annotables

Tables for annotating gene lists and converting between identifiers.

Why?

Many bioinformatics tasks require converting gene identifiers from one convention to another, or annotating gene identifiers with gene symbol, description, position, etc. Sure, biomaRt dos this for you, but I got tired of remembering biomaRt syntax and hammering Ensembl's servers every time I needed to do this.

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.

Usage

Installation:

install.packages("devtools")
devtools::install_github("stephenturner/annotables")

It isn't necessary to load dplyr, but the tables are tbl_df and will print nicely if you have dplyr loaded.

library(dplyr)
library(annotables)

Look at the human genes table (note the description column gets cut off because the table becomes too wide to print nicely):

grch38

## 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:

grch38_gt

## 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

description
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 DESeq2 results from the airway package, made tidy with biobroom:

library(DESeq2)
library(airway)

data(airway)
airway <- DESeqDataSet(airway, design = ~cell + dex)
airway <- DESeq(airway)
res <- results(airway)

# tidy results with biobroom
library(biobroom)
res_tidy <- tidy.DESeqResults(res)
head(res_tidy)

## 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)

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	4.624e-134	SPARCL1
ENSG00000165995	-3.189	1.402e-133	CACNB2
ENSG00000101347	-3.618	6.44e-125	SAMHD1
ENSG00000120129	-2.871	6.44e-125	DUSP1
ENSG00000189221	-3.231	9.212e-119	MAOA
ENSG00000211445	-3.553	3.834e-107	GPX3
ENSG00000157214	-1.949	8.503e-102	STEAP2
ENSG00000162614	-2.003	2.969e-98	NEXN
ENSG00000125148	-2.167	1.735e-92	MT2A
ENSG00000154734	-2.286	4.4e-86	ADAMTS1
ENSG00000139132	-2.181	2.434e-83	FGD4
ENSG00000162493	-1.858	4.101e-83	PDPN
ENSG00000162692	3.453	3.467e-82	VCAM1
ENSG00000179094	-3.044	1.167e-81	PER1
ENSG00000134243	-2.149	2.657e-81	SORT1
ENSG00000163884	-4.079	1.044e-80	KLF15
ENSG00000178695	2.446	6.106e-75	KCTD12
ENSG00000146250	2.64	1.112e-69	PRSS35
ENSG00000198624	-2.784	1.661e-69	CCDC69
ENSG00000148848	1.783	1.714e-69	ADAM12

Table: Table continues below

description
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]

How?

All the datasets here were collected using biomaRt. The code is below. It should be fairly easy to add new organisms.

library(biomaRt)
library(dplyr)

fix_genes <- . %>% 
  tbl_df %>% 
  distinct %>% 
  rename(ensgene=ensembl_gene_id,
         entrez=entrezgene,
         symbol=external_gene_name,
         chr=chromosome_name,
         start=start_position,
         end=end_position,
         biotype=gene_biotype)

myattributes <- c("ensembl_gene_id",
                  "entrezgene",
                  "external_gene_name",
                  "chromosome_name",
                  "start_position",
                  "end_position",
                  "strand",
                  "gene_biotype",
                  "description")

# Human
grch38 <- useMart("ensembl") %>% 
  useDataset(mart=., dataset="hsapiens_gene_ensembl") %>% 
  getBM(mart=., attributes=myattributes) %>% 
  fix_genes

# Mouse
grcm38 <- useMart("ensembl") %>% 
  useDataset(mart=., dataset="mmusculus_gene_ensembl") %>% 
  getBM(mart=., attributes=myattributes) %>% 
  fix_genes

# Rat
rnor6 <- useMart("ensembl") %>% 
  useDataset(mart=., dataset="rnorvegicus_gene_ensembl") %>% 
  getBM(mart=., attributes=myattributes) %>% 
  fix_genes

# Chicken
galgal4 <- useMart("ensembl") %>% 
  useDataset(mart=., dataset="ggallus_gene_ensembl") %>% 
  getBM(mart=., attributes=myattributes) %>% 
  fix_genes

# Fly
bdgp6 <- useMart("ensembl") %>% 
  useDataset(mart=., dataset="dmelanogaster_gene_ensembl") %>% 
  getBM(mart=., attributes=myattributes) %>% 
  fix_genes

# Worm
wbcel235 <- useMart("ensembl") %>% 
  useDataset(mart=., dataset="celegans_gene_ensembl") %>% 
  getBM(mart=., attributes=myattributes) %>% 
  fix_genes

fix_txps <- . %>% 
  tbl_df %>% 
  distinct %>% 
  rename(ensgene=ensembl_gene_id,
         enstxp=ensembl_transcript_id)

grch38_gt <- useMart("ensembl") %>% 
  useDataset(mart=., dataset="hsapiens_gene_ensembl") %>% 
  getBM(mart=., attributes=c("ensembl_gene_id", "ensembl_transcript_id")) %>% 
  fix_txps

grcm38_gt <- useMart("ensembl") %>% 
  useDataset(mart=., dataset="mmusculus_gene_ensembl") %>% 
  getBM(mart=., attributes=c("ensembl_gene_id", "ensembl_transcript_id"))

rm(fix_genes, fix_txps, myattributes)
devtools::use_data(grch38)
devtools::use_data(grcm38)
devtools::use_data(rnor6)
devtools::use_data(galgal4)
devtools::use_data(bdgp6)
devtools::use_data(wbcel235)
devtools::use_data(grch38_gt)
devtools::use_data(grcm38_gt)

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annotables

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