SLAM combines the power of spline modeling and maximization of a mixture model to estimate methylation percentages that are analogous to bisulfite sequecing results.

SLAM is packaged for windows users with a user-friendly graphical user interface.  It also exists in a command-line python built for MacOS, UNIX, and Linux users.

SLAM was created for use with Agilent tiling arrays.  Data created from Nimblegen arrays can be adapted for SLAM using a script written in R.

Correspondence regarding SLAM can be directed to: timbahr@gmail.com

Developers:

Timothy M. Bahr, Dept. of Statistics, Brigham Young University, Provo, UT.

W. Evan Johnson, Dept. of Statistics, Brigham Young University, Provo, UT.

Spencer Clark, Dept. of Computer Science, Brigham Young University, Provo, UT.