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. 2025 Apr 26;13(5):995.
doi: 10.3390/microorganisms13050995.

Optimization of Mapping Tools and Investigation of Ribosomal RNA Influence for Data-Driven Gene Expression Analysis in Complex Microbiomes

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Optimization of Mapping Tools and Investigation of Ribosomal RNA Influence for Data-Driven Gene Expression Analysis in Complex Microbiomes

Ryo Mameda et al. Microorganisms. .

Abstract

For gene expression analysis in complex microbiomes, utilizing both metagenomic and metatranscriptomic reads from the same sample enables advanced functional analysis. Due to their diversity, metagenomic contigs are often used as reference sequences instead of complete genomes. However, studies optimizing mapping strategies for both read types remain limited. In addition, although transcripts per million (TPM) is commonly used for normalization, few studies have evaluated the influence of ribosomal RNA (rRNA) in metatranscriptomic reads. This study compared Burrows-Wheeler Aligner-Maximal Exact Match (BWA-MEM) and Bowtie2 as mapping tools for metagenomic contigs. Even after optimizing Bowtie2 parameters, BWA-MEM showed higher efficiency in mapping both metagenomic and metatranscriptomic reads. Further analysis revealed that rRNA sequences contaminate predicted protein-coding regions in metagenomic contigs. When comparing TPM values across samples, contamination by rRNA led to an overestimation of TPM changes. This effect was more pronounced when the difference in rRNA content between samples was larger. These findings suggest that metatranscriptomic reads mapped to rRNA should be excluded before TPM calculations. This study highlights key factors influencing read mapping and quantification in gene expression analysis of complex microbiomes. The findings provide insights for improving analytical accuracy and advancing functional studies using both metagenomic and metatranscriptomic data.

Keywords: NGS; gene expression; metagenomics; metatranscriptomics; read mapping; ribosomal RNA.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Data-driven analytical pipeline for gene expression analysis in complex microbiomes. Features of DNA sequences and read data are shown in rhombuses, processing methods are indicated in rectangles, datasets used as annotation references are placed in cylinders, and the final output data are presented in rounded rectangles.
Figure 2
Figure 2
Mapping rates of metagenomic and metatranscriptomic reads. Reads were mapped using BWA-MEM (filled circle) or Bowtie2 (outlined shapes), whose setting was sensitive (circles), local -L 19 (squares), or very-sensitive-local -L 19 (triangles).
Figure 3
Figure 3
Log-fold change in TPM between samples. A random subset of 500 expressed genes were plotted. A comparison was conducted between (A) SRR24888495 and SRR24887388, (B) SRR24887267 and SRR24887388, (C) SRR22506317 and SRR22506321 to evaluate the impact of rRNA removal.

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