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Uncovering the protein–DNA interactions involved in cell fate, development, and disease in a timeand cell-specific manner is a fundamental goal of molecular biology. The advent of the sequencing technologies has opened a new genomic era, uncovering the information encoded in genomes, epigenomes, and transcriptomes (McPherson, 2014). For example, the popular ChIPbased techniques ChIP-seq (Johnson et al., 2007; Robertson et al., 2007) and ChIP-exo (Rhee and Pugh, 2011) are widely used to detect transcription factor (TF)-binding sites using an antibody against a single protein of interest (Mahony and Pugh, 2015). Alternative protocols assaying the chromatin landscape, such as those based ondigestion byDNase I enzyme (DNase-seq),micrococcal nuclease (MNase-seq), and Tn5 transposase attack (ATAC-seq), enable the identification of DNAbinding protein footprints of many TFs in a single experiment (Tsompana and Buck, 2014). Timeseries experiments might be required for the identification of those TFs cataloged as pioneer factors, allowing their effects on chromatin to be investigated (Zaret and Carroll, 2011; Pajoro et al., 2014; Sherwood et al., 2014). Despite the initial promise of detecting the majority of TFs in one assay, DNA sequencespecific biases, together with TF-dependent binding kinetics, have been recently pinpointed as major confounding factors in DNase-seq experiments (Koohy et al., 2013; He et al., 2014; Raj and McVicker, 2014; Rusk, 2014; Sung et al., 2014). These influencing factors were not considered by any of the previous computational approaches for the analysis of next-generation sequencing chromatin accessibility data (Madrigal and Krajewski, 2012); neither those strategies based on TFgeneric DNase signature nor those based on TF-specific DNase signature (Luo and Hartemink, 2013).

On Accounting for Sequence-Specific Bias in Genome-Wide Chromatin Accessibility Experiments: Recent Advances and Contradictions