SCReadCounts

SCReadCounts quantifies cell-level single nucleotide variant (SNV) reference and variant read counts from barcoded single-cell RNA sequencing (scRNA-seq) alignments to generate cell-SNV matrices and expressed Variant Allele Fraction (VAFRNA) matrices for downstream variant-aware analyses.

Key Features:

• Cell-level read-count tabulation: Provides per-cell counts of sequencing reads supporting reference and variant alleles from barcoded scRNA-seq alignments.
• Matrix generation: Produces cell-SNV matrices containing absolute reference and variant read counts and expressed Variant Allele Fraction (VAFRNA) matrices.
• Discovery mode: Assesses reference and alternative nucleotides at genomic positions without prior SNV annotation.
• Somatic and RNA-editing quantification: Estimates cell-level expression of known somatic mutations and RNA-editing sites.
• Allele-specific expression: Estimates per-cell allele expression at germline heterozygous SNVs.
• Benchmarked read-counts module: Read counts performance has been benchmarked against analogous modules in GATK and Samtools.

Scientific Applications:

• Tumor vs. normal cell identification: Distinguishes tumor cells from normal cells based on cell-level SNV profiles.
• Intra-tumoral heterogeneity: Characterizes intra-tumoral genetic heterogeneity at single-cell resolution.
• Mutation-associated expression signatures: Defines expression signatures associated with specific somatic mutations.
• Cell-level somatic mutation and RNA-editing analysis: Measures the expression of known somatic variants and RNA-editing events per cell.
• Allelic expression studies: Enables allele-specific expression analyses and assessment of germline heterozygous SNVs.
• Discovery of novel coding variants: Detects novel nucleotide variations in coding regions without prior SNV information.
• Transcriptional burst kinetics: Supports studies of transcriptional burst kinetics through allele-resolved expression.
• Chromosome X inactivation: Facilitates assessment of X chromosome inactivation at the single-cell level.
• Ploidy estimation and haplotype inference: Assists ploidy estimations and haplotype inference from allele-resolved read counts.
• Recurrent mutation analysis (e.g., KRAS): Identifies and assesses known and novel recurrent somatic mutations in genes such as KRAS.

Methodology:

Analyzes barcoded scRNA-seq alignments using user-provided expected alleles and genomic positions to tabulate reference and variant read counts and construct cell-SNV and VAFRNA matrices.