Error correction algorithms for efficient gene expression quantification in single cell transcriptomics with Arcane.

Arcane improves gene expression quantification in droplet-based scRNA-seq by efficiently correcting errors in barcodes and UMIs, achieving faster performance than existing methods.

• Why it matters: Accurate processing of sequencing errors in barcodes and UMIs is crucial for reliable single-cell transcriptome data, yet current methods are often slow or less precise, limiting large-scale analyses.
• What they did: The authors developed Arcane, an algorithmic workflow that enhances barcode correction, read-to-gene mapping, and UMI resolution by building on the Fourway method, and implemented it as a command-line tool.
• The result: Arcane outperforms existing tools like Cell Ranger, kallisto | bustools, and alevin-fry in speed while maintaining similar accuracy, enabling more efficient and reliable gene expression quantification in large scRNA-seq datasets.