Single-cell RNA sequencing (scRNA-seq) technologies allow the dissection of gene expression at single-cell resolution, which greatly revolutionizes transcriptomic studies. A number of scRNA-seq protocols have been developed, and these methods possess their unique features with distinct advantages and disadvantages. Due to technical limitations and biological factors, scRNA-seq data are noisier and more complex than bulk RNA-seq data. The high variability of scRNA-seq data raises computational challenges in data analysis. Although an increasing number of bioinformatics methods are proposed for analyzing and interpreting scRNA-seq data, novel algorithms are required to ensure the accuracy and reproducibility of results. In this review, we provide an overview of currently available single-cell isolation protocols and scRNA-seq technologies, and discuss the methods for diverse scRNA-seq data analyses including quality control, read mapping, gene expression quantification, batch effect correction, normalization, imputation, dimensionality reduction, feature selection, cell clustering, trajectory inference, differential expression calling, alternative splicing, allelic expression, and gene regulatory network reconstruction. Further, we outline the prospective development and applications of scRNA-seq technologies.

DNA methylation status is closely associated with diverse diseases, and is generally more stable than gene expression, thus abnormal DNA methylation could be important biomarkers for tumor diagnosis, treatment and prognosis. However, the signatures regarding DNA methylation changes for pan-cancer diagnosis and prognosis are less explored. Here we systematically analyzed the genome-wide DNA methylation patterns in diverse TCGA cancers with machine learning. We identified seven CpG sites that could effectively discriminate tumor samples from adjacent normal tissue samples for 12 main cancers of TCGA (1216 samples, AUC > 0.99). Those seven potential diagnostic biomarkers were further validated in the other 9 different TCGA cancers and 4 independent datasets (AUC > 0.92). Three out of the seven CpG sites were correlated with cell division, DNA replication and cell cycle. We also identified 12 CpG sites that can effectively distinguish 26 different cancers (7605 samples), and the result was repeatable in independent datasets as well as two disparate tumors with metastases (micro-average AUC > 0.89). Furthermore, a series of potential signatures that could significantly predict the prognosis of tumor patients for 7 different cancer were identified via survival analysis (p-value < 1e-4). Collectively, DNA methylation patterns vary greatly between tumor and adjacent normal tissues, as well as among different types of cancers. Our identified signatures may aid the decision of clinical diagnosis and prognosis for pan-cancer and the potential cancer-specific biomarkers could be used to predict the primary site of metastatic breast and prostate cancers.

Gene regulatory networks (GRNs) formed by transcription factors (TFs) and their downstream target genes play essential roles in gene expression regulation. Moreover, GRNs can be dynamic changing across different conditions, which are crucial for understanding the underlying mechanisms of disease pathogenesis. However, no existing database provides comprehensive GRN information for various human and mouse normal tissues and diseases at the single-cell level. Based on the known TF-target relationships and the large-scale single-cell RNA-seq data collected from public databases as well as the bulk data of The Cancer Genome Atlas and the Genotype-Tissue Expression project, we systematically predicted the GRNs of 184 different physiological and pathological conditions of human and mouse involving >633 000 cells and >27 700 bulk samples. We further developed GRNdb, a freely accessible and user-friendly database (http://www.grndb.com/) for searching, comparing, browsing, visualizing, and downloading the predicted information of 77 746 GRNs, 19 687 841 TF-target pairs, and related binding motifs at single-cell/bulk resolution. GRNdb also allows users to explore the gene expression profile, correlations, and the associations between expression levels and the patient survival of diverse cancers. Overall, GRNdb provides a valuable and timely resource to the scientific community to elucidate the functions and mechanisms of gene expression regulation in various conditions.

Multi-omics allows the systematic understanding of the information flow across different omics layers, while single omics can mainly reflect one aspect of the biological system. The advancement of bulk and single-cell sequencing technologies and related computational methods for multi-omics largely facilitated the development of system biology and precision medicine. Single-cell approaches have the advantage of dissecting cellular dynamics and heterogeneity, whereas traditional bulk technologies are limited to individual/population-level investigation. In this review, we first summarize the technologies for producing bulk and single-cell multi-omics data. Then, we survey the computational approaches for integrative analysis of bulk and single-cell multimodal data, respectively. Moreover, the databases and data storage for multi-omics, as well as the tools for visualizing multimodal data are summarized. We also outline the integration between bulk and single-cell data, and discuss the applications of multi-omics in precision medicine. Finally, we present the challenges and perspectives for multi-omics development.

Leaf growth and senescence are controlled by tight genetic factors involved regulation at multiple levels. Circular RNAs (circRNAs) have recently been reported as the microRNA sponge to accomplish corresponding regulatory roles. This study aims to explore the expression profile and functional role of circRNAs in Arabidopsis leaf growth and senescence. We used publically available RNA-seq data of Arabidopsis leaves to identify the circular RNA expression profile and used quantitative real-time PCR to validate our identified circRNAs. The functions of circRNAs were explored using distinct bioinformatics methods including analysis of network, gene ontology and KEGG pathway. We identified 168 circRNAs, including 40 novel circRNAs, in Arabidopsis thaliana leaves, with 158 (94.1%) circRNAs arising from the exons of genes. Real-time PCRs were used to verify 4 highly expressed circRNAs and they all showed consistent expression patterns with the RNA-seq results. Interestingly, 6 and 35 circRNAs were differentially expressed at G- to -M stage and M- to -S stage, respectively. The circRNAs display an upregulation trend during the lifespan of Arabidopsis leaves. Moreover, the expression of circRNAs during senescence is independent of host gene expression to a certain degree. The gene ontology (GO) and KEGG pathway analysis of the targeted mRNA of circRNA–miRNA–mRNA network showed that the circRNAs may be involved in plant hormone signal transduction, Porphyrin and chlorophyll metabolism during leaves senescence. Our comprehensive analysis of the expression profile of circRNAs and their potential functions during leaf growth and senescence suggest that circRNAs may function as new post-transcriptional regulators in the senescence of Arabidopsis leaves.