Fig. 5: Construction and functionalities of the SCMD database for single-cell m⁶A data.

A Schematic workflow illustrating the construction of the SCMD website. The process involves integrating data from multiple single-cell m⁶A sequencing and prediction methods, including Scm⁶A, scm⁶A-seq, sn-m6A-CT, and scDART-seq. The collected data is processed for UMAP visualizations, m⁶A site identification, and cell annotation, allowing users to explore m⁶A modifications at the single-cell level. Created in BioRender. Liu, Z. (2025) https://BioRender.com/3poviur. B Screenshot of the SCMD homepage, where users can select different single-cell m⁶A sequencing and prediction methods to analyze specific genes or datasets. The selection includes options like Scm⁶A, scm⁶A-seq, sn-m⁶A-CT, and scDART-seq. C Example output of the SCMD when using the Scm⁶A method to search for a specific gene. The table displays the single-cell m⁶A modification levels across different cancer types, and the bar chart below shows the variation in m⁶A modification levels across various cell types within those cancers. D Example output of SCMD when using the Scm⁶A method to search for specific diseases. The output table includes the location information of m⁶A modification sites associated with the disease and the modification levels in each cell type. t-SNE and UMAP plots display the distribution of m⁶A modifications across cells (cell types are automatically annotated using SingleR). E Download interface of the SCMD, where users can access datasets generated by different methods and download data for various diseases, such as AML, GBM, and BRCA. The interface allows for easy retrieval of m⁶A-related data for further analysis.