G4 biophysics database visualization and management
In R (e.g. the console of RStudio), run:
install.packages("devtools")
devtools::install_github('EricLarG4/g4dbr')
You may update some or all of the packages that were already installed, or skip this step.
Restart your R session before use.
To use g4db, run:
library(g4dbr)
g4db()
For more details, consult the package (HTML vignette) and function documentation using help(package = 'g4dbr'). The vignette is also available in pdf here.
To be able to generate reports in pdf (Word and HTML reports are possible out of the box), tinytex (a lightweight LaTeX distribution) must be installed.
Install the package with install.packages('tinytex'), then finish the installation using tinytex::install_tinytex().
Restart your IDE and verify that tinytex:::is_tinytex() is TRUE.
In case of issue, check the help page.
Local file system. An example database (demo_database.Rda), an empty database (empty_database.Rda), and a demo input file (demo_input.xlsx) are located in the extdata subfolder of your package installation path.
To locate these files, use system.file("extdata/", package = 'g4dbr') in R. On Windows, the output should be something like C:\Users\username\Documents\R\win-library\X.Y\g4dbr.
These files will be overridden if the package is re-installed, and removed is the package in uninstalled. Do not save files at this location
From source zip. The zip file contains the example database (demo_database.Rda), empty database (empty_database.Rda), and demo input file (demo_input.xlsx) in the inst/extdata subfolder.
Use
To use the demo files, load them in the g4db() app.
To use epsilon.calculator, run:
library(g4dbr)
epsilon.calculator("SEQUENCE")
where SEQUENCE is the DNA sequence of choice.
This tool can be ran from inside the g4db app. To use as a standalone function mass.diet, prepare your data in a data frame containing the following columns:
mz, the m/z axis,int, the intensity,oligo, the oligonucleotide names,buffer.id, the buffer name,tune, the MS tune name,rep, the replicate number
Then run:
library(g4dbr)
mass.diet(fat.mass = data.to.reduce,
base.start, base.end,
range.start, range.end,
baseline.int)
Where data.to.reduce is the dataframe prepared at the previous step, the m/z range to keep is given by range.start and range.end, the baseline for noise calculation with base.start and base.end, and the threshold coefficient for noise removal is specified with baseline.int.
This tool can be ran from inside the g4db app. To use as a standalone function database.eraser, run:
library(g4dbr)
database.eraser(db.to.erase,
remove.oligos,
erase.CD, erase.NMR, erase.MS, erase.UV)
Where db.to.erase is an .Rda file prepared with g4db, remove oligos is a vector containing the oligo names for which data must be removed, and erase.CD, erase.NMR, erase.MS and erase.UV are logical values indicating whether to remove data from the corresponging techniques (respectively circular dichroism, ^1^H-NMR, mass spectrometry, and UV-melting).
GPL-3 Eric Largy