With the dramatic development of single-cell RNA sequencing (scRNA-seq) technologies, the systematic decoding of cell-cell communication has received great research interest. To date, several in-silico methods have been developed, but most of them lack the ability to predict the communication pathways connecting the insides and outsides of cells. Here, we developed CellCall, a toolkit to infer inter- and intracellular communication pathways by integrating paired ligand-receptor and transcription factor (TF) activity. Moreover, CellCall uses an embedded pathway activity analysis method to identify the significantly activated pathways involved in intercellular crosstalk between certain cell types. Additionally, CellCall offers a rich suite of visualization options (Circos plot, Sankey plot, bubble plot, ridge plot, etc.) to present the analysis results. Case studies on scRNA-seq datasets of human testicular cells and the tumor immune microenvironment demonstrated the reliable and unique functionality of CellCall in intercellular communication analysis and internal TF activity exploration, which were further validated experimentally. Comparative analysis of CellCall and other tools indicated that CellCall was more accurate and offered more functions. In summary, CellCall provides a sophisticated and practical tool allowing researchers to decipher intercellular communication and related internal regulatory signals based on scRNA-seq data. CellCall is freely available at https://github.com/ShellyCoder/cellcall.

Establishing an RNA-associated interaction repository facilitates the system-level understanding of RNA functions. However, as these interactions are distributed throughout various resources, an essential prerequisite for effectively applying these data requires that they are deposited together and annotated with confidence scores. Hence, we have updated the RNA-associated interaction database RNAInter (RNA Interactome Database) to version 4.0, which is freely accessible at http://www.rnainter.org or http://www.rna-society.org/rnainter/. Compared with previous versions, the current RNAInter not only contains an enlarged data set, but also an updated confidence scoring system. The merits of this 4.0 version can be summarized in the following points: (i) a redefined confidence scoring system as achieved by integrating the trust of experimental evidence, the trust of the scientific community and the types of tissues/cells, (ii) a redesigned fully functional database that enables for a more rapid retrieval and browsing of interactions via an upgraded user-friendly interface and (iii) an update of entries to >47 million by manually mining the literature and integrating six database resources with evidence from experimental and computational sources. Overall, RNAInter will provide a more comprehensive and readily accessible RNA interactome platform to investigate the regulatory landscape of cellular RNAs.

With the development of biotechnologies and computational prediction algorithms, the number of experimental and computational prediction RNA-associated interactions has grown rapidly in recent years. However, diverse RNA-associated interactions are scattered over a wide variety of resources and organisms, whereas a fully comprehensive view of diverse RNA-associated interactions is still not available for any species. Hence, we have updated the RAID database to version 2.0 (RAID v2.0, www.rna-society.org/raid/) by integrating experimental and computational prediction interactions from manually reading literature and other database resources under one common framework. The new developments in RAID v2.0 include (i) over 850-fold RNA-associated interactions, an enhancement compared to the previous version; (ii) numerous resources integrated with experimental or computational prediction evidence for each RNA-associated interaction; (iii) a reliability assessment for each RNA-associated interaction based on an integrative confidence score; and (iv) an increase of species coverage to 60. Consequently, RAID v2.0 recruits more than 5.27 million RNA-associated interactions, including more than 4 million RNA–RNA interactions and more than 1.2 million RNA–protein interactions, referring to nearly 130 000 RNA/protein symbols across 60 species.

Research on RNA-associated interactions has exploded in recent years, and increasing numbers of studies are not limited to RNA–RNA and RNA–protein interactions but also include RNA–DNA/compound interactions. To facilitate the development of the interactome and promote understanding of the biological functions and molecular mechanisms of RNA, we updated RAID v2.0 to RNAInter (RNA Interactome Database), a repository for RNA-associated interactions that is freely accessible at http://www.rna-society.org/rnainter/ or http://www.rna-society.org/raid/. Compared to RAID v2.0, new features in RNAInter include (i) 8-fold more interaction data and 94 additional species; (ii) more definite annotations organized, including RNA editing/localization/modification/structure and homology interaction; (iii) advanced functions including fuzzy/batch search, interaction network and RNA dynamic expression and (iv) four embedded RNA interactome tools: RIscoper, IntaRNA, PRIdictor and DeepBind. Consequently, RNAInter contains >41 million RNA-associated interaction entries, involving more than 450 thousand unique molecules, including RNA, protein, DNA and compound. Overall, RNAInter provides a comprehensive RNA interactome resource for researchers and paves the way to investigate the regulatory landscape of cellular RNAs.

Increasing evidence has revealed that RNA subcellular localization is a very important feature for deeply understanding RNA's biological functions after being transported into intra- or extra-cellular regions. RNALocate is a web-accessible database that aims to provide a high-quality RNA subcellular localization resource and facilitate future researches on RNA function or structure. The current version of RNALocate documents more than 37 700 manually curated RNA subcellular localization entries with experimental evidence, involving more than 21 800 RNAs with 42 subcellular localizations in 65 species, mainly including Homo sapiens, Mus musculus and Saccharomyces cerevisiae etc. Besides, RNA homology, sequence and interaction data have also been integrated into RNALocate. Users can access these data through online search, browse, blast and visualization tools. In conclusion, RNALocate will be of help in elucidating the entirety of RNA subcellular localization, and developing new prediction methods. The database is available at http://www.rna-society.org/rnalocate/.

Resolving the spatial distribution of the transcriptome at a subcellular level can increase our understanding of biology and diseases. To facilitate studies of biological functions and molecular mechanisms in the transcriptome, we updated RNALocate, a resource for RNA subcellular localization analysis that is freely accessible at http://www.rnalocate.org/ or http://www.rna-society.org/rnalocate/. Compared to RNALocate v1.0, the new features in version 2.0 include (i) expansion of the data sources and the coverage of species; (ii) incorporation and integration of RNA-seq datasets containing information about subcellular localization; (iii) addition and reorganization of RNA information (RNA subcellular localization conditions and descriptive figures for method, RNA homology information, RNA interaction and ncRNA disease information) and (iv) three additional prediction tools: DM3Loc, iLoc-lncRNA and iLoc-mRNA. Overall, RNALocate v2.0 provides a comprehensive RNA subcellular localization resource for researchers to deconvolute the highly complex architecture of the cell.