SCExecute: custom cell barcode-stratified analyses of scRNA-seq data

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Single-cell RNA sequencing (scRNA-seq) enables cell-specific gene expression analysis.
• Existing tools lack sufficient support for analyzing cell-specific features beyond gene expression from scRNA-seq data.
• Stratification of sequencing reads by cellular barcode is crucial for studying these cell-specific features.

Purpose of the Study:

• To introduce SCExecute, a novel tool for executing user-defined commands on barcode-stratified single-cell alignment files.
• To facilitate custom, cell-level analyses on scRNA-seq data.
• To enable the study of low-frequency transcriptome features.

Main Methods:

• SCExecute processes barcode-stratified single-cell binary alignment map (scBAM) files.
• It extracts alignments per cell barcode from pooled, aligned scRNA-seq data on-the-fly.
• User-provided commands, scripts, or pipelines are executed on individual scBAM files, which can be filtered by barcode or genomic region.

Main Results:

• Demonstrated SCExecute's utility with GATK and Strelka2 for detecting single-nucleotide variants in droplet scRNA-seq data.
• Successfully executed shell scripts for BAM manipulation and variant filtering.
• Showcased the detection of single-cell-specific expressed single nucleotide variants.

Conclusions:

• SCExecute empowers custom cell-level analyses on barcoded scRNA-seq data.
• It effectively integrates with existing bioinformatics tools for advanced feature analysis.
• Provides a robust solution for investigating low-frequency transcriptome features at the single-cell level.