Optimal Dual RNA-Seq Mapping for Accurate Pathogen Detection in Complex Eukaryotic Hosts.

Area of Science:

• Bioinformatics
• Genomics
• Microbiology

Background:

• Dual RNA-sequencing (RNA-Seq) enables parallel transcriptomic analysis of interacting organisms.
• Current analysis methods use diverse bioinformatics tools, lacking standardization.
• Optimizing read mapping is critical for accurate dual RNA-Seq data interpretation.

Purpose of the Study:

• To benchmark and compare different bioinformatics tools and methods for dual RNA-Seq data analysis.
• To identify crucial criteria for selecting standard tools, focusing on read trimming and mapping.
• To establish a robust, unified standard methodology for dual RNA-Seq data processing.

Main Methods:

• Benchmarking top-ranking bioinformatics software for quality control, adapter trimming, and read mapping.
• Utilizing a publicly available dual RNA-Seq dataset from the Sequence Read Archive (SRA).
• Evaluating different read mapping strategies, including host-first versus pathogen-first mapping.

Main Results:

• Mapping adapter-trimmed reads to the pathogen genome first resulted in a higher number of aligned pathogen reads compared to traditional host-first mapping.
• This pathogen-first mapping approach prevents misalignment of pathogen reads to the host genome, improving accuracy.
• Key criteria for effective mapping include high unique host mapping, low host multi-mapping, and high pathogen unmapped reads.

Conclusions:

• A standardized, pathogen-first read mapping approach is superior for accurately extracting pathogen-specific information from dual RNA-Seq data.
• This method is crucial for precisely identifying low-proportion pathogenic reads in complex eukaryotic datasets.
• The study provides a comprehensive comparison and recommends a unified standard methodology for dual RNA-Seq analysis.