The aim of
marge is to provide an R API to
HOMER for the analysis of genomic data, utilizing a tidy framework to accelerate organization and visualization of analyses.
If here courtesy of Bitbucket, check out the docs at:
marge requires having a working installation of
HOMER on your computer. Please see the HOMER website for more information on installing and configuring
HOMER and to learn more about the methodology. In particular, note that you should install your desired genomes in addition to installing
HOMER using the
To install the latest development version of
marge, navigate to the
marge bitbucket downloads page to download and build, or simply do:
To install a stable version, simply navigate to the downloads page, navigate to the tab called “Tags”, and change the
ref argument from
master to your desired release (for example,
marge is currently in semi-active development, the package currently includes the ability to:
find_motifs_genome()- runs the
findMotifsGenome.plvia R, and outputs a results directory in the default
read_*_results()- read in either
knownenriched motifs with the
read_known_results()functions, pointing to the
HOMERdirectory that was created in the previous step. The
read_*functions produce tibbles summarizing the motif enrichment results into a tidy format for easier visualization and analysis. See the reference pages of each for more details.
find_motifs_instances()and read in the results with
Further details can be found in the associated vignette, describing installation and typical workflows encompassing basic/advanced usage schemas.
Like the actual Homer Simpson,
HOMER is made better with the addition of
marge. With the continually increasing throughput in conducting sequencing analysis,
marge provides a native R framework to work from end to end with motif analyses - from processing to storing to visualizing these results, all using modern tidy conventions.