SNV identification from single-cell RNA sequencing data

Area of Science:

• Genomics
• Computational Biology
• Molecular Biology

Background:

• Single-cell RNA sequencing (scRNA-seq) is crucial for understanding cell type-specific gene expression.
• Integrating scRNA-seq with DNA sequencing aids in identifying functional genetic variants.
• Lack of DNA data in many scRNA-seq studies limits variant analysis.

Purpose of the Study:

• To evaluate single nucleotide variant (SNV) calling pipelines using scRNA-seq data alone.
• To provide practical recommendations for analyzing SNVs in existing scRNA-seq datasets.
• To assess the accuracy and utility of GATK and Monovar for SNV detection from scRNA-seq.

Main Methods:

• Extensive analysis of GATK and Monovar SNV calling pipelines.
• Examination of various parameter settings for optimal SNV detection.
• Evaluation of SNV calling performance across different genomic regions.

Main Results:

• Combining all reads and using GATK Best Practices yielded the highest number of high-concordance SNVs.
• Monovar demonstrated better SNV quality in individual cells, though overall accuracy was limited.
• SNV calling quality varied significantly across different functional genomic regions.

Conclusions:

• Calling SNVs directly from scRNA-seq data is feasible and can uncover functional genetic variants.
• The GATK pipeline, when applied to combined reads, is recommended for maximizing SNV identification.
• Future research can leverage scRNA-seq for novel SNV function investigations, especially in specific genomic contexts.