The current release of Bioconductor is version 2.14; it works with R version 3.1.0. Users of older R and Bioconductor versions must update their installation to take advantage of new features.
Install the latest release of R, then get the latest version of Bioconductor by starting R and entering the commands
Details, including instructions to
install additional packages and to
troubleshoot are provided
below. A devel version of
Bioconductor is available. There are good
reasons for using
biocLite() for managing
Download the most recent version of R. The R FAQs and the R Installation and Administration Manual contain detailed instructions for installing R on various platforms (Linux, OS X, and Windows being the main ones).
Start the R program; on Windows and OS X, this will usually mean double-clicking on the R application, on UNIX-like systems, type "R" at a shell prompt.
As a first step with R, start the R help browser by typing
help.start() in the R command window. For help on any
function, e.g. the "mean" function, type
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biocLite.R script to install Bioconductor packages. To
install core packages, type the following in an R command window:
Install specific packages, e.g., "GenomicFeatures" and "AnnotationDbi", with
biocLite() function (in the BiocInstaller package installed by
biocLite.R script) has arguments that change its default
?biocLite for further help.
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Bioconductor packages, especially those in the development branch, are updated fairly regularly. To identify packages requiring update within your version of Bioconductor, start a new session of R and enter
source("http://bioconductor.org/biocLite.R") biocLite() ## R version 3.0 or later
Use the argument
ask=FALSE to update old packages without being
prompted. For older versions of
R, use the command
biocLite(NULL). Read the help page for
?biocLite for additional
Some versions of R support more than one version of Bioconductor. To use the latest version of Bioconductor for your version of R, enter
source("http://bioconductor.org/biocLite.R") biocLite("BiocUpgrade") ## R version 2.15 or later
Read the help page for
?BiocUpgrade for additional details. Remember
that more recent versions of Bioconductor may be available if your
version of R is out-of-date.
Rarely, underlying changes in the operating system require ALL installed packages to be recompiled for source (C or Fortran) compatibility. One way to address this might be to start a new R session and enter
source("http://bioconductor.org/biocLite.R") pkgs <- rownames(installed.packages()) biocLite(pkgs, type="source")
As this will reinstall all currently installed packages, it likely involves a significant amount of network bandwidth and compilation time. All packages are implicitly updated, and the cumulative effect might introduce wrinkles that disrupt your work flow. It also requires that you have the necessary compilers installed.
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Use the commands
library(BiocInstaller) biocValid() ## R version 3.0 or later
to flag packages that are either out-of-date or too new for your
version of Bioconductor. The output suggests ways to solve identified
problems, and the help page
?biocValid lists arguments influencing
the behavior of the function.
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biocLite() is the recommended way to install Bioconductor
packages. There are several reasons for preferring this to the
'standard' way in which R pacakges are installed via
Bioconductor has a repository and release schedule that differs from R (Bioconductor has a 'devel' branch to which new packages and updates are introduced, and a stable 'release' branch emitted once every 6 months to which bug fixes but not new features are introduced).
A consequences of the mismatch between R and Bioconductor release
schedules is that the Bioconductor version identified by
install.packages() is sometimes not the most recent 'release'
available. For instance, an R minor version may be introduced some
months before the next Bioc release. After the Bioc release the users
of the R minor version will be pointed to an out-of-date version of
A consequence of the distinct 'devel' branch is that
install.packages() sometimes points only to the 'release'
repository, whereas Bioconductor developers and users wanting
leading-edge features wish to access the Bioconductor 'devel'
repository. For instance, the Bioconductor 3.0 release is available
for R.3.1.x, so Bioconductor developers and leading-edge users need to
be able to install the devel version of Bioconductor packages into the
same version (though perhaps different instance or at least library
location) of R that supports version 2.14 of Bioconductor.
An indirect consequence of Bioconductor's structured release is that packages generally have more extensive dependencies with one another, both explicitly via the usual package mechanisms and implicitly because the repository, release structure, and Bioconductor community interactions favor re-use of data representations and analysis concepts across packages. There is thus a higher premium on knowing that packages are from the same release, and that all packages are current within the release.
These days, the main purpose of
source("http://bioconductor.org/biocLite.R") is to install and
attach the 'BiocInstaller' package.
In a new installation, the script installs the most recent version of the BiocInstaller package relevant to the version of R in use, regardless of the relative times of R and Bioconductor release cycles. The BiocInstaller package serves as the primary way to identify the version of Bioconductor in use
> library(BiocInstaller) Bioconductor version 2.14 (BiocInstaller 1.14.2), ?biocLite for help
Since new features are often appealing to users, but at the same time require an updated version of Bioconductor, the source() command evaluated in an out-of-date R will nudge users to upgrade, e.g., in R-2.15.3
> source("http://bioconductor.org/biocLite.R") A new version of Bioconductor is available after installing the most recent version of R; see http://bioconductor.org/install
biocLite() function is provided by BiocInstaller. This is a
install.packages, but with the repository chosen
according to the version of Bioconductor in use, rather than to the
version relevant at the time of the release of R.
biocLite() also nudges users to remain current within a release, by
default checking for out-of-date packages and asking if the user would
like to update
> biocLite() BioC_mirror: http://bioconductor.org Using Bioconductor version 2.14 (BiocInstaller 1.14.2), R version 3.1.0. Old packages: 'BBmisc', 'genefilter', 'GenomicAlignments', 'GenomicRanges', 'IRanges', 'MASS', 'reshape2', 'Rgraphviz', 'RJSONIO', 'rtracklayer' Update all/some/none? [a/s/n]:
The BiocInstaller package provides facilities for switching to the 'devel' version of Bioconductor
> BiocInstaller::useDevel() Installing package into ‘/home/mtmorgan/R/x86_64-unknown-linux-gnu-library/3.1’ (as ‘lib’ is unspecified) trying URL 'http://bioconductor.org/packages/3.0/bioc/src/contrib/BiocInstaller_1.15.5.tar.gz' Content type 'application/x-gzip' length 14144 bytes (13 Kb) opened URL ================================================== downloaded 13 Kb * installing *source* package ‘BiocInstaller’ ... ... Bioconductor version 3.0 (BiocInstaller 1.15.5), ?biocLite for help 'BiocInstaller' changed to version 1.15.5
(at some points in the R / Bioconductor release cycle use of 'devel'
requires use of a different version of R itself, in which case the
useDevel() fails with an appropriate message).
The BiocInstaller package also provides
biocValid() to test that the
installed packages are not a hodgepodge from different Bioconductor
releases (the 'too new' packages have been installed from source
rather than a repository; regular users would seldom have these).
> biocValid() * sessionInfo() R version 3.1.0 Patched (2014-05-06 r65533) Platform: x86_64-unknown-linux-gnu (64-bit) ... * Out-of-date packages ... update with biocLite() * Packages too new for Bioconductor version '3.0' ... downgrade with biocLite(c("ShortRead", "BatchJobs")) Error: 9 package(s) out of date; 2 package(s) too new
For users who spend a lot of time in Bioconductor, the features
outlined above become increasingly important and
biocLite() is much
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Bioconductor Release »
Packages in the stable, semi-annual release:
Common Bioconductor workflows include: