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Objectives <p>Garden sage (<i>Salvia officinalis</i> L.) is a traditional medicinal plant known for its rich bioactive secondary metabolites. However, there is limited information about the diversity of endophytic microbial communities, including bacteria, fungi, archaea, and viruses. Therefore, the study employs shotgun metagenomics to generate and make publicly available a dataset representing the leaf endophytic microbiome of <i>Salvia officinalis</i>.</p> Data description <p>Metagenomic DNA was extracted from leaves of <i>S. officinalis</i> collected as three biological replicates and sequenced using the Illumina NovaSeq X platform. Host-derived and contaminant sequences were removed by mapping reads to the <i>S. officinalis</i> reference genome using BWA-MEM. The resulting high-quality FASTQ files were analyzed to characterize the taxonomic composition of the endophytic microbiome using Kraken2-based classification.</p>

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Shotgun metagenomic dataset of leaf endophytic microbiome of the garden sage (Salvia officinalis L.)

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月6日

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  • Mouliraj Palanisamy,
  • Olubukola Oluranti Babalola,
  • Sathishkumar Ramalingam

摘要

Objectives

Garden sage (Salvia officinalis L.) is a traditional medicinal plant known for its rich bioactive secondary metabolites. However, there is limited information about the diversity of endophytic microbial communities, including bacteria, fungi, archaea, and viruses. Therefore, the study employs shotgun metagenomics to generate and make publicly available a dataset representing the leaf endophytic microbiome of Salvia officinalis.

Data description

Metagenomic DNA was extracted from leaves of S. officinalis collected as three biological replicates and sequenced using the Illumina NovaSeq X platform. Host-derived and contaminant sequences were removed by mapping reads to the S. officinalis reference genome using BWA-MEM. The resulting high-quality FASTQ files were analyzed to characterize the taxonomic composition of the endophytic microbiome using Kraken2-based classification.