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
HOMER
First, running 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 ./configureHomer.pl
script.
marge
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, v0.0.3_carl
).
marge
is currently in semi-active development, the package currently includes the ability to:
find_motifs_genome()
- runs the HOMER
script findMotifsGenome.pl
via R, and outputs a results directory in the default HOMER
styleread_*_results()
- read in either denovo
or known
enriched motifs with the read_denovo_results()
or read_known_results()
functions, pointing to the HOMER
directory 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.write_homer_motif()
find_motifs_instances()
and read in the results with read_motifs_instances()
HOMER_motifs
objectFurther 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.