MOTIVATION: The increasing amount of time-series single-cell RNA sequencing (scRNA-seq) data raises the key issue of connecting cell states (i.e. cell clusters or cell types) to obtain the continuous temporal dynamics of transcription, which can highlight the unified biological mechanisms involved in cell state transitions. However, most existing trajectory methods are specifically designed for individual cells, so they can hardly meet the needs of accurately inferring the trajectory topology of the cell state, which usually contains cells assigned to different branches. RESULTS: Here, we present CStreet, a computed Cell State trajectory inference method for time-series scRNA-seq data. It uses time-series information to construct the k-nearest neighbor connections between cells within each time point and between adjacent time points. Then, CStreet estimates the connection probabilities of the cell states and visualizes the trajectory, which may include multiple starting points and paths, using a force-directed graph. By comparing the performance of CStreet with that of six commonly used cell state trajectory reconstruction methods on simulated data and real data, we demonstrate the high accuracy and high tolerance of CStreet. AVAILABILITY AND IMPLEMENTATION: CStreet is written in Python and freely available on the web at https://github.com/TongjiZhanglab/CStreet and https://doi.org/10.5281/zenodo.4483205. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Germline cells are important carriers of genetic and epigenetic information transmitted across generations in mammals. During the mammalian germline cell development cycle (i.e., the germline cycle), cell potency changes cyclically, accompanied by dynamic transcriptional changes and epigenetic reprogramming. Recently, to understand these dynamic and regulatory mechanisms, multiomic analyses, including transcriptomic and epigenomic analyses of DNA methylation, chromatin accessibility and histone modifications of germline cells, have been performed for different stages in human and mouse germline cycles. However, the long time span of the germline cycle and material scarcity of germline cells have largely limited the understanding of these dynamic characteristic changes. A tool that integrates the existing multiomics data and visualizes the overall continuous dynamic trends in the germline cycle can partially overcome such limitations. RESULTS: Here, we present GLEANER, a web server for GermLine cycle Expression ANalysis and Epigenetics Roadmap visualization. GLEANER provides a comprehensive collection of the transcriptome, DNA methylome, chromatin accessibility, and H3K4me3, H3K27me3, and H3K9me3 histone modification characteristics in human and mouse germline cycles. For each input gene, GLEANER shows the integrative analysis results of its transcriptional and epigenetic features, the genes with correlated transcriptional changes, and the overall continuous dynamic trends in the germline cycle. We further used two case studies to demonstrate the detailed functionality of GLEANER and highlighted that it can provide valuable clues to the epigenetic regulation mechanisms in the genetic and epigenetic information transmitted during the germline cycle. CONCLUSIONS: To the best of our knowledge, GLEANER is the first web server dedicated to the analysis and visualization of multiomics data related to the mammalian germline cycle. GLEANER is freely available at http://compbio-zhanglab.org/GLEANER .