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Updated: Oct 4, 2025

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

Anish M S Shrestha1,2, Joyce Emlyn B Guiao3,4, Kyle Christian R Santiago3,5

  • 1Bioinformatics Lab, Advanced Research Institute for Informatics, Computing, and Networking (AdRIC), De La Salle University, Manila, Philippines. anish.shrestha@dlsu.edu.ph.

BMC Genomics
|February 5, 2022
PubMed
Summary

Researchers developed a faster, more accurate RNA-seq analysis method for non-model organisms. This approach avoids computationally expensive transcriptome assembly, significantly reducing time and resources for gene expression studies.

Keywords:
DNA-protein alignmentDifferential gene expression analysisNon-model organismsRNA-seqTranscriptome assembly

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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.