BACKGROUND: Single-cell technologies allow detailed mapping of cell type-specific regulatory and transcriptomic landscapes, yet a systematic way to quantify cell type specificity of chromatin accessibility and gene expression remains limited. SPICEY (SPecificity Index for Coding and Epigenetic activitY) is an R package that measures cell-type specificity from single cell multi-omic data. RESULTS: We developed SPICEY, an R package that combines differential and entropy-based metrics to measure cell-type specificity from annotated single-cell accessibility and gene expression data. It computes two indices: RETSI (Regulatory Element cell Type Specificity Index) for chromatin accessibility and GETSI (Gene Expression cell Type Specificity Index) for gene expression. When links between distal chromatin regions and target genes are provided, SPICEY integrates regulatory and transcriptional specificity scores. CONCLUSIONS: Applied to human pancreatic islet data, SPICEY identified cell-type-specific gene-regulatory pairs and regulatory features enriched in endocrine cells -including beta cells- providing a framework to dissect cell-type-specific regulatory mechanisms in health and disease.

Proliferating cells experience a global reduction of transcription during mitosis, yet their cell identity is maintained and regulatory information is propagated from mother to daughter cells. Mitotic bookmarking by transcription factors has been proposed as a potential mechanism to ensure the reactivation of transcription at the proper set of genes exiting mitosis. Recently, mitotic transcription and waves of transcription reactivation have been observed in synchronized populations of human hepatoma cells. However, the study did not consider that mitotic-arrested cell populations progressively desynchronize leading to measurements of gene expression on a mixture of cells at different internal cell-cycle times. Moreover, it is not well understood yet what is the precise role of mitotic bookmarking on mitotic transcription as well as on the transcription reactivation waves. Ultimately, the core gene regulatory network driving the precise transcription reactivation dynamics remains to be identified. To address these questions, we developed a mathematical model to correct for the progressive desynchronization of cells and estimate gene expression dynamics with respect to a cell-cycle pseudotime. Furthermore, we used a multiple linear regression model to infer transcription factor activity dynamics. Our analysis allows us to characterize waves of transcription factor activities exiting mitosis and predict a core gene regulatory network responsible of the transcription reactivation dynamics. Moreover, we identified more than 60 transcription factors that are highly active during mitosis and represent new candidates of mitotic bookmarking factors which could be relevant therapeutic targets to control cell proliferation.

MOTIVATION: Probabilistic approaches for inferring transcription factor binding sites (TFBSs) and regulatory motifs from DNA sequences have been developed for over two decades. Previous work has shown that prediction accuracy can be significantly improved by incorporating features such as the competition of multiple transcription factors (TFs) for binding to nearby sites, the tendency of TFBSs for co-regulated TFs to cluster and form cis-regulatory modules and explicit evolutionary modeling of conservation of TFBSs across orthologous sequences. However, currently available tools only incorporate some of these features, and significant methodological hurdles hampered their synthesis into a single consistent probabilistic framework. RESULTS: We present MotEvo, a integrated suite of Bayesian probabilistic methods for the prediction of TFBSs and inference of regulatory motifs from multiple alignments of phylogenetically related DNA sequences, which incorporates all features just mentioned. In addition, MotEvo incorporates a novel model for detecting unknown functional elements that are under evolutionary constraint, and a new robust model for treating gain and loss of TFBSs along a phylogeny. Rigorous benchmarking tests on ChIP-seq datasets show that MotEvo's novel features significantly improve the accuracy of TFBS prediction, motif inference and enhancer prediction. AVAILABILITY: Source code, a user manual and files with several example applications are available at www.swissregulon.unibas.ch.