CQF-deNoise

CQF-deNoise performs fast and memory-efficient k-mer counting by dynamically identifying and removing erroneous k-mers to improve accuracy in sequencing data analysis.

Key Features:

• Fast k-mer counting: Performs rapid k-mer counting while maintaining competitive processing speeds.
• Memory efficiency: Reduces memory consumption by 49–76% compared to the second-best existing method.
• False k-mer filtering: Dynamically identifies and eliminates erroneous k-mers generated during sequencing without compromising count accuracy.
• scRNA-seq clustering performance: Produces cell clusters highly consistent with CellRanger while requiring only 5% of the running time for similar memory consumption.
• Large-scale applicability: Suitable for k-mer counting in large-scale sequencing datasets where memory constraints are critical.

Scientific Applications:

• Single-cell RNA sequencing (scRNA-seq): Produces cell clusters consistent with CellRanger and enables rapid previews of cell-cluster structure in scRNA-seq datasets.
• K-mer based sequencing analyses: Supports general k-mer counting tasks and reduces the impact of false k-mers on downstream analyses.
• Memory-constrained large-scale projects: Enables k-mer counting workflows for large sequencing datasets under tight memory constraints.

Methodology:

Performs k-mer counting with a mechanism that dynamically identifies and eliminates false/erroneous k-mers to filter them, thereby reducing memory usage while maintaining count accuracy and competitive processing speeds.