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Summary Emerging single-cell technologies (e.g. single-cell ATAC-seq, DNase-seq or ChIP-seq) have made it possible to assay regulome of individual cells. Single-cell regulome data are highly sparse and discrete. Analyzing such data is challenging. User-friendly software tools are still lacking. We present SCRAT, a Single-Cell Regulome Analysis Toolbox with a graphical user interface, for studying cell heterogeneity using single-cell regulome data. SCRAT can be used to conveniently summarize regulatory activities according to different features (e.g. gene sets, transcription factor binding motif sites, etc.). Using these features, users can identify cell subpopulations in a heterogeneous biological sample, infer cell identities of each subpopulation, and discover distinguishing features such as gene sets and transcription factors that show different activities among subpopulations. Availability and implementation SCRAT is freely available at https://zhiji.shinyapps.io/scrat as an online web service and at https://github.com/zji90/SCRAT as an R package. Supplementary information Supplementary data are available at Bioinformatics online.

Motivation: Protein–DNA interaction has significant importance in many biological processes. However, the underlying principle of the molecular recognition process is still largely unknown. As more high-resolution 3D structures of protein–DNA complex are becoming available, the surface characteristics of the complex become an important research topic. Result: In our work, we apply an alpha shape model to represent the surface structure of the protein–DNA complex and developed an interface-atom curvature-dependent conditional probability discriminatory function for the prediction of protein–DNA interaction. The interface-atom curvature-dependent formalism captures atomic interaction details better than the atomic distance-based method. The proposed method provides good performance in discriminating the native structures from the docking decoy sets, and outperforms the distance-dependent formalism in terms of the z-score. Computer experiment results show that the curvature-dependent formalism with the optimal parameters can achieve a native z-score of −8.17 in discriminating the native structure from the highest surface-complementarity scored decoy set and a native z-score of −7.38 in discriminating the native structure from the lowest RMSD decoy set. The interface-atom curvature-dependent formalism can also be used to predict apo version of DNA-binding proteins. These results suggest that the interface-atom curvature-dependent formalism has a good prediction capability for protein–DNA interactions. Availability: The code and data sets are available for download on http://www.hy8.com/bioinformatics.htm Contact: kenandzhou@hotmail.com