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All Journal JSMARTech : Journal of Smart Bioprospecting and Technology
Verrone, Valeria
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Biochemistry, Genetics & Molecular Biology Chemistry Materials Science & Nanotechnology Medicine & Pharmacology Neuroscience

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Soil Microbiome Dataset from Rawa Aopa Watumohai National Park in the Agroforestry Region of Southeast Sulawesi Reski, Alpi Meitia; Andrianto, Dimas; Pratama, Rahadian; Verrone, Valeria; Edy, Nur; Agusti, Randi; Hafazallah, Khalid; Ahmad, Roland Putra Pribadi; Dea, Andi Amriyana; Sembiring, Christine
JSMARTech: Journal of Smart Bioprospecting and Technology Vol. 7 No. 1 (2026): JSMARTech Volume 7, No 1, 2026
Publisher : JSMARTech

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jsmartech.2026.007.01.35_2

Abstract

The soil microbiome dataset is a vital dataset to support restoration and sustainable land management. Rawa Aopa Watumohai National Park (TNRAW) is a national park located in Southeast Sulawesi Province Indonesia, which has not been analyzed for its microbiome. This research aims to characterize the diversity of soil fungal and eukaryotic communities using a dual-marker metabarcoding approach and to generate baseline data to support future biodiversity monitoring, ecosystem restoration, and sustainable agroforestry management in TNRAW. Soil samples were collected from five agroforestry plots and analyzed using a dual-marker metabarcoding approach targeting the ITS2 region for fungi and the SSU rRNA region for broader eukaryotes. DNA was extracted and checked for purity using Nanodrop and Quantus. PCR amplification was confirmed via agarose gel electrophoresis, and sequencing was conducted with Oxford Nanopore Technologies. Bioinformatics processing included basecalling, quality filtering, taxonomic classification using Kraken2, and Visualization with Krona and ampvis2. The ITS2 dataset results revealed dominant fungal taxa such as Fusarium falciforme, Talaromyces marneffei, and Aspergillus, with high read counts. In contrast, the SSU dataset complemented this by detecting non-fungal eukaryotes like Cryptophyceae, Vibrio anguillarum, and Theileria equi and filtering retained 2–16% high-quality reads per sample, sufficient for downstream ecological interpretation. The resulting dataset can serve as a starting point for future research on tropical soil biodiversity and as a local reference for conservation and agroforestry management in TNRAW.
Co-Authors Agusti, Randi Ahmad, Roland Putra Pribadi Dea, Andi Amriyana DIMAS ANDRIANTO Hafazallah, Khalid Nur Edy Rahadian Pratama Reski, Alpi Meitia Sembiring, Christine