Single-cell RNA sequencing (scRNA-seq) technologies are poised to reshape the current cell-type classification system. However, a transcriptome-based single-cell atlas has not been achieved for complex mammalian systems. Here, we developed Microwell-seq, a high-throughput and low-cost scRNA-seq platform using simple, inexpensive devices. Using Microwell-seq, we analyzed more than 400,000 single cells covering all of the major mouse organs and constructed a basic scheme for a mouse cell atlas (MCA). We reveal a single-cell hierarchy for many tissues that have not been well characterized previously. We built a web-based "single-cell MCA analysis" pipeline that accurately defines cell types based on single-cell digital expression. Our study demonstrates the wide applicability of the Microwell-seq technology and MCA resource.

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It has come to our attention that in preparing the final version of this paper, we inadvertently misspelled the first name of an author Ziming Zhou as ''Zimin Zhou''. In addition, we have made two errors in describing the reagents in the STAR Methods. First, under the subheading of ''Synthesis of barcoded beads'' in the Method Details section, the supplier of the magnetic beads coated with carboxyl groups should be Suzhou Knowledge & Benefit Sphere Tech. Co., Ltd. (diameter 20-25 mm, http://www.kbspheretech. com/), instead of Zhiyi. Second, under the subheading of ''Cell collection and lysis'' in the Method Details section, the concentration of Tris-HCL for the cold lysis buffer should be 0.1 M, instead of 1 M. These errors have been corrected online, and we apologize for any confusions we may have caused.

Insights into the circular RNA (circRNA) exploration have revealed that they are abundant in eukaryotic transcriptomes. Diverse genomic regions can generate different types of RNA circles, implying their diversity. Covalently closed loop structures elevate the stability of this new type of noncoding RNA. High-throughput sequencing analyses suggest that circRNAs exhibit tissue- and developmental-specific expression, indicating that they may play crucial roles in multiple cellular processes. Strikingly, several circRNAs could function as microRNA sponges and regulate gene transcription, highlighting a new class of important regulators. Here, we review the recent advances in knowledge of endogenous circRNA biogenesis, properties and functions. We further discuss the current findings about circRNAs in human diseases. In plants, the roles of circRNAs remain a mystery. Online resources and bioinformatics identification of circRNAs are essential for the analysis of circRNA biology, although different strategies yield divergent results. The understanding of circRNA functions remains limited; however, circRNAs are enriching the RNA world, acting as an emerging key player.

Motivation Protein-protein interaction (PPI), as a relative property, is determined by two binding proteins, which brings a great challenge to design an expert model with an unbiased learning architecture and a superior generalization performance. Additionally, few efforts have been made to allow PPI predictors to discriminate between relative properties and intrinsic properties. Results We present a sequence-based approach, DeepTrio, for PPI prediction using mask multiple parallel convolutional neural networks. Experimental evaluations show that DeepTrio achieves a better performance over several state-of-the-art methods in terms of various quality metrics. Besides, DeepTrio is extended to provide additional insights into the contribution of each input neuron to the prediction results. Availability We provide an online application at http://bis.zju.edu.cn/deeptrio. The DeepTrio models and training data are deposited at https://github.com/huxiaoti/deeptrio.git. Supplementary information Supplementary data are available at Bioinformatics online.

Individual cells are basic units of life. Despite extensive efforts to characterize the cellular heterogeneity of different organisms, cross-species comparisons of landscape dynamics have not been achieved. Here, we applied single-cell RNA sequencing (scRNA-seq) to map organism-level cell landscapes at multiple life stages for mice, zebrafish and Drosophila. By integrating the comprehensive dataset of > 2.6 million single cells, we constructed a cross-species cell landscape and identified signatures and common pathways that changed throughout the life span. We identified structural inflammation and mitochondrial dysfunction as the most common hallmarks of organism aging, and found that pharmacological activation of mitochondrial metabolism alleviated aging phenotypes in mice. The cross-species cell landscape with other published datasets were stored in an integrated online portal—Cell Landscape. Our work provides a valuable resource for studying lineage development, maturation and aging.

Circular RNA (circRNA) is a novel type of endogenous noncoding RNA with covalently closed loop structures, which are widely expressed in various tissues and have functional implications in cellular processes. Acting as competing endogenous RNAs (ceRNAs), circRNAs are important regulators of miRNA activities. The identification of these circRNAs underlines the increasing complexity of ncRNA-mediated regulatory networks. However, more biological evidence is required to infer direct circRNA–miRNA associations while little attention has been paid to circRNAs in plants as compared to the abundant research in mammals. PlantCircNet is presented as an integrated database that provides visualized plant circRNA–miRNA–mRNA regulatory networks containing identified circRNAs in eight model plants. The bioinformatics integration of data from multiple sources reveals circRNA–miRNA–mRNA regulatory networks and helps identify mechanisms underlying metabolic effects of circRNAs. An enrichment analysis tool was implemented to detect significantly overrepresented Gene Ontology categories of miRNA targets. The genomic annotations, sequences and isoforms of circRNAs were also investigated. PlantCircNet provides a user-friendly interface for querying detailed information of specific plant circRNAs. The database may serve as a resource to facilitate plant circRNA research. Several circRNAs were identified to play potential regulatory roles in flower development and response to environmental stress from regulatory networks related with miR156a and AT5G59720, respectively. This present research indicated that circRNAs could be involved in diverse biological processes. Database URL: http://bis.zju.edu.cn/plantcircnet/index.php

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