The scDART-seq data used in the <b>Statistical modeling of single-cell epitranscriptomics enabled trajectory and regulatory inference of RNA methylation</b>

The scDART-seq data used in the study "Statistical Modeling of Single-Cell Epitranscriptomics Enabled Trajectory and Regulatory Inference of RNA Methylation" was obtained from both the SMART-seq2 and 10x Genomics platforms. For the SMART-seq2 dataset, the aim was to profile the m6A epitranscriptome in 1,382 HEK293T cells, consisting of 991 cells with m6A modifications (APOBEC1-YTH) and 391 negative control cells (APOBEC1-YTHmut). The negative control cells, identified by their mutated YTH domain, were unable to induce m6A-associated signals, thereby serving as a means to estimate background noise. After data extraction, 510,554 candidate m6A sites were retained for further analysis. The dataset also includes two case studies using SMART-seq2 data. These case studies utilized the Odds Ratio (OR) results from SigRM for nine trajectory-related genes (MCM6, PCNA, and SLBP as markers for the G1 phase; RRM2, MCM5, and DTL associated with the S phase; and TOP2A, CCNB1, and AURKA for the G2/M phase) as well as the expression levels of five genes related to m6A modification (the well-acknowledged m6A writers METTL3, METTL14, and WTAP, and the erasers FTO and ALKBH5). For the 10x Genomics data, the dataset contains read count information for two replicates (frequency_rep_1_processed.rds, frequency_rep_3_processed.rds), and data from 2,000 single cells from another replicate, including 1,000 test cells and 1,000 control cells. The read counts for these cells are found in frequency_all_processed.rds, and expression data is provided in expression_TPM.rds. A total of 17,733 candidate m6A sites were identified, with further details available in SNP_all_processed.rds. The code used can be found in the code files. The supplementary file contains the results of the case study 2. The files associated with this study include: SNP File (SNP_all.rds):Data Details: Stored in RDS format, containing a GRanges object.Content: Each entry in the GRanges object represents a specific genomic region corresponding to an m6A modification site detected through scDART-seq.seqnames: Factor Rle object containing chromosome or genomic sequence names.ranges: IRanges object containing genomic intervals (start and end positions).strand: Factor Rle object indicating the strand (directionality) of the genomic region.mcols: DataFrame object containing optional metadata columns, such as quality scores, coverage depth, and mutation data.seqinfo: Seqinfo object providing information about the genomic sequences present in the GRanges object.Purpose: Provides detailed genomic information about identified m6A modification sites, facilitating further analysis of their distribution, characteristics, and genomic context in HEK293T cells.Frequency File (frequency_all.rds):Data Details: Also stored in RDS format, consisting of a list.Content: Each item in the list represents a single-cell, containing counts of methylated and unmethylated reads for corresponding m6A modification sites detected in scDART-seq data.Purpose: Offers quantitative data on the abundance of methylated and unmethylated sequences at each m6A site across individual cells, enabling investigation of m6A modification patterns at a single-cell level.Expression TPM File (expression_TPM.rds):Data Details: Stored as an RDS file, comprising a list.Content: Each item in the list represents a single-cell, with corresponding TPM values for gene expression.Purpose: Provides information on gene expression levels across individual cells, facilitating examination of potential correlations between m6A modification patterns and gene expression profiles in scDART-seq data from HEK293T cells.gene Information File (gene_informations.rds):Data Details: Stored as an RDS file, comprising a data frame.Content: Includes information such as Gene ID, Gene Name, Reference, Strand, Start position, End position, and Coverage.Purpose: Offers additional details about gene expression data, aiding in the interpretation and analysis of gene expression profiles in conjunction with m6A modification patterns.Cite: Statistical modeling of single-cell epitranscriptomics enabled trajectory and regulatory inference of RNA methylation: Cell Genomics