SCSit: A high-efficiency preprocessing tool for single-cell sequencing data from SPLiT-seq.

Area of Science:

• Single-cell genomics
• Bioinformatics
• Computational biology

Background:

• SPLiT-seq enables low-cost single-cell RNA sequencing through combinatorial barcoding.
• Existing methods lack automated, rapid preprocessing for SPLiT-seq data, hindering efficient analysis.
• A need exists for tools that accurately identify barcodes and UMIs while maximizing data yield.

Purpose of the Study:

• To develop a high-efficiency preprocessing tool (SCSit) for SPLiT-seq single-cell sequencing data.
• To enable direct identification and labeling of combinatorial barcodes and UMIs.
• To enhance the accuracy and quantity of processed single-cell sequencing data.

Main Methods:

• Developed SCSit, a computational tool for processing SPLiT-seq raw data.
• Implemented exact alignment for insertion and deletion to improve read mapping.
• Focused on direct identification of combinatorial barcodes and UMIs for cell type labeling.

Main Results:

• SCSit achieves 97% consistency in identified reads compared to the original SPLiT-seq method.
• The tool doubles the number of mapped reads, significantly increasing data yield.
• SCSit processing time is less than 10% of the original method's runtime.

Conclusions:

• SCSit provides an accurate and rapid solution for analyzing SPLiT-seq raw data.
• The tool effectively enhances single-cell data quality and quantity from the SPLiT-seq platform.
• SCSit improves the efficiency of single-cell RNA sequencing analysis for low-cost applications.