PixelCut: A Unified Solution for Zero-Configuration 16S rRNA Trimming via Computer Vision

Area of Science:

• Microbiology
• Bioinformatics

Background:

• 16S rRNA amplicon sequencing is cost-effective for microbial taxonomy profiling.
• Accurate trimming of low-quality bases is crucial for reliable 16S rRNA sequencing results.
• Existing trim-position prediction tools often require computational expertise or biological priors.

Purpose of the Study:

• To introduce PixelCut, an automated framework for predicting and removing erroneous bases in 16S rRNA sequencing reads.
• To provide a user-friendly and accessible tool for accurate trim-position prediction.
• To enhance the reliability of taxonomic profiling in microbiome research.

Main Methods:

• PixelCut analyzes FastQC per-base quality reports, not raw FASTQ data.
• It uses computer vision and character recognition to determine trim positions based on quality scores.
• A web application and command-line version are available for accessibility.

Main Results:

• PixelCut accurately predicts trim positions without hyperparameters or prior biological information.
• The framework demonstrates consistency with established trim-location prediction algorithms.
• Simulations confirm PixelCut's effectiveness in producing reliable taxonomic profiles.

Conclusions:

• PixelCut offers an automated, accessible, and accurate solution for read trimming in 16S rRNA sequencing.
• The method enhances the usability and reliability of microbiome analysis.
• PixelCut addresses limitations of existing tools, benefiting researchers with varying computational backgrounds.