The valr package provides tools to read and manipulate genome
intervals and signals, similar to the BEDtools suite.
valr enables analysis in the R/RStudio environment, leveraging modern R
tools in the tidyverse for a
terse, expressive syntax. Compute-intensive algorithms are implemented
in Rcpp/C++, and many methods take
advantage of the speed and grouping capability provided by dplyr. See
vignette(valr) for more details.
The latest stable version can be installed from CRAN:
install.packages('valr')The latest development version can be installed from github:
# install.packages("devtools")
devtools::install_github('rnabioco/valr')Functions in valr have similar names to their BEDtools counterparts, and so will be familiar to users coming from the BEDtools suite. Unlike other tools that wrap BEDtools and write temporary files to disk, valr tools run natively in memory. Similar to pybedtools, valr has a terse syntax:
library(valr)
library(dplyr)
snps <- read_bed(valr_example('hg19.snps147.chr22.bed.gz'), n_fields = 6)
genes <- read_bed(valr_example('genes.hg19.chr22.bed.gz'), n_fields = 6)
# find snps in intergenic regions
intergenic <- bed_subtract(snps, genes)
# find distance from intergenic snps to nearest gene
nearby <- bed_closest(intergenic, genes)
nearby %>%
select(starts_with('name'), .overlap, .dist) %>%
filter(abs(.dist) < 5000)
#> # A tibble: 1,047 × 4
#> name.x name.y .overlap .dist
#> <chr> <chr> <int> <int>
#> 1 rs530458610 P704P 0 2579
#> 2 rs2261631 P704P 0 -268
#> 3 rs570770556 POTEH 0 -913
#> 4 rs538163832 POTEH 0 -953
#> 5 rs190224195 POTEH 0 -1399
#> 6 rs2379966 DQ571479 0 4750
#> 7 rs142687051 DQ571479 0 3558
#> 8 rs528403095 DQ571479 0 3309
#> 9 rs555126291 DQ571479 0 2745
#> 10 rs5747567 DQ571479 0 -1778
#> # … with 1,037 more rows
#> # ℹ Use `print(n = ...)` to see more rows