Table: Table continues below
SPARC-like 1 (hevin) [Source:HGNC Symbol;Acc:HGNC:11220]
calcium channel, voltage-dependent, beta 2 subunit [Source:HGNC Symbol;Acc:HGNC:1402]
SAM domain and HD domain 1 [Source:HGNC Symbol;Acc:HGNC:15925]
dual specificity phosphatase 1 [Source:HGNC Symbol;Acc:HGNC:3064]
monoamine oxidase A [Source:HGNC Symbol;Acc:HGNC:6833]
glutathione peroxidase 3 [Source:HGNC Symbol;Acc:HGNC:4555]
STEAP family member 2, metalloreductase [Source:HGNC Symbol;Acc:HGNC:17885]
nexilin (F actin binding protein) [Source:HGNC Symbol;Acc:HGNC:29557]
metallothionein 2A [Source:HGNC Symbol;Acc:HGNC:7406]
ADAM metallopeptidase with thrombospondin type 1 motif, 1 [Source:HGNC Symbol;Acc:HGNC:217]
FYVE, RhoGEF and PH domain containing 4 [Source:HGNC Symbol;Acc:HGNC:19125]
podoplanin [Source:HGNC Symbol;Acc:HGNC:29602]
vascular cell adhesion molecule 1 [Source:HGNC Symbol;Acc:HGNC:12663]
period circadian clock 1 [Source:HGNC Symbol;Acc:HGNC:8845]
sortilin 1 [Source:HGNC Symbol;Acc:HGNC:11186]
Kruppel-like factor 15 [Source:HGNC Symbol;Acc:HGNC:14536]
potassium channel tetramerization domain containing 12 [Source:HGNC Symbol;Acc:HGNC:14678]
protease, serine, 35 [Source:HGNC Symbol;Acc:HGNC:21387]
coiled-coil domain containing 69 [Source:HGNC Symbol;Acc:HGNC:24487]
ADAM metallopeptidase domain 12 [Source:HGNC Symbol;Acc:HGNC:190]


This data can also be used for toying around with dplyr verbs and generally getting a sense of what’s in here. First, tet some help.
Let’s join the transcript table to the gene table.
gt = grch38_gt %>% 
inner_join(grch38, by="ensgene")
Now, let’s filter to get only protein-coding genes, group by the ensembl gene ID, summarize to count how many transcripts are in each gene, inner join that result back to the original gene list, so we can select out only the gene, number of transcripts, symbol, and description, mutate the description column so that it isn’t so wide that it’ll break the display, arrange the returned data descending by the number of transcripts per gene, head to get the top 10 results, and optionally, pipe that to further utilities to output a nice HTML table.
gt %>% 
filter(biotype=="protein_coding") %>%
group_by(ensgene) %>%
summarize(ntxps=n_distinct(enstxp)) %>%
inner_join(grch38, by="ensgene") %>%
select(ensgene, ntxps, symbol, description) %>%
mutate(description=substr(description, 1, 20)) %>%
arrange(desc(ntxps)) %>%
head(10) %>%
pander::pandoc.table(split.table=100, justify="lrll", style="rmarkdown")
ensgene ntxps symbol description
ENSG00000165795 77 NDRG2 NDRG family member 2
ENSG00000205336 77 ADGRG1 adhesion G protein-c
ENSG00000196628 75 TCF4 transcription factor
ENSG00000161249 68 DMKN dermokine [Source:HG
ENSG00000154556 64 SORBS2 sorbin and SH3 domai
ENSG00000166444 62 ST5 suppression of tumor
ENSG00000204580 58 DDR1 discoidin domain rec
ENSG00000087460 57 GNAS GNAS complex locus [
ENSG00000169398 57 PTK2 protein tyrosine kin
ENSG00000104529 56 EEF1D eukaryotic translati
Let’s look up DMKN (dermkine) in Ensembl. Search Ensembl for ENSG00000161249, or use this direct link. You can browse the table or graphic to see the splicing complexity in this gene.
Or, let’s do something different. Let’s group the data by what type of gene it is (e.g., protein coding, pseudogene, etc), get the number of genes in each category, and plot the top 20.
grch38 %>%
group_by(biotype) %>%
summarize(n=n_distinct(ensgene)) %>%
arrange(desc(n)) %>%
head(20) %>%
ggplot(aes(reorder(biotype, n), n)) +
geom_bar(stat="identity") +
xlab("Type") +
theme_bw() +

To leave a comment for the author, please follow the link and comment on their blog: Getting Genetics Done. offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

If you got this far, why not subscribe for updates from the site? Choose your flavor: e-mail, twitter, RSS, or facebook...

Comments are closed.

Search R-bloggers


Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)