The combined analysis as the best strategy for Dual RNA-Seq mapping

Area of Science:

• Microbiology
• Bioinformatics
• Genomics

Background:

• Dual RNA-sequencing (RNA-Seq) enables simultaneous gene expression analysis of interacting organisms.
• Accurate read separation is critical for reliable Dual RNA-Seq data analysis.
• Current in silico methods may lead to information loss, particularly for prokaryotic organisms.

Purpose of the Study:

• To present and evaluate a combined in silico read mapping approach for Dual RNA-Seq.
• To compare the combined approach with traditional sequential mapping methods.
• To highlight the importance of accurate read sorting for preserving gene expression data.

Main Methods:

• Simulated Dual RNA-Seq experiments using eukaryotic (Zea mays) and prokaryotic (Herbaspirillum seropediceae) organisms.
• Utilized a combined mapping strategy: alignment to a concatenated genome followed by mapping to individual genomes.
• Compared the combined method against a sequential mapping approach using public and real Dual RNA-Seq datasets.

Main Results:

• The combined approach significantly reduced cross-mapped reads compared to sequential analysis.
• Sequential analysis showed a bias, attributing more reads to the first reference genome.
• The combined method demonstrated superior accuracy in read distribution between interacting organisms.

Conclusions:

• Combining reference genomes for initial read sorting is essential in Dual RNA-Seq.
• This strategy prevents the loss of valuable gene expression information, especially for prokaryotic organisms.
• The findings underscore the need to adopt improved mapping techniques to maximize data integrity in Dual RNA-Seq studies.