Although it has been shown that the widely used change-point based methods can increase statistical power to identify variants, it remains challenging to effectively identify CNVs with weak signals due to the noisy nature of genotyping intensity data. modSaRa2 is a novel improvement of our previously developed method modified Screening and Ranking algorithm (modSaRa) by integrating the relative allelic intensity with prior empirical statistics. modSaRa2 markedly improved both sensitivity and specificity over existing methods. The improvement for detecting weak CNV signals is the most substantial, while simultaneously improving stability when CNV size varies.
To obtain and run modSaRa2 (Linux), please download the source package with the file name extension “.tar.gz” (modSaRa2_1.0.tar.gz) To be noted that the name extension of the downloaded packages may contain a series of characters (e.g., _1.0_344094_282_43380_v2) in the middle, which should be removed before installation.
Please follow the following steps in R:
1. In R, Enter the directory where .tar.gz package file is saved,
2. Type the R command: install.packages("modSaRa2_1.0.tar.gz",repos=NULL, type="source"),3. Type the R command: library(modSaRa2)
To obtain and run modSaRa2 (Windows), please download the zipped file(modSaRa2_1.0.zip), and follow the following steps:
1. Download and Install Rtools https://cran.r-project.org/bin/windows/Rtools/
2. Add the following path to the system path
3. Install Rcpp packages
4. Select "packages" on the top of R Gui, choose "install package from the local zip file," and select the "modSaRa2_1.0.zip" file.
5. Type the R command: library(modSaRa2).
For Mac, users need to have Xcode installed on the computer before installing modSaRa2. To that end, the following steps are also recommended for installing the .tar.gz file.
1. Go to http://hpc.sourceforge.net
2. Download the newest version of gcc-x.x-bin.tar.gz
3. Run “gunzip gcc-x.x-bin.tar.gz” in the Mac terminal (change the directory to where this package was saved first)
4. Run “sudo tar -xvf gcc-x.x-bin.tar -C /” in the Mac terminal
5. Run “install.packages(path_to_file, repos = NULL, type="source”)” in the Console window of RStudio or R, where path_to_file represents the full path and file name
Details of how to use the package for CNV analysis can be found in Reference Manual