With the development of single‐cell RNA sequencing technology (scRNA‐seq), we have the ability to study biological questions at the level of the individual cell transcriptome. Nowadays, many analysis tools, specifically suitable for single‐cell RNA sequencing data, have been developed. In this review, the currently commonly used scRNA‐seq protocols are discussed. The upstream processing flow pipeline of scRNA‐seq data, including goals and popular tools for reads mapping and expression quantification, quality control, normalization, imputation, and batch effect removal is also introduced. Finally, methods to evaluate these tools in both cellular and genetic dimensions, clustering and differential expression analysis are presented.

The global dynamics in a variety of biological processes can be revealed by mapping transcriptional m 6 A sites, in particular full-transcriptome m 6 A. And individual m 6 A sites have contributed to biological function, which can be evaluated by stoichiometric information obtained from the single nucleotide resolution. Currently, the identification of m 6 A sites is mainly carried out by experiment and prediction methods, based on high-throughput sequencing and machine learning model respectively. This review summarizes the recent topics and progress made in bioinformatics methods of deciphering the m 6 A methylation, including the experimental detection of m 6 A methylation sites, techniques of data analysis, the way of predicting m 6 A methylation sites, m 6 A methylation databases, and detection of m 6 A modification in circRNA. At the end, the essay makes a brief discussion for the development perspective in this area.

In recent years, the emergence and development of single-cell sequencing technologies have provided unprecedented opportunities to analyze deoxyribonucleic acid, ribonucleic acid and proteins at single-cell resolution. The advancements and reduced costs of high-throughput technologies allow for parallel sequencing of multiple molecular layers from a single cell, providing a comprehensive insight into the biological state and behavioral mechanisms of cells through the integration of genomics, transcriptomics, epigenomics and proteomics information. Researchers are actively working to further improve the cost-effectiveness, stability and high-throughput capabilities of single-cell multi-omics sequencing technologies and exploring their potential in precision medicine through clinical diagnostics. This review aims to survey the cutting-edge advancements in single-cell multi-omics sequencing, summarizing the representative technologies and their applications in profiling complex diseases, with a particular focus on tumors.