Pattern-Filter structural validation of single-cell RNA-seq reads reduces artifactual barcodes and improves...

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• Single-cell RNA sequencing (scRNA-seq) pipelines assume correct read structure, but structural aberrations are common.
• Existing tools fail to identify artifacts like missing anchor motifs, leading to spurious barcodes and inflated cell counts.
• These technical noise artifacts compromise biological interpretation and data reproducibility.

Purpose of the Study:

• To develop a universal preprocessing tool, Pattern-Filter, for validating read integrity in scRNA-seq data.
• To address the issue of structural artifacts that affect barcode diversity and cell quantification.
• To enhance the reliability and biological accuracy of scRNA-seq analyses.

Main Methods:

• Developed Pattern-Filter, a preprocessing tool that validates read integrity before alignment.
• Pattern-Filter detects platform-specific anchor sequences and filters reads based on base composition.
• Applied Pattern-Filter across diverse scRNA-seq platforms including 10x Genomics, Drop-seq, BD Rhapsody, and SPLiT-seq.

Main Results:

• Pattern-Filter systematically removed 2%-18% of total reads across platforms.
• Spurious barcode diversity was reduced by up to 80% after applying Pattern-Filter.
• Data reproducibility was enhanced, and previously obscured cell types, like dopaminergic neurons, were recovered.

Conclusions:

• Structural validation of sequencing reads is an essential prerequisite for scRNA-seq analysis.
• Pattern-Filter effectively removes technical noise, ensuring molecular fidelity and reliable biological discovery.
• This tool enhances the accuracy and reproducibility of single-cell transcriptomics studies.