annotables

Provides tables for converting and annotating Ensembl Gene IDs.

Installation

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 109 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: 68,336 × 9
##    ensgene         entrez symbol   chr       start       end strand biotype        description                                                
##    <chr>            <int> <chr>    <chr>     <int>     <int>  <int> <chr>          <chr>                                                      
##  1 ENSG00000000003   7105 TSPAN6   X     100627108 100639991     -1 protein_coding tetraspanin 6                                              
##  2 ENSG00000000005  64102 TNMD     X     100584936 100599885      1 protein_coding tenomodulin                                                
##  3 ENSG00000000419   8813 DPM1     20     50934867  50959140     -1 protein_coding dolichyl-phosphate mannosyltransferase subunit 1, catalytic
##  4 ENSG00000000457  57147 SCYL3    1     169849631 169894267     -1 protein_coding SCY1 like pseudokinase 3                                   
##  5 ENSG00000000460  55732 C1orf112 1     169662007 169854080      1 protein_coding chromosome 1 open reading frame 112                        
##  6 ENSG00000000938   2268 FGR      1      27612064  27635185     -1 protein_coding FGR proto-oncogene, Src family tyrosine kinase             
##  7 ENSG00000000971   3075 CFH      1     196652043 196747504      1 protein_coding complement factor H                                        
##  8 ENSG00000001036   2519 FUCA2    6     143494812 143511720     -1 protein_coding alpha-L-fucosidase 2                                       
##  9 ENSG00000001084   2729 GCLC     6      53497341  53616970     -1 protein_coding glutamate-cysteine ligase catalytic subunit                
## 10 ENSG00000001167   4800 NFYA     6      41072974  41102403      1 protein_coding nuclear transcription factor Y subunit alpha               
## # … with 68,326 more rows

Look at the human genes-to-transcripts table:

grch38_tx2gene

## # A tibble: 266,615 × 2
##    enstxp          ensgene        
##    <chr>           <chr>          
##  1 ENST00000373020 ENSG00000000003
##  2 ENST00000612152 ENSG00000000003
##  3 ENST00000614008 ENSG00000000003
##  4 ENST00000496771 ENSG00000000003
##  5 ENST00000494424 ENSG00000000003
##  6 ENST00000373031 ENSG00000000005
##  7 ENST00000485971 ENSG00000000005
##  8 ENST00000466152 ENSG00000000419
##  9 ENST00000371582 ENSG00000000419
## 10 ENST00000683048 ENSG00000000419
## # … with 266,605 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)

## estimating size factors

## estimating dispersions

## gene-wise dispersion estimates

## mean-dispersion relationship

## final dispersion estimates

## fitting model and testing

res <- results(airway)

# tidy results with biobroom
library(biobroom)

## Loading required package: broom

## Registered S3 methods overwritten by 'biobroom':
##   method      from 
##   glance.list broom
##   tidy.list   broom

res_tidy <- tidy.DESeqResults(res)

## Warning: `tbl_df()` was deprecated in dplyr 1.0.0.
## ℹ Please use `tibble::as_tibble()` instead.
## ℹ The deprecated feature was likely used in the biobroom package.
##   Please report the issue at <]8;;https://github.com/StoreyLab/biobroom/issueshttps://github.com/StoreyLab/biobroom/issues]8;;>.

head(res_tidy)

## # A tibble: 6 × 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   0.000152    0.00128
## 2 ENSG00000000005    0      NA        NA        NA     NA          NA      
## 3 ENSG00000000419  520.     -0.207     0.112    -1.84   0.0653      0.197  
## 4 ENSG00000000457  237.     -0.0379    0.143    -0.264  0.792       0.911  
## 5 ENSG00000000460   57.9     0.0882    0.287     0.307  0.759       0.895  
## 6 ENSG00000000938    0.318   1.38      3.50      0.394  0.694      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(.)