annotables

Provides tables for converting and annotating Ensembl Gene IDs.

Installation

This is an R package.

Bioconductor method

source("https://bioconductor.org/biocLite.R")
biocLite("stephenturner/annotables")

devtools method

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

Rationale

Many bioinformatics tasks require converting gene identifiers from one convention to another, or annotating gene identifiers with gene symbol, description, position, etc. Sure, biomaRt does 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 Genes 91 for:

• Human build 38 (grch38)
• Human build 37 (grch37)
• Mouse (grcm38)
• Rat (rnor6)
• Chicken (galgal5)
• Worm (wbcel235)
• Fly (bdgp6)
• Macaque (mmul801)

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 tx2gene tables that link Ensembl gene IDs to Ensembl transcript IDs.

Usage

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

## # A tibble: 64,428 x 9
##    ensgene  entrez symbol chr    start    end strand biotype description  
##    <chr>     <int> <chr>  <chr>  <int>  <int>  <int> <chr>   <chr>        
##  1 ENSG000…   7105 TSPAN6 X     1.01e⁸ 1.01e⁸     -1 protei… tetraspanin …
##  2 ENSG000…  64102 TNMD   X     1.01e⁸ 1.01e⁸      1 protei… tenomodulin …
##  3 ENSG000…   8813 DPM1   20    5.09e⁷ 5.10e⁷     -1 protei… dolichyl-pho…
##  4 ENSG000…  57147 SCYL3  1     1.70e⁸ 1.70e⁸     -1 protei… SCY1 like ps…
##  5 ENSG000…  55732 C1orf… 1     1.70e⁸ 1.70e⁸      1 protei… chromosome 1…
##  6 ENSG000…   2268 FGR    1     2.76e⁷ 2.76e⁷     -1 protei… FGR proto-on…
##  7 ENSG000…   3075 CFH    1     1.97e⁸ 1.97e⁸      1 protei… complement f…
##  8 ENSG000…   2519 FUCA2  6     1.43e⁸ 1.44e⁸     -1 protei… fucosidase, …
##  9 ENSG000…   2729 GCLC   6     5.35e⁷ 5.36e⁷     -1 protei… glutamate-cy…
## 10 ENSG000…   4800 NFYA   6     4.11e⁷ 4.11e⁷      1 protei… nuclear tran…
## # ... with 64,418 more rows

Look at the human genes-to-transcripts table:

grch38_tx2gene

## # A tibble: 219,288 x 2
##    enstxp          ensgene        
##    <chr>           <chr>          
##  1 ENST00000373020 ENSG00000000003
##  2 ENST00000496771 ENSG00000000003
##  3 ENST00000494424 ENSG00000000003
##  4 ENST00000612152 ENSG00000000003
##  5 ENST00000614008 ENSG00000000003
##  6 ENST00000373031 ENSG00000000005
##  7 ENST00000485971 ENSG00000000005
##  8 ENST00000371588 ENSG00000000419
##  9 ENST00000466152 ENSG00000000419
## 10 ENST00000371582 ENSG00000000419
## # ... with 219,278 more rows

Tables are saved in tibble format, pipe-able with dplyr:

grch38 %>% 
    dplyr::filter(biotype == "protein_coding" & chr == "1") %>% 
    dplyr::select(ensgene, symbol, chr, start, end, description) %>% 
    head %>% 
    knitr::kable(.)

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)

## # A tibble: 6 x 7
##   gene            baseMean estimate stderror statistic  p.value p.adjusted
##   <chr>              <dbl>    <dbl>    <dbl>     <dbl>    <dbl>      <dbl>
## 1 ENSG00000000003  709       0.381     0.101     3.79   1.52e⁻⁴    0.00128
## 2 ENSG00000000005    0      NA        NA        NA     NA         NA      
## 3 ENSG00000000419  520     - 0.207     0.112   - 1.84   6.53e⁻²    0.197  
## 4 ENSG00000000457  237     - 0.0379    0.143   - 0.264  7.92e⁻¹    0.911  
## 5 ENSG00000000460   57.9     0.0882    0.287     0.307  7.59e⁻¹    0.895  
## 6 ENSG00000000938    0.318   1.38      3.50      0.394  6.94e⁻¹   NA

res_tidy %>% 
    dplyr::arrange(p.adjusted) %>% 
    head(20) %>% 
    dplyr::inner_join(grch38, by = c("gene" = "ensgene")) %>% 
    dplyr::select(gene, estimate, p.adjusted, symbol, description) %>% 
    knitr::kable(.)