marge /

Filename Size Date modified Message
R
data
data-raw
docs
exec
inst/extdata
man
tests
vignettes
133 B
homogenized known and denovo results, added a new parsing function for subfields of motif name and experiment
38 B
more small tweaks for 0.0.2, first version of vignette done
846 B
added onAttach and better HOMER install parsing
54 B
small tweaks for CRAN
872 B
tweak exports mistake from utils.R
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v0.0.3_carl
3.1 KB
added onAttach and better HOMER install parsing
711.2 KB
v0.0.3_carl
3.2 KB
README.md edited online with Bitbucket
167 B
added onAttach and better HOMER install parsing
1.1 MB
added onAttach and better HOMER install parsing

NOTE

marge has migrated to Github! Check it out at: https://github.com/robertamezquita/marge

marge <img src="man/figures/Marge_Simpson.png" align="right" height=205 width=132 />

Overview

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

Installing 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.

Installing marge

To install the latest development version of marge, navigate to the marge bitbucket downloads page to download and build, or simply do:

devtools::install_bitbucket('robert_amezquita/marge', ref = 'master')

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).

Usage

marge is currently in semi-active development, the package currently includes the ability to:

  • Run a motif analysis: find_motifs_genome() - runs the HOMER script findMotifsGenome.pl via R, and outputs a results directory in the default HOMER style
  • Read in results: read_*_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 motifs in HOMER compatible format with write_homer_motif()
  • Find specific motif instances across regions using supplied PWMs with find_motifs_instances() and read in the results with read_motifs_instances()
  • Access the HOMER database of known motifs by inspecting the HOMER_motifs object

Further details can be found in the associated vignette, describing installation and typical workflows encompassing basic/advanced usage schemas.

Compared to HOMER alone

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.


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