valr overview

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Why valr?

Why another tool set for interval manipulations? There are several other software packages available for genome interval analysis. However, based on our experiences teaching genome analysis, we were motivated to develop a toolset that:

valr can currently be used for analysis of pre-processed data in BED and related formats. We plan to support BAM and VCF files soon via tabix indexes.

Familiar tools, natively in R

The functions in valr have similar names to their BEDtools counterparts, and so will be familiar to users coming from the BEDtools suite. Similar to pybedtools, valr has a terse syntax:


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)
# distance from intergenic snps to nearest gene
nearby <- bed_closest(intergenic, genes)

nearby %>%
  select(starts_with('name'), .overlap, .dist) %>%
  filter(abs(.dist) < 1000)

Input data

valr assigns common column names to facilitate comparisons between tbls. All tbls will have chrom, start, and end columns, and some tbls from multi-column formats will have additional pre-determined column names. See the read_bed() documentation for details.

bed_file <- valr_example("3fields.bed.gz")
read_bed(bed_file) # accepts filepaths or URLs

valr can also operate on BED-like data.frames already constructed in R, provided that columns named chrom, start and end are present. New tbls can also be constructed as either tibbles or base R data.frames.

bed <- tribble(
  ~chrom, ~start,  ~end, 
  "chr1", 1657492, 2657492, 
  "chr2", 2501324, 3094650


Interval coordinates

valr adheres to the BED format which specifies that the start position for an interval is zero based and the end position is one-based. The first position in a chromosome is 0. The end position for a chromosome is one position passed the last base, and is not included in the interval. For example:

# a chromosome 100 basepairs in length
chrom <- tribble(
  ~chrom, ~start, ~end, 
  "chr1", 0,      100


# single basepair intervals
bases <- tribble(
  ~chrom, ~start, ~end, 
  "chr1", 0,      1, # first base of chromosome
  "chr1", 1,      2,  # second base of chromosome
  "chr1", 99,     100 # last base of chromosome


Remote databases

Remote databases can be accessed with db_ucsc() (to access the UCSC Browser) and db_ensembl() (to access Ensembl databases).

# access the `refGene` tbl on the `hg38` assembly.
if(require(RMySQL)) {
  ucsc <- db_ucsc('hg38')
  tbl(ucsc, 'refGene')

Visual documentation

The bed_glyph() tool illustrates the results of operations in valr, similar to those found in the BEDtools documentation. This glyph shows the result of intersecting x and y intervals with bed_intersect():

x <- tribble(
  ~chrom, ~start, ~end,
  'chr1', 25,     50,
  'chr1', 100,    125

y <- tribble(
  ~chrom, ~start, ~end,
  'chr1', 30,     75

bed_glyph(bed_intersect(x, y))

And this glyph illustrates bed_merge():

x <- tribble(
  ~chrom, ~start, ~end,
  'chr1',      1,      50,
  'chr1',      10,     75,
  'chr1',      100,    120


Grouping data

The group_by function in dplyr can be used to perform functions on subsets of single and multiple data_frames. Functions in valr leverage grouping to enable a variety of comparisons. For example, intervals can be grouped by strand to perform comparisons among intervals on the same strand.

x <- tribble(
  ~chrom, ~start, ~end, ~strand,
  'chr1', 1,      100,  '+',
  'chr1', 50,     150,  '+',
  'chr2', 100,    200,  '-'

y <- tribble(
  ~chrom, ~start, ~end, ~strand,
  'chr1', 50,     125,  '+',
  'chr1', 50,     150,  '-',
  'chr2', 50,     150,  '+'

# intersect tbls by strand
x <- group_by(x, strand)
y <- group_by(y, strand)

bed_intersect(x, y)

Comparisons between intervals on opposite strands are done using the flip_strands() function:

x <- group_by(x, strand)

y <- flip_strands(y)
y <- group_by(y, strand)

bed_intersect(x, y)

Both single set (e.g. bed_merge()) and multi set operations will respect groupings in the input intervals.

Column specification

Columns in BEDtools are referred to by position:

# calculate the mean of column 6 for intervals in `b` that overlap with `a`
bedtools map -a a.bed -b b.bed -c 6 -o mean

In valr, columns are referred to by name and can be used in multiple name/value expressions for summaries.

# calculate the mean and variance for a `value` column
bed_map(a, b, .mean = mean(value), .var = var(value))

# report concatenated and max values for merged intervals
bed_merge(a, .concat = concat(value), .max = max(value))

Getting started


This demonstration illustrates how to use valr tools to perform a "meta-analysis" of signals relative to genomic features. Here we to analyze the distribution of histone marks surrounding transcription start sites.

First we load libraries and relevant data.

# `valr_example()` identifies the path of example files
bedfile <- valr_example('genes.hg19.chr22.bed.gz')
genomefile <- valr_example('hg19.chrom.sizes.gz')
bgfile  <- valr_example('')

genes <- read_bed(bedfile, n_fields = 6)
genome <- read_genome(genomefile)
y <- read_bedgraph(bgfile)

Then we generate 1 bp intervals to represent transcription start sites (TSSs). We focus on + strand genes, but - genes are easily accommodated by filtering them and using bed_makewindows() with reversed window numbers.

# generate 1 bp TSS intervals, `+` strand only
tss <- genes %>%
  filter(strand == '+') %>%
  mutate(end = start + 1)

# 1000 bp up and downstream
region_size <- 1000
# 50 bp windows
win_size <- 50

# add slop to the TSS, break into windows and add a group
x <- tss %>%
  bed_slop(genome, both = region_size) %>%


Now we use the .win_id group with bed_map() to calculate a sum by mapping y signals onto the intervals in x. These data are regrouped by .win_id and a summary with mean and sd values is calculated.

# map signals to TSS regions and calculate summary statistics.
res <- bed_map(x, y, win_sum = sum(value, na.rm = TRUE)) %>%
  group_by(.win_id) %>%
  summarize(win_mean = mean(win_sum, na.rm = TRUE),
            win_sd = sd(win_sum, na.rm = TRUE))


Finally, these summary statistics are used to construct a plot that illustrates histone density surrounding TSSs.


x_labels <- seq(-region_size, region_size, by = win_size * 5)
x_breaks <- seq(1, 41, by = 5)

sd_limits <- aes(ymax = win_mean + win_sd, ymin = win_mean - win_sd)

ggplot(res, aes(x = .win_id, y = win_mean)) +
  geom_point() + geom_pointrange(sd_limits) + 
  scale_x_continuous(labels = x_labels, breaks = x_breaks) + 
  xlab('Position (bp from TSS)') + ylab('Signal') + 
  ggtitle('Human H3K4me3 signal near transcription start sites') +


Function names are similar to their their BEDtools counterparts, with some additions.

Data types

Reading data

Transforming single interval sets

Comparing multiple interval sets

Randomizing intervals

Interval statistics


Related work

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valr documentation built on Dec. 11, 2021, 9:57 a.m.