The patient specific background mutation probabilities were estimated by fitting a logistic regression model on all genomic sites after masking CDS regions, immunoglobin loci, and poorly mappable regions. Replication timing was discretized into eight equally sized bins, the local mutation rate was discretized into ten equally sized bins, and the chromatin features and TF-binding profiles were binarized. P-value signal tracks of the histone modification profiles from the Roadmap Epigenomics were binarized using a cutoff of 10−4. ENCODE TF-binding profiles were binarized according to the presence of a peak in any cell line assayed. We performed logistic regression using the frequency table of the counts of mutated and non-mutated sites for each combination of the covariates. Separate logistic regression models were fit to estimate the background mutation probabilities of SNVs and indels. This is to account for the different mutational processes from which SNVs and indels arise, as well as the different uncertainties associated with SNV and indel calls.\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{glm}\left( {y\sim \mathrm{rep} + \mathrm{epi} + \mathrm{sequence} + \mathrm{pid},\,\mathrm{family} = \mathrm{logit}} \right)$$\end{document}glmy~rep+epi+sequence+pid,family=logit