A computational model for correcting intrinsic enzymatic biases in chromatin accessibility profiling data

Genome-wide profiling of chromatin accessibility using high-throughput sequencing-based techniques such as ATAC-seq (with Tn5 transposase) or DNase-seq (with DNase I) has been widely used for studying regulatory DNA elements and transcriptional regulation. Enzymatic DNA cleavage exhibits intrinsic sequence biases that confound data analysis. We developed a simplex encoding-based mathematical model for accurate estimation of such sequence biases. Here we present a computational method for correcting intrinsic biases from both bulk and single-cell sequencing data. We show that bias correction can improve transcription factor binding prediction from DNase footprints and can generate more accurate cell clustering from single-cell ATAC-seq data. This work demonstrates that innovative quantitative modeling can enhance conventional bioinformatics analysis and extract biologically meaningful information from complicated high-throughput data.