Ranges revisited

In Bioconductor there are two classes, IRanges and GRanges, that are standard data structures for representing genomics data. Throughout this document I refer to either of these classes as Ranges if an operation can be performed on either class, otherwise I explicitly mention if a function is appropriate for an IRanges or GRanges.

Ranges objects can either represent sets of integers as IRanges (which have start, end and width attributes) or represent genomic intervals (which have additional attributes, sequence name, and strand) as GRanges. In addition, both types of Ranges can store information about their intervals as metadata columns (for example GC content over a genomic interval).

Ranges objects follow the tidy data principle: each row of a Ranges object corresponds to an interval, while each column will represent a variable about that interval, and generally each object will represent a single unit of observation (like gene annotations).

Consequently, Ranges objects provide a powerful representation for reasoning about genomic data. In this vignette, you will learn more about Ranges objects and how via grouping, restriction and summarisation you can perform common data tasks.

Constructing Ranges

To construct an IRanges we require that there are at least two columns that represent at either a starting coordinate, finishing coordinate or the width of the interval.

## IRanges object with 7 ranges and 0 metadata columns:
##           start       end     width
##       <integer> <integer> <integer>
##   [1]         2         1         0
##   [2]         1         1         1
##   [3]         0         1         2
##   [4]        -1         1         3
##   [5]        13        14         2
##   [6]        14        14         1
##   [7]        15        14         0

To construct a GRanges we require a column that represents that sequence name ( contig or chromosome id), and an optional column to represent the strandedness of an interval.

## GRanges object with 7 ranges and 1 metadata column:
##       seqnames    ranges strand |                gc
##          <Rle> <IRanges>  <Rle> |         <numeric>
##   [1]     chr2       2-1      - |  0.76255108229816
##   [2]     chr1         1      - | 0.669021712383255
##   [3]     chr2       0-1      + | 0.204612161964178
##   [4]     chr2      -1-1      - | 0.357524853432551
##   [5]     chr1     13-14      - | 0.359475114848465
##   [6]     chr1        14      - | 0.690290528349578
##   [7]     chr2     15-14      - | 0.535811153938994
##   -------
##   seqinfo: 2 sequences from an unspecified genome; no seqlengths

Arithmetic on Ranges

Sometimes you want to modify a genomic interval by altering the width of the interval while leaving the start, end or midpoint of the coordinates unaltered. This is achieved with the mutate verb along with anchor_* adverbs.

The act of anchoring fixes either the start, end, center coordinates of the Range object, as shown in the figure and code below and anchors are used in combination with either mutate or stretch.

rng <- as_iranges(data.frame(start=c(1, 2, 3), end=c(5, 2, 8)))
grng <- as_granges(data.frame(start=c(1, 2, 3), end=c(5, 2, 8), 
                          seqnames = "seq1",
                          strand = c("+", "*", "-")))
mutate(rng, width = 10)
## IRanges object with 3 ranges and 0 metadata columns:
##           start       end     width
##       <integer> <integer> <integer>
##   [1]         1        10        10
##   [2]         2        11        10
##   [3]         3        12        10
mutate(anchor_start(rng), width = 10)
## IRanges object with 3 ranges and 0 metadata columns:
##           start       end     width
##       <integer> <integer> <integer>
##   [1]         1        10        10
##   [2]         2        11        10
##   [3]         3        12        10
mutate(anchor_end(rng), width = 10)
## IRanges object with 3 ranges and 0 metadata columns:
##           start       end     width
##       <integer> <integer> <integer>
##   [1]        -4         5        10
##   [2]        -7         2        10
##   [3]        -1         8        10
mutate(anchor_center(rng), width = 10)
## IRanges object with 3 ranges and 0 metadata columns:
##           start       end     width
##       <integer> <integer> <integer>
##   [1]        -2         7        10
##   [2]        -3         6        10
##   [3]         1        10        10
mutate(anchor_3p(grng), width = 10) # leave negative strand fixed
## GRanges object with 3 ranges and 0 metadata columns:
##       seqnames    ranges strand
##          <Rle> <IRanges>  <Rle>
##   [1]     seq1      -4-5      +
##   [2]     seq1      -7-2      *
##   [3]     seq1      3-12      -
##   -------
##   seqinfo: 1 sequence from an unspecified genome; no seqlengths
mutate(anchor_5p(grng), width = 10) # leave positive strand fixed
## GRanges object with 3 ranges and 0 metadata columns:
##       seqnames    ranges strand
##          <Rle> <IRanges>  <Rle>
##   [1]     seq1      1-10      +
##   [2]     seq1      2-11      *
##   [3]     seq1      -1-8      -
##   -------
##   seqinfo: 1 sequence from an unspecified genome; no seqlengths

Similarly, you can modify the width of an interval using the stretch verb. Without anchoring, this function will extend the interval in either direction by an integer amount. With anchoring, either the start, end or midpoint are preserved.

## IRanges object with 3 ranges and 0 metadata columns:
##           start       end     width
##       <integer> <integer> <integer>
##   [1]        -4        10        15
##   [2]        -3         7        11
##   [3]        -2        13        16
stretch(anchor_end(rng2), 10)
## IRanges object with 3 ranges and 0 metadata columns:
##           start       end     width
##       <integer> <integer> <integer>
##   [1]       -14        10        25
##   [2]       -13         7        21
##   [3]       -12        13        26
## IRanges object with 3 ranges and 0 metadata columns:
##           start       end     width
##       <integer> <integer> <integer>
##   [1]        -4        20        25
##   [2]        -3        17        21
##   [3]        -2        23        26
stretch(anchor_3p(grng), 10)
## GRanges object with 3 ranges and 0 metadata columns:
##       seqnames    ranges strand
##          <Rle> <IRanges>  <Rle>
##   [1]     seq1      -9-5      +
##   [2]     seq1      -8-2      *
##   [3]     seq1      3-18      -
##   -------
##   seqinfo: 1 sequence from an unspecified genome; no seqlengths
stretch(anchor_5p(grng), 10)
## GRanges object with 3 ranges and 0 metadata columns:
##       seqnames    ranges strand
##          <Rle> <IRanges>  <Rle>
##   [1]     seq1      1-15      +
##   [2]     seq1      2-12      *
##   [3]     seq1      -7-8      -
##   -------
##   seqinfo: 1 sequence from an unspecified genome; no seqlengths

Ranges can be shifted left or right. If strand information is available we can also shift upstream or downstream.

shift_left(rng, 10)
## IRanges object with 3 ranges and 0 metadata columns:
##           start       end     width
##       <integer> <integer> <integer>
##   [1]        -9        -5         5
##   [2]        -8        -8         1
##   [3]        -7        -2         6
shift_right(rng, 10)
## IRanges object with 3 ranges and 0 metadata columns:
##           start       end     width
##       <integer> <integer> <integer>
##   [1]        11        15         5
##   [2]        12        12         1
##   [3]        13        18         6
shift_upstream(grng, 10)
## GRanges object with 3 ranges and 0 metadata columns:
##       seqnames    ranges strand
##          <Rle> <IRanges>  <Rle>
##   [1]     seq1     -9--5      +
##   [2]     seq1        -8      *
##   [3]     seq1     13-18      -
##   -------
##   seqinfo: 1 sequence from an unspecified genome; no seqlengths
## GRanges object with 3 ranges and 0 metadata columns:
##       seqnames    ranges strand
##          <Rle> <IRanges>  <Rle>
##   [1]     seq1     11-15      +
##   [2]     seq1        12      *
##   [3]     seq1     -7--2      -
##   -------
##   seqinfo: 1 sequence from an unspecified genome; no seqlengths

Grouping Ranges

plyranges introduces a new class of Ranges called RangesGrouped, this is a similar idea to the grouped data.frame\tibble in dplyr.

Grouping can act on either the core components or the metadata columns of a Ranges object.

It is most effective when combined with other verbs such as mutate(), summarise(), filter(), reduce_ranges() or disjoin_ranges().

grng <- data.frame(seqnames = sample(c("chr1", "chr2"), 7, replace = TRUE),
         strand = sample(c("+", "-"), 7, replace = TRUE),
         gc = runif(7),
         start = 1:7,
         width = 10) %>%
  as_granges()

grng_by_strand <- grng %>%
  group_by(strand)

grng_by_strand
## GRanges object with 7 ranges and 1 metadata column:
## Groups: strand [2]
##       seqnames    ranges strand |                gc
##          <Rle> <IRanges>  <Rle> |         <numeric>
##   [1]     chr2      1-10      - | 0.889453538926318
##   [2]     chr2      2-11      + | 0.180407245177776
##   [3]     chr1      3-12      - | 0.629390850430354
##   [4]     chr2      4-13      + | 0.989564136601985
##   [5]     chr1      5-14      - | 0.130288870073855
##   [6]     chr1      6-15      - | 0.330660525709391
##   [7]     chr2      7-16      - | 0.865120546659455
##   -------
##   seqinfo: 2 sequences from an unspecified genome; no seqlengths

Restricting Ranges

The verb filter can be used to restrict rows in the Ranges. Note that grouping will cause the filter to act within each group of the data.

grng %>% filter(gc < 0.3)
## GRanges object with 2 ranges and 1 metadata column:
##       seqnames    ranges strand |                gc
##          <Rle> <IRanges>  <Rle> |         <numeric>
##   [1]     chr2      2-11      + | 0.180407245177776
##   [2]     chr1      5-14      - | 0.130288870073855
##   -------
##   seqinfo: 2 sequences from an unspecified genome; no seqlengths
## GRanges object with 2 ranges and 1 metadata column:
## Groups: strand [2]
##       seqnames    ranges strand |                gc
##          <Rle> <IRanges>  <Rle> |         <numeric>
##   [1]     chr2      1-10      - | 0.889453538926318
##   [2]     chr2      4-13      + | 0.989564136601985
##   -------
##   seqinfo: 2 sequences from an unspecified genome; no seqlengths

We also provide the convenience methods filter_by_overlaps and filter_by_non_overlaps for restricting by any overlapping Ranges.

ir0 <- data.frame(start = c(5,10, 15,20), width = 5) %>%
  as_iranges()
ir1 <- data.frame(start = 2:6, width = 3:7) %>%
  as_iranges()
ir0
## IRanges object with 4 ranges and 0 metadata columns:
##           start       end     width
##       <integer> <integer> <integer>
##   [1]         5         9         5
##   [2]        10        14         5
##   [3]        15        19         5
##   [4]        20        24         5
## IRanges object with 5 ranges and 0 metadata columns:
##           start       end     width
##       <integer> <integer> <integer>
##   [1]         2         4         3
##   [2]         3         6         4
##   [3]         4         8         5
##   [4]         5        10         6
##   [5]         6        12         7
## IRanges object with 2 ranges and 0 metadata columns:
##           start       end     width
##       <integer> <integer> <integer>
##   [1]         5         9         5
##   [2]        10        14         5
## IRanges object with 2 ranges and 0 metadata columns:
##           start       end     width
##       <integer> <integer> <integer>
##   [1]        15        19         5
##   [2]        20        24         5

Summarising Ranges

The summarise function will return a DataFrame because the information required to return a Ranges object is lost. It is often most useful to use summarise() in combination with the group_by() family of functions.

## DataFrame with 2 rows and 2 columns
##       query                gc
##   <integer>         <numeric>
## 1         1 0.675554534769617
## 2         2 0.635795166483149

Joins, or another way at looking at overlaps between Ranges

A join acts on two GRanges objects, a query and a subject.

Query and Subject Ranges

Query and Subject Ranges

The join operator is relational in the sense that metadata from the query and subject ranges is retained in the joined range. All join operators in the plyranges DSL generate a set of hits based on overlap or proximity of ranges and use those hits to merge the two datasets in different ways. There are four supported matching algorithms: overlap, nearest, precede, and follow. We can further restrict the matching by whether the query is completely within the subject, and adding the directed suffix ensures that matching ranges have the same direction (strand).

The first function, join_overlap_intersect() will return a Ranges object where the start, end, and width coordinates correspond to the amount of any overlap between the left and right input Ranges. It also returns any metadatain the subject range if the subject overlaps the query.

## GRanges object with 2 ranges and 2 metadata columns:
##       seqnames    ranges strand |    key.a    key.b
##          <Rle> <IRanges>  <Rle> | <factor> <factor>
##   [1]     chr1       2-4      + |        a        A
##   [2]     chr1       6-7      + |        a        B
##   -------
##   seqinfo: 1 sequence from an unspecified genome; no seqlengths
Intersect Join

Intersect Join

The join_overlap_inner() function will return the Ranges in the query that overlap any Ranges in the subject. Like the join_overlap_intersect() function metadata of the subject Range is returned if it overlaps the query.

## GRanges object with 2 ranges and 2 metadata columns:
##       seqnames    ranges strand |    key.a    key.b
##          <Rle> <IRanges>  <Rle> | <factor> <factor>
##   [1]     chr1       1-7      + |        a        A
##   [2]     chr1       1-7      + |        a        B
##   -------
##   seqinfo: 1 sequence from an unspecified genome; no seqlengths
Inner Join

Inner Join

We also provide a convenience method called find_overlaps that computes the same result as join_overlap_inner().

find_overlaps(query, subject)
## GRanges object with 2 ranges and 2 metadata columns:
##       seqnames    ranges strand |    key.a    key.b
##          <Rle> <IRanges>  <Rle> | <factor> <factor>
##   [1]     chr1       1-7      + |        a        A
##   [2]     chr1       1-7      + |        a        B
##   -------
##   seqinfo: 1 sequence from an unspecified genome; no seqlengths

The join_overlap_left() method will perform an outer left join.

First any overlaps that are found will be returned similar to join_overlap_inner(). Then any non-overlapping ranges will be returned, with missing values on the metadata columns.

## GRanges object with 3 ranges and 2 metadata columns:
##       seqnames    ranges strand |    key.a    key.b
##          <Rle> <IRanges>  <Rle> | <factor> <factor>
##   [1]     chr1       1-7      + |        a        A
##   [2]     chr1       1-7      + |        a        B
##   [3]     chr1      9-10      - |        b     <NA>
##   -------
##   seqinfo: 1 sequence from an unspecified genome; no seqlengths
Left Join

Left Join

Compared with filter_by_overlaps() above, the overlap left join expands the Ranges to give information about each interval on the query Ranges that overlap those on the subject Ranges as well as the intervals on the left that do not overlap any range on the right.

Finding your neighbours

We also provide methods for finding nearest, preceding or following Ranges. Conceptually this is identical to our approach for finding overlaps, except the semantics of the join are different.

join_nearest(ir0, ir1)
## IRanges object with 4 ranges and 1 metadata column:
##           start       end     width |                gc
##       <integer> <integer> <integer> |         <numeric>
##   [1]         5         9         5 | 0.780358511023223
##   [2]        10        14         5 | 0.780358511023223
##   [3]        15        19         5 | 0.780358511023223
##   [4]        20        24         5 | 0.780358511023223
join_follow(ir0, ir1)
## IRanges object with 4 ranges and 1 metadata column:
##           start       end     width |                gc
##       <integer> <integer> <integer> |         <numeric>
##   [1]         5         9         5 | 0.777584439376369
##   [2]        10        14         5 | 0.603324356488883
##   [3]        15        19         5 | 0.780358511023223
##   [4]        20        24         5 | 0.780358511023223
join_precede(ir0, ir1) # nothing precedes returns empty `Ranges`
## IRanges object with 0 ranges and 1 metadata column:
##        start       end     width |        gc
##    <integer> <integer> <integer> | <numeric>
join_precede(ir1, ir0)
## IRanges object with 5 ranges and 1 metadata column:
##           start       end     width |                gc
##       <integer> <integer> <integer> |         <numeric>
##   [1]         2         4         3 | 0.777584439376369
##   [2]         3         6         4 | 0.827303449623287
##   [3]         4         8         5 | 0.603324356488883
##   [4]         5        10         6 | 0.491231821943074
##   [5]         6        12         7 | 0.780358511023223

Example: dealing with multi-mapping

This example is taken from the Bioconductor support site.

We have two Ranges objects. The first contains single nucleotide positions corresponding to an intensity measurement such as a ChiP-seq experiment, while the other contains coordinates for two genes of interest.

We want to identify which positions in the intensities Ranges overlap the genes, where each row corresponds to a position that overlaps a single gene.

First we create the two Ranges objects

intensities <- data.frame(seqnames = "VI",
                          start = c(3320:3321,3330:3331,3341:3342),
                          width = 1) %>% 
  as_granges()

intensities 
## GRanges object with 6 ranges and 0 metadata columns:
##       seqnames    ranges strand
##          <Rle> <IRanges>  <Rle>
##   [1]       VI      3320      *
##   [2]       VI      3321      *
##   [3]       VI      3330      *
##   [4]       VI      3331      *
##   [5]       VI      3341      *
##   [6]       VI      3342      *
##   -------
##   seqinfo: 1 sequence from an unspecified genome; no seqlengths
genes <- data.frame(seqnames = "VI", 
                    start = c(3322, 3030),
                    end = c(3846, 3338),
                    gene_id=c("YFL064C", "YFL065C")) %>% 
  as_granges()
                    
genes
## GRanges object with 2 ranges and 1 metadata column:
##       seqnames    ranges strand |  gene_id
##          <Rle> <IRanges>  <Rle> | <factor>
##   [1]       VI 3322-3846      * |  YFL064C
##   [2]       VI 3030-3338      * |  YFL065C
##   -------
##   seqinfo: 1 sequence from an unspecified genome; no seqlengths

Now to find where the positions overlap each gene, we can perform an overlap join. This will automatically carry over the gene_id information as well as their coordinates (we can drop those by only selecting the gene_id).

olap <- join_overlap_inner(intensities, genes) %>%
  select(gene_id)
olap
## GRanges object with 8 ranges and 1 metadata column:
##       seqnames    ranges strand |  gene_id
##          <Rle> <IRanges>  <Rle> | <factor>
##   [1]       VI      3320      * |  YFL065C
##   [2]       VI      3321      * |  YFL065C
##   [3]       VI      3330      * |  YFL065C
##   [4]       VI      3330      * |  YFL064C
##   [5]       VI      3331      * |  YFL065C
##   [6]       VI      3331      * |  YFL064C
##   [7]       VI      3341      * |  YFL064C
##   [8]       VI      3342      * |  YFL064C
##   -------
##   seqinfo: 1 sequence from an unspecified genome; no seqlengths

Several positions match to both genes. We can count them using summarise and grouping by the start position:

## DataFrame with 6 rows and 2 columns
##       start         n
##   <integer> <integer>
## 1      3320         1
## 2      3321         1
## 3      3330         2
## 4      3331         2
## 5      3341         1
## 6      3342         1

Grouping by overlaps

It’s also possible to group by overlaps. Using this approach we can count the number of overlaps that are greater than 0.

## IRanges object with 6 ranges and 2 metadata columns:
## Groups: query [2]
##           start       end     width |                gc     query
##       <integer> <integer> <integer> |         <numeric> <integer>
##   [1]         5         9         5 | 0.827303449623287         1
##   [2]         5         9         5 | 0.603324356488883         1
##   [3]         5         9         5 | 0.491231821943074         1
##   [4]         5         9         5 | 0.780358511023223         1
##   [5]        10        14         5 | 0.491231821943074         2
##   [6]        10        14         5 | 0.780358511023223         2
grp_by_olap %>%
  mutate(n_overlaps = n())
## IRanges object with 6 ranges and 3 metadata columns:
## Groups: query [2]
##           start       end     width |                gc     query n_overlaps
##       <integer> <integer> <integer> |         <numeric> <integer>  <integer>
##   [1]         5         9         5 | 0.827303449623287         1          4
##   [2]         5         9         5 | 0.603324356488883         1          4
##   [3]         5         9         5 | 0.491231821943074         1          4
##   [4]         5         9         5 | 0.780358511023223         1          4
##   [5]        10        14         5 | 0.491231821943074         2          2
##   [6]        10        14         5 | 0.780358511023223         2          2

Of course we can also add overlap counts via the count_overlaps() function.

ir0 %>%
  mutate(n_overlaps = count_overlaps(., ir1))
## IRanges object with 4 ranges and 1 metadata column:
##           start       end     width | n_overlaps
##       <integer> <integer> <integer> |  <integer>
##   [1]         5         9         5 |          4
##   [2]        10        14         5 |          2
##   [3]        15        19         5 |          0
##   [4]        20        24         5 |          0

Data Import/Output

We provide convenience functions via rtracklayer and GenomicAlignments for reading/writing the following data formats to/from Ranges objects.

plyranges functions File Format
read_bam() BAM
read_bed()/write_bed() BED
read_bedgraph()/ write_bedgraph() BEDGraph
read_narrowpeaks()/ write_narrowpeaks() narrowPeaks
read_gff() / write_gff() GFF(1-3) / GTF
read_bigwig() / write_bigwig() BigWig
read_wig() / write_wig() Wig

Learning more

Appendix

## R Under development (unstable) (2019-11-12 r77405)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 14.04.5 LTS
## 
## Matrix products: default
## BLAS:   /home/travis/R-bin/lib/R/lib/libRblas.so
## LAPACK: /home/travis/R-bin/lib/R/lib/libRlapack.so
## 
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##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] stats4    parallel  stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
## [1] ggplot2_3.2.1        plyranges_1.7.4      GenomicRanges_1.39.1
## [4] GenomeInfoDb_1.23.0  IRanges_2.21.1       S4Vectors_0.25.0    
## [7] BiocGenerics_0.33.0  BiocStyle_2.15.0    
## 
## loaded via a namespace (and not attached):
##  [1] Rcpp_1.0.3                  lattice_0.20-38            
##  [3] Rsamtools_2.3.2             Biostrings_2.55.0          
##  [5] assertthat_0.2.1            rprojroot_1.3-2            
##  [7] digest_0.6.22               R6_2.4.1                   
##  [9] plyr_1.8.4                  backports_1.1.5            
## [11] evaluate_0.14               highr_0.8                  
## [13] pillar_1.4.2                zlibbioc_1.33.0            
## [15] rlang_0.4.1                 lazyeval_0.2.2             
## [17] Matrix_1.2-17               rmarkdown_1.17             
## [19] pkgdown_1.4.1               desc_1.2.0                 
## [21] labeling_0.3                BiocParallel_1.21.0        
## [23] stringr_1.4.0               RCurl_1.95-4.12            
## [25] munsell_0.5.0               DelayedArray_0.13.0        
## [27] compiler_4.0.0              rtracklayer_1.47.0         
## [29] xfun_0.11                   pkgconfig_2.0.3            
## [31] htmltools_0.4.0             tidyselect_0.2.5           
## [33] SummarizedExperiment_1.17.0 tibble_2.1.3               
## [35] GenomeInfoDbData_1.2.2      bookdown_0.15              
## [37] matrixStats_0.55.0          XML_3.98-1.20              
## [39] crayon_1.3.4                dplyr_0.8.3                
## [41] withr_2.1.2                 GenomicAlignments_1.23.1   
## [43] MASS_7.3-51.4               bitops_1.0-6               
## [45] grid_4.0.0                  gtable_0.3.0               
## [47] magrittr_1.5                scales_1.0.0               
## [49] stringi_1.4.3               XVector_0.27.0             
## [51] reshape2_1.4.3              fs_1.3.1                   
## [53] tools_4.0.0                 Biobase_2.47.0             
## [55] glue_1.3.1                  purrr_0.3.3                
## [57] yaml_2.2.0                  colorspace_1.4-1           
## [59] BiocManager_1.30.9          memoise_1.1.0              
## [61] knitr_1.26