ZINBA, is a statistical pipeline characterized by its flexibility to process recovered signals with differential characteristics [131]. Following data preprocessing, the algorithm classifies genomic regions as background, enriched or zero-inflated using a mixture regression model, without a priori knowledge of genomic enrichment. In turn, identified proximal enriched regions are combined within a defined distance using the broad setting, and the shape-detection algorithm is implemented to discover sharp signals within broader areas of enrichment. The advantage of ZINBA is that it can accurately identify enriched regions in the absence of an input control. In addition, the software uses a priori or modeled covariate information (for example G/C content) to represent signal components, which improves detection accuracy especially when the signal-to-noise ratio is low or in analysis of complex datasets (for example DNA copy number amplifications). MACS a model-based analysis algorithm with wide applicability for the analysis of ChIP-seq data [138–140], has also been effectively applied for DHS detection. The algorithm empirically models the shift size of sequence reads, and employs a Poisson distribution as a background model to capture local biases attributed to inherent differential sequencing and mapping genomic properties.
