Getting Started

In BRGenomics: Tools for the Efficient Analysis of High-Resolution Genomics Data

Installation

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.

Parallel Processing

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.

Included Datasets

BRGenomics ships with an example dataset of PRO-seq data from Drosophila melanogaster^[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 chromosome^[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

Notice that the data is contained within a GRanges object. GRanges objects, from the r Biocpkg("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

The GRanges above contains all Flybase annotated transcripts from chromosome 4, with no filtering of any kind.

Basic Operations on GRanges

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) 

\

Select the 2nd transcript:

txs_dm6_chr4[2]

\

Select 4 transcripts:

tx4 <- txs_dm6_chr4[c(1, 10, 200, 300)]
tx4

\

Get the lengths of the first 4 transcripts:

width(tx4)

\

Get a dataframe of the metadata for the first 4 transcripts:

mcols(tx4)

\

Access a single metadata column for the first 4 transcripts:

mcols(tx4)[, 2]
mcols(tx4)[, "gene_id"]
tx4$gene_id
tx4_names <- tx4$tx_name
tx4_names

\

Get the first gene_id (a metadata element):

tx4$gene_id[1]

\

Remove a metadata column:

mcols(tx4) <- mcols(tx4)[, -1]
tx4

\

Rename metadata:

names(mcols(tx4)) <- "gene_id"
tx4

\

Add metadata; same as access methods (mcols()[], mcols()$, or simply $):

tx4$tx_name <- tx4_names
tx4

\

Modify metadata:

tx4$gene_id[1] <- "gene1"
tx4$tx_name <- 1:4
tx4

\

Get beginning of ranges (not strand specific):

start(tx4)

\

Get beginning of ranges (strand specific):

tx4_tss <- resize(tx4, width = 1, fix = "start")
tx4_tss
start(tx4_tss)

\

Remove all metadata:

mcols(tx4) <- NULL
tx4

BRGenomics documentation built on Nov. 8, 2020, 8:03 p.m.