valr: Reproducible genome interval analysis in R

Implementation

valr is an R package that makes extensive use of dplyr, a flexible and high-performance framework for data manipulation in R⁷. Additionally, compute intensive functions in valr are written in C++ using Rcpp to enable fluid interactive analysis of large datasets⁸. Interval intersections and related operations use an interval tree algorithm to efficiently search for overlapping intervals⁹. BED files are imported and handled in R as data_frame objects, requiring minimal pre or post-processing to integrate with additional R packages or command-line tools.

Operation

valr is distributed as part of the CRAN R package repository and is compatible with Mac OS X, Windows, and major Linux operating systems. Package dependencies and system requirements are documented in the valr CRAN repository.

Basic usage

Input data. valr provides a set of functions to read BED, BEDgraph, and VCF formats into R as convenient tibble (tbl) data_frame objects. All tbls have chrom, start, and end columns, and tbls from multi-column formats have additional pre-determined column names. Standards methods for importing data (e.g. read.table, readr::read_tsv) are also supported provided the constructed dataframes contain the requisite column names (chrom, start, end). Additionally, valr supports connections to remote databases to access the UCSC and Ensembl databases via the db_ucsc and db_ensembl functions.

library(valr)
# function to retrieve path to example data
bed_filepath <- valr_example("3fields.bed.gz")
read_bed(bed_filepath)
#> # A tibble: 10 x 3
#>    chrom  start    end
#>    <chr>  <int>  <int>
#>  1  chr1  11873  14409
#>  2  chr1  14361  19759
#>  3  chr1  14406  29370
#>  4  chr1  34610  36081
#>  5  chr1  69090  70008
#>  6  chr1 134772 140566
#>  7  chr1 321083 321115
#>  8  chr1 321145 321207
#>  9  chr1 322036 326938
#> 10  chr1 327545 328439

#using URL
read_bed("https://github.com/rnabioco/valr/raw/master/inst/extdata/3fields.bed.gz")
#> # A tibble: 10 x 3
#>    chrom  start    end
#>    <chr>  <int>  <int>
#>  1  chr1  11873  14409
#>  2  chr1  14361  19759
#>  3  chr1  14406  29370
#>  4  chr1  34610  36081
#>  5  chr1  69090  70008
#>  6  chr1 134772 140566
#>  7  chr1 321083 321115
#>  8  chr1 321145 321207
#>  9  chr1 322036 326938
#> 10  chr1 327545 328439

Example of combining valr tools. The functions in valr have similar names to their BEDtools counterparts, and so will be familiar to users of the BEDtools suite. Also, similar to pybedtools¹⁰, a python wrapper for BEDtools, valr has a terse syntax. For example, shown below is a demonstration of how to find all intergenic SNPs within 1 kilobase of genes using valr. The BED files used in the following examples are described in the Data Availability section.

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

nearby %>%
  select(starts_with("name"), .overlap, .dist) %>%
  filter(abs(.dist) < 1000)
#> # A tibble: 285 x 4
#>         name.x            name.y .overlap .dist
#>          <chr>             <chr>    <int> <int>
#>  1   rs2261631             P704P        0  -267
#>  2 rs570770556             POTEH        0  -912
#>  3 rs538163832             POTEH        0  -952
#>  4   rs9606135            TPTEP1        0  -421
#>  5  rs11912392 ANKRD62P1-PARP4P3        0   104
#>  6   rs8136454          BC038197        0   355
#>  7   rs5992556              XKR3        0  -455
#>  8 rs114101676              GAB4        0   473
#>  9  rs62236167             CECR7        0   261
#> 10   rs5747023             CECR1        0  -386
#> # ... with 275 more rows

Visual documentation. By conducting interval arithmetic entirely in R, valr is also an effective teaching tool for introducing interval analysis to early-stage analysts without requiring familiarity with both command-line tools and R. To aid in demonstrating the interval operations available in valr, we developed the bed_glyph() tool which produces plots demonstrating the input and output of operations in valr in a manner similar to those found in the BEDtools documentation. Shown below is the code required to produce glyphs displaying the results of intersecting x and y intervals with bed_intersect(), and the result of merging x intervals with bed_merge() (Figure 1).

x <- tibble::tribble(
  ~chrom, ~start, ~end,
  "chr1", 25,      50,
  "chr1", 100,     125
)

y <- tibble::tribble(
  ~chrom, ~start, ~end,
  "chr1", 30,      75
)

bed_glyph(bed_intersect(x, y))

And this glyph illustrates bed_merge():

x <- tibble::tribble(
  ~chrom, ~start, ~end,
  "chr1",       1,      50,
  "chr1",       10,     75,
  "chr1",       100,    120
)

bed_glyph(bed_merge(x))

Figure 1. Visualizing interval operations in valr with bed_glyph().

Grouping data. The group_by function in dplyr can be used to execute 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 <- tibble::tribble(
  ~chrom, ~start, ~end, ~strand,
  "chr1", 1,       100,  "+",
  "chr1", 50,      150,  "+",
  "chr2", 100,     200,  "-"
)

y <- tibble::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)
#> # A tibble: 2 x 8
#>   chrom start.x end.x strand.x start.y end.y strand.y .overlap
#>   <chr>   <dbl> <dbl>    <chr>   <dbl> <dbl>    <chr>    <int>
#> 1  chr1       1   100        +      50   125        +       50
#> 2  chr1      50   150        +      50   125        +       75

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)
#> # A tibble: 3 x 8
#>   chrom start.x end.x strand.x start.y end.y strand.y .overlap
#>   <chr>   <dbl> <dbl>    <chr>   <dbl> <dbl>    <chr>    <int>
#> 1  chr2     100   200        -      50   150        -       50
#> 2  chr1       1   100        +      50   150        +       50
#> 3  chr1      50   150        +      50   150        +      100

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))

API. The major functions available in valr are shown in Table 1.

Summarizing interval coverage across genomic features

This demonstration illustrates how to use valr tools to perform a “meta-analysis” of signals relative to genomic features. Here we analyze the distribution of histone marks surrounding transcription start sites, using H3K4Me3 Chip-Seq data from the ENCODE project.

First we load packages and relevant data.

bedfile <- valr_example("genes.hg19.chr22.bed.gz")
genomefile <- valr_example("hg19.chrom.sizes.gz")
bgfile  <- valr_example("hela.h3k4.chip.bg.gz")

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) %>%
  bed_makewindows(genome, win_size)

x
#> # A tibble: 13,530 x 7
#>    chrom    start      end      name score strand .win_id
#>    <chr>    <int>    <int>     <chr> <chr>  <chr>   <int>
#>  1 chr22 16161065 16161115 LINC00516     3      +       1
#>  2 chr22 16161115 16161165 LINC00516     3      +       2
#>  3 chr22 16161165 16161215 LINC00516     3      +       3
#>  4 chr22 16161215 16161265 LINC00516     3      +       4
#>  5 chr22 16161265 16161315 LINC00516     3      +       5
#>  6 chr22 16161315 16161365 LINC00516     3      +       6 
#>  7 chr22 16161365 16161415 LINC00516     3      +       7
#>  8 chr22 16161415 16161465 LINC00516     3      +       8
#>  9 chr22 16161465 16161515 LINC00516     3      +       9
#> 10 chr22 16161515 16161565 LINC00516     3      +      10
#> # ... with 13,520 more rows

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))
             
res
#> # A tibble: 41 x 3
#>    .win_id win_mean    win_sd
#>      <int>    <dbl>     <dbl>
#>  1       1 100.8974  85.83423
#>  2       2 110.6829  81.13521
#>  3       3 122.9070  99.09635
#>  4       4 116.2800  96.30098
#>  5       5 116.3500 102.33773
#>  6       6 124.9048  95.08887
#>  7       7 122.9437  94.39792
#>  8       8 127.5946  91.47407
#>  9       9 130.2051  95.71309
#> 10      10 130.1220  88.82809
#> # ... with 31 more rows

Finally, these summary statistics are used to construct a plot that illustrates histone density surrounding TSSs (Figure 2).

library(ggplot2)

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)

p <- ggplot(res, aes(x = .win_id, y = win_mean)) +
  geom_point(size = 0.25) + geom_pointrange(sd_limits, size = 0.1) +
  scale_x_continuous(labels = x_labels, breaks = x_breaks) +
  xlab("Position (bp from TSS)") + ylab("Signal") +
  theme_classic()

Figure 2. Meta-analysis of signals relative to genomic features with valr.

(A) Summarized coverage of human H3K4Me3 Chip-Seq coverage across positive strand transcription start sites on chromosome 22. Data presented +/- SD.

Interval statistics

Estimates of significance for interval overlaps can be obtained by combining bed_shuffle(), bed_random() and the sample_ functions from dplyr with interval statistics in valr.

Here, we examine the extent of overlap of repeat classes (repeatmasker track obtained from the UCSC genome browser) with exons in the human genome (hg19 build, on chr22 only, for simplicity) using the jaccard similarity index. bed_jaccard() implements the jaccard test to examine the similarity between two sets of genomic intervals. Using bed_shuffle() and replicate() we generate a data_frame containing 100 sets of randomly selected intervals then calculate the jaccard index for each set against the repeat intervals to generate a null-distribution of jaccard scores. Finally, an empirical p-value is calculated from the null-distribution.

library(tidyverse)

repeats <- read_bed(valr_example("hg19.rmsk.chr22.bed.gz"), n_fields = 6)
genome <- read_genome(valr_example("hg19.chrom.sizes.gz"))
genes <- read_bed12(valr_example("hg19.refGene.chr22.bed.gz"))
# convert bed12 to bed with exons
exons <- bed12_to_exons(genes)

# function to repeat interval shuffling
shuffle_intervals <- function(n, .data, genome) {
  replicate(n, bed_shuffle(.data, genome), simplify = FALSE) %>%
    bind_rows(.id = "rep") %>%
    group_by(rep) %>% nest()
}

nreps <- 100

shuffled <- shuffle_intervals(n = nreps, repeats, genome) %>%
  mutate(jaccard = data %>%
            map(bed_jaccard, repeats) %>%
            map_dbl("jaccard"))
shuffled
#> # A tibble: 100 x 3
#>      rep                  data      jaccard
#>    <chr>                <list>        <dbl>
#>  1     1 <tibble [10,000 x 6]> 0.0003388967
#>  2     2 <tibble [10,000 x 6]> 0.0004965988
#>  3     3 <tibble [10,000 x 6]> 0.0002974843
#>  4     4 <tibble [10,000 x 6]> 0.0006899870
#>  5     5 <tibble [10,000 x 6]> 0.0004678412
#>  6     6 <tibble [10,000 x 6]> 0.0001726937
#>  7     7 <tibble [10,000 x 6]> 0.0004694941
#>  8     8 <tibble [10,000 x 6]> 0.0004660410
#>  9     9 <tibble [10,000 x 6]> 0.0006846643
#> 10    10 <tibble [10,000 x 6]> 0.0002143829
#> # ... with 90 more rows

obs <- bed_jaccard(repeats, exons)
obs
#> # A tibble: 1 x 4
#>    len_i   len_u    jaccard     n
#>    <dbl>   <dbl>      <dbl> <dbl>
#> 1 112123 4132109 0.02789139   805

pvalue <- sum(shuffled$jaccard >= obs$jaccard) + 1 /(nreps + 1)
pvalue
#> [1] 0.00990099

Benchmarking against bedtools

In order to ensure that valr performs fast enough to enable interactive analysis, key functionality is implemented in C++. To test the speed of major valr functions we generated two data_frames containing 1 million randomly selected 1 kilobase intervals derived from the human genome (hg19). Most of the major valr functions complete execution in less than 1 second, demonstrating that valr can process large interval datasets efficiently (Figure 3A).

Figure 3. Performance of valr functions.

(A) Timings were calculated by performing 10 repetitions of indicated functions on data frames preloaded in R containing 1 million random 1 kilobase x/y intervals generated using bed_random(). (B) Timings for executing functions in BEDtools v2.25.0 or equivalent functions in valr using the same interval sets as in (A) written to files. All BEDtools function outputs were written to /dev/null/, and were timed using GNU time. Timings for valr functions in (B) include times for reading files using read_bed() functions and were timed using the microbenchmark package.

We also benchmarked major valr functions against corresponding commands in BEDtools. valr operates on data_frames already loaded into RAM, whereas BEDtools performs file-reading, processing, and writing. To compare valr against BEDtools we generated two BED files containing 1 million randomly selected 1 kilobase intervals derived from the human genome (hg19). For valr functions, we timed reading the table into R (e.g. with read_bed()) and performing the respective function. For BEDtools commands we timed executing the command with the output written to /dev/null. valr functions performed similarly or faster than BEDtools commands, with the exception of bed_map and bed_fisher (Figure 3B).

Reproducible reports and interactive visualizations

Command-line tools like BEDtools and bedops can be incorporated into reproducible workflows (e.g., with snakemake¹¹), but it is cumbersome to transition from command-line tools to exploratory analysis and plotting software. RMarkdown documents are plain text files, amenable to version control, which provide an interface to generate feature rich PDF and HTML reports that combine text, executable code, and figures in a single document. valr can be used in RMarkdown documents to provide rapid documentation of exploratory data analyses and generate reproducible work-flows for data processing. Moreover, new features in RStudio, such as notebook viewing, and multiple language support enable similar functionality to another popular notebook platform jupyter notebooks.

Additionally, valr seamlessly integrates into R shiny¹² applications allowing for complex interactive visualizations relating to genomic interval analyses. We have developed a shiny application (available on Gitub) that explores ChiP-Seq signal density surrounding transcription start sites and demonstrates the ease of implementing valr to power dynamic visualizations.