Assembly-free rapid differential gene expression analysis in non-model organisms using DNA-protein alignment

Area of Science:

• Bioinformatics
• Genomics
• Computational Biology

Background:

• RNA sequencing (RNA-seq) is crucial for gene expression studies in non-model organisms across agriculture, aquaculture, ecology, and environmental science.
• Conventional RNA-seq analysis for organisms lacking reference genomes necessitates de novo transcriptome assembly and protein annotation, which are computationally intensive and prone to errors.
• The assembly step is critical for read mapping and subsequent functional analysis of differentially expressed genes.

Purpose of the Study:

• To develop a computationally efficient and accurate method for differential gene expression analysis in non-model organisms using RNA-seq data.
• To bypass the need for de novo transcriptome assembly, thereby reducing computational costs and analysis time.
• To improve the sensitivity and precision of differential expression analysis compared to traditional assembly-based pipelines.

Main Methods:

• A novel bioinformatics pipeline was designed to directly align RNA-seq reads to a high-confidence proteome database.
• This approach bypasses the computationally expensive de novo transcriptome assembly step.
• The workflow was implemented using Snakemake for reproducibility and accessibility.

Main Results:

• The proposed method drastically reduces computational costs, decreasing running time from tens of hours to under half an hour and memory requirements from tens of Gbytes to tens of Mbytes.
• Experiments on simulated and real data demonstrate that the pipeline achieves higher sensitivity and precision than conventional assembly-based methods.
• The direct alignment to proteomes provides a more efficient and accurate alternative for differential gene expression analysis.

Conclusions:

• The study presents a streamlined RNA-seq analysis workflow that significantly lowers the bioinformatics bottleneck for non-model organisms.
• Avoiding transcriptome assembly enables quicker, easier, and more resource-efficient differential gene expression analysis.
• This method enhances accessibility for labs with limited computational resources, improving the speed and accuracy of gene expression studies.