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 90 for:

• Human build 38 (grch38)
• Human build 37 (grch37)
• Mouse (grcm38)
• Rat (rnor6)
• Chicken (galgal5)
• 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 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: 64,366 x 9
##            ensgene entrez   symbol   chr     start       end strand
##              <chr>  <int>    <chr> <chr>     <int>     <int>  <int>
##  1 ENSG00000000003   7105   TSPAN6     X 100627109 100639991     -1
##  2 ENSG00000000005  64102     TNMD     X 100584802 100599885      1
##  3 ENSG00000000419   8813     DPM1    20  50934867  50958555     -1
##  4 ENSG00000000457  57147    SCYL3     1 169849631 169894267     -1
##  5 ENSG00000000460  55732 C1orf112     1 169662007 169854080      1
##  6 ENSG00000000938   2268      FGR     1  27612064  27635277     -1
##  7 ENSG00000000971   3075      CFH     1 196651878 196747504      1
##  8 ENSG00000001036   2519    FUCA2     6 143494811 143511690     -1
##  9 ENSG00000001084   2729     GCLC     6  53497341  53616970     -1
## 10 ENSG00000001167   4800     NFYA     6  41072945  41099976      1
## # ... with 64,356 more rows, and 2 more variables: biotype <chr>,
## #   description <chr>

Look at the human genes-to-transcripts table:

grch38_tx2gene

## # A tibble: 218,207 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 218,197 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
##             <chr>       <dbl>       <dbl>     <dbl>      <dbl>
## 1 ENSG00000000003 708.6021697  0.38125397 0.1006560  3.7876937
## 2 ENSG00000000005   0.0000000          NA        NA         NA
## 3 ENSG00000000419 520.2979006 -0.20681259 0.1122218 -1.8428915
## 4 ENSG00000000457 237.1630368 -0.03792034 0.1434532 -0.2643394
## 5 ENSG00000000460  57.9326331  0.08816367 0.2871677  0.3070111
## 6 ENSG00000000938   0.3180984  1.37822703 3.4998728  0.3937935
## # ... with 2 more variables: p.value <dbl>, p.adjusted <dbl>

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(.)