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.