BRGenomics 1.8.0
As of Bioconductor 3.11 (release date April 28, 2020), BRGenomics can be installed directly from Bioconductor:
# install.packages("BiocManager")
BiocManager::install("BRGenomics")Alternatively, the latest development version can be installed from
GitHub:
# install.packages("remotes")
remotes::install_github("mdeber/BRGenomics@R3")BRGenomics (and Bioconductor 3.11) require R version 4.0 (release April 24,
2020). Installing the R3 branch, as shown above, is required to install under
R 3.x.
If you install the development version from Github and you’re using Windows, Rtools for Windows is required.
By default, many BRGenomics functions use multicore processing as implemented in
the parallel package. BRGenomics functions that can be parallelized always
contain the argument ncores. If not specified, the default is to use the
global option “mc.cores” (the same option used by the parallel package), or
2 if that option is not set.
If you wanted to change the global default to 4 cores, for example, you would
run options(mc.cores = 4) at the start of your R session. If you’re unsure
how many cores your processor has, run parallel::detectCores().
While performance can be memory constrained in some cases (and thus actually hampered by excessive parallelization), substantial performance benefits can be achieved by maximizing parallelization.
However, parallel processing is not available on Windows. To maintain
compatibility, all code in this vignette as well as the example code in the
documentation is to use a single core, i.e. ncores = 1.
BRGenomics ships with an example dataset of PRO-seq data from Drosophila melanogaster1 Hojoong Kwak, Nicholas J. Fuda, Leighton J. Core, John T. Lis (2013). Precise Maps of RNA Polymerase Reveal How Promoters Direct Initiation and Pausing. Science 339(6122): 950–953. https://doi.org/10.1126/science.1229386. PRO-seq is a basepair-resolution method that uses 3’-end sequencing of nascent RNA to map the locations of actively engaged RNA polymerases.
To keep the dataset small, we’ve only included reads mapping to the fourth chromosome2 Chromosome 4 in Drosophila, often referred to as the “dot” chromosome, is very small and contains very few genes.
The included datasets can be accessed using the data() function:
library(BRGenomics)data("PROseq")
PROseq## GRanges object with 47380 ranges and 1 metadata column:
## seqnames ranges strand | score
## <Rle> <IRanges> <Rle> | <integer>
## [1] chr4 1295 + | 1
## [2] chr4 41428 + | 1
## [3] chr4 42588 + | 1
## [4] chr4 42590 + | 2
## [5] chr4 42593 + | 5
## ... ... ... ... . ...
## [47376] chr4 1307742 - | 1
## [47377] chr4 1316537 - | 1
## [47378] chr4 1318960 - | 1
## [47379] chr4 1319004 - | 1
## [47380] chr4 1319369 - | 1
## -------
## seqinfo: 7 sequences from an unspecified genomeNotice that the data is contained within a GRanges object. GRanges objects,
from the GenomicRanges package, are very easy to work with, and
are supported by a plethora of useful functions and packages.
The structure of the data will be described later on (in section “Basepair-Resolution GRanges Objects”). For now, we’ll just note that both annotations (e.g. genelists) and data are contained using the same GRanges class.
We’ve included an example genelist to accompany the PRO-seq data:
data("txs_dm6_chr4")
txs_dm6_chr4## GRanges object with 339 ranges and 2 metadata columns:
## seqnames ranges strand | tx_name gene_id
## <Rle> <IRanges> <Rle> | <character> <character>
## [1] chr4 879-5039 + | FBtr0346692 FBgn0267363
## [2] chr4 42774-43374 + | FBtr0344900 FBgn0266617
## [3] chr4 44774-46074 + | FBtr0340499 FBgn0265633
## [4] chr4 56497-60974 + | FBtr0333704 FBgn0264617
## [5] chr4 56497-63124 + | FBtr0333705 FBgn0264617
## ... ... ... ... . ... ...
## [335] chr4 1192419-1196848 - | FBtr0100543 FBgn0039924
## [336] chr4 1192419-1196848 - | FBtr0100544 FBgn0039924
## [337] chr4 1225089-1230713 - | FBtr0100406 FBgn0027101
## [338] chr4 1225737-1230713 - | FBtr0100402 FBgn0027101
## [339] chr4 1225737-1230713 - | FBtr0100404 FBgn0027101
## -------
## seqinfo: 7 sequences from dm6 genomeThe GRanges above contains all Flybase annotated transcripts from chromosome 4, with no filtering of any kind.
For users who are unfamiliar with GRanges objects, this section demonstrates a number of basic operations.
A quick summary of the general structure: Each element of a GRanges object is
called a “range”. As you can see above, each range consists of several
components: seqnames, ranges, and strand. These essential attributes are
all found to the left of the vertical divider above; everything to the right of
that divider is an optional, metadata attribute.
The core attributes can be accessed using the functions seqnames(),
ranges(), and strand(). All metadata can be accessed using mcols(), and
individual columns are accessible with the $ operator. The only reserved
metadata column is the score column, which is just like any other metadata
column, except that users can use the score() function to assess it.
All of the above functions are both “getters” and “setters”, e.g. strand(x)
returns the strand information, and strand(x) <- "+" assigns it.
These and other operations are demonstrated below.
To learn more about GRanges objects, including a general overview of their components, see the useful vignette An Introduction to the GenomicRanges Package. Alternatively, see the archived materials from the 2018 Bioconductor workshop Solving Common Bioinformatic Challenges Using GenomicRanges. Note that this package will implement and streamline a number of common operations, but users should still have a basic familiarity with GRanges objects.
Get the length of the genelist:
length(txs_dm6_chr4) ## [1] 339
Select the 2nd transcript:
txs_dm6_chr4[2]## GRanges object with 1 range and 2 metadata columns:
## seqnames ranges strand | tx_name gene_id
## <Rle> <IRanges> <Rle> | <character> <character>
## [1] chr4 42774-43374 + | FBtr0344900 FBgn0266617
## -------
## seqinfo: 7 sequences from dm6 genome
Select 4 transcripts:
tx4 <- txs_dm6_chr4[c(1, 10, 200, 300)]
tx4## GRanges object with 4 ranges and 2 metadata columns:
## seqnames ranges strand | tx_name gene_id
## <Rle> <IRanges> <Rle> | <character> <character>
## [1] chr4 879-5039 + | FBtr0346692 FBgn0267363
## [2] chr4 69326-110059 + | FBtr0308615 FBgn0085432
## [3] chr4 184225-193489 - | FBtr0089150 FBgn0039890
## [4] chr4 1009895-1027101 - | FBtr0309865 FBgn0025741
## -------
## seqinfo: 7 sequences from dm6 genome
Get the lengths of the first 4 transcripts:
width(tx4)## [1] 4161 40734 9265 17207
Get a dataframe of the metadata for the first 4 transcripts:
mcols(tx4)## DataFrame with 4 rows and 2 columns
## tx_name gene_id
## <character> <character>
## 1 FBtr0346692 FBgn0267363
## 2 FBtr0308615 FBgn0085432
## 3 FBtr0089150 FBgn0039890
## 4 FBtr0309865 FBgn0025741
Access a single metadata column for the first 4 transcripts:
mcols(tx4)[, 2]## [1] "FBgn0267363" "FBgn0085432" "FBgn0039890" "FBgn0025741"mcols(tx4)[, "gene_id"]## [1] "FBgn0267363" "FBgn0085432" "FBgn0039890" "FBgn0025741"tx4$gene_id## [1] "FBgn0267363" "FBgn0085432" "FBgn0039890" "FBgn0025741"tx4_names <- tx4$tx_name
tx4_names## [1] "FBtr0346692" "FBtr0308615" "FBtr0089150" "FBtr0309865"
Get the first gene_id (a metadata element):
tx4$gene_id[1]## [1] "FBgn0267363"
Remove a metadata column:
mcols(tx4) <- mcols(tx4)[, -1]
tx4## GRanges object with 4 ranges and 1 metadata column:
## seqnames ranges strand | X
## <Rle> <IRanges> <Rle> | <character>
## [1] chr4 879-5039 + | FBgn0267363
## [2] chr4 69326-110059 + | FBgn0085432
## [3] chr4 184225-193489 - | FBgn0039890
## [4] chr4 1009895-1027101 - | FBgn0025741
## -------
## seqinfo: 7 sequences from dm6 genome
Rename metadata:
names(mcols(tx4)) <- "gene_id"
tx4## GRanges object with 4 ranges and 1 metadata column:
## seqnames ranges strand | gene_id
## <Rle> <IRanges> <Rle> | <character>
## [1] chr4 879-5039 + | FBgn0267363
## [2] chr4 69326-110059 + | FBgn0085432
## [3] chr4 184225-193489 - | FBgn0039890
## [4] chr4 1009895-1027101 - | FBgn0025741
## -------
## seqinfo: 7 sequences from dm6 genome
Add metadata; same as access methods (mcols()[], mcols()$, or simply $):
tx4$tx_name <- tx4_names
tx4## GRanges object with 4 ranges and 2 metadata columns:
## seqnames ranges strand | gene_id tx_name
## <Rle> <IRanges> <Rle> | <character> <character>
## [1] chr4 879-5039 + | FBgn0267363 FBtr0346692
## [2] chr4 69326-110059 + | FBgn0085432 FBtr0308615
## [3] chr4 184225-193489 - | FBgn0039890 FBtr0089150
## [4] chr4 1009895-1027101 - | FBgn0025741 FBtr0309865
## -------
## seqinfo: 7 sequences from dm6 genome
Modify metadata:
tx4$gene_id[1] <- "gene1"
tx4$tx_name <- 1:4
tx4## GRanges object with 4 ranges and 2 metadata columns:
## seqnames ranges strand | gene_id tx_name
## <Rle> <IRanges> <Rle> | <character> <integer>
## [1] chr4 879-5039 + | gene1 1
## [2] chr4 69326-110059 + | FBgn0085432 2
## [3] chr4 184225-193489 - | FBgn0039890 3
## [4] chr4 1009895-1027101 - | FBgn0025741 4
## -------
## seqinfo: 7 sequences from dm6 genome
Get beginning of ranges (not strand specific):
start(tx4)## [1] 879 69326 184225 1009895
Get beginning of ranges (strand specific):
tx4_tss <- resize(tx4, width = 1, fix = "start")
tx4_tss## GRanges object with 4 ranges and 2 metadata columns:
## seqnames ranges strand | gene_id tx_name
## <Rle> <IRanges> <Rle> | <character> <integer>
## [1] chr4 879 + | gene1 1
## [2] chr4 69326 + | FBgn0085432 2
## [3] chr4 193489 - | FBgn0039890 3
## [4] chr4 1027101 - | FBgn0025741 4
## -------
## seqinfo: 7 sequences from dm6 genomestart(tx4_tss)## [1] 879 69326 193489 1027101
Remove all metadata:
mcols(tx4) <- NULL
tx4## GRanges object with 4 ranges and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## [1] chr4 879-5039 +
## [2] chr4 69326-110059 +
## [3] chr4 184225-193489 -
## [4] chr4 1009895-1027101 -
## -------
## seqinfo: 7 sequences from dm6 genome