New tool helps researchers spot flawed single-cell genetic data before analysis

Researchers have developed SkewC, a computational method to catch quality problems in single-cell RNA sequencing data early in the pipeline. The tool addresses a growing bottleneck in cell biology research: bad data wastes months of lab work and millions in research funding. For biotech firms and academic labs investing heavily in single-cell studies, early quality checks could slash costs and accelerate drug discovery timelines.

Originaltitel: Computational approach to evaluate scRNA-seq data quality and gene body coverage with SkewC