Soil microbiome dataset from Guanica dry forest in Puerto Rico generated by shotgun sequencing.
Soil microbiome dataset from Guanica dry forest in Puerto Rico generated by shotgun sequencing.
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通过鸟枪法测序生成的波多黎各瓜尼卡干燥森林的土壤微生物组数据集。
DOI：
10.1016/j.dib.2019.104919
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发表时间：
2020
期刊：
影响因子：
1.2
通讯作者：
Rios-Velazquez,Carlos
中科院分区：
文献类型：
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作者：
Sotomayor-Mena,RobertoG;Rios-Velazquez,Carlos
瓜尼卡干林（GDF）位于波多黎各西南部地区，是世界上保存最完整的亚热带干林之一[1]。为了描述该环境的分类多样性和功能概况，从GDF产生的宏基因组文库中提取宏基因组DNA。使用Illumina对DNA进行鸟枪测序，并使用MG-RAST服务器进行分析。多样性分析表明，细菌（97.8%）是最丰富的结构域，其次是细菌（1.12%），真核生物（1.02%）和病毒（0.03%）。在50门中，最丰富的是变形菌门（41.6%），其次是放线菌门（18.7%）和酸杆菌门（7.06%）。共鉴定出213目、384科、791属。功能谱显示与碳水化合物（13.16%）、基于发酵的子系统（13.0%）、氨基酸及其衍生物（9.9%）和蛋白质代谢（8.24%）相关的基因丰富。此外，更具体的分组显示，NULL（21.5%）是最丰富的功能组，其次是植物-原核生物DOE计划（6.05%），蛋白质生物合成（4.82%），中央碳水化合物代谢（3.98%），DNA修复（2.72%）和抗生素和有毒化合物的抗性（2.66%）。该数据集可用于生物勘探研究，并可应用于生物医学科学、生物技术和微生物、人口和应用生态学领域。
Guanica dry forest (GDF), located in the southwest area or region of Puerto Rico, is among the most preserved subtropical dry forests in the world [1]. To describe the taxonomic diversity and functional profiles of this environment, metagenomic DNA was extracted from a metagenomic library generated from the GDF. The DNA was shotgun-sequenced using Illumina and analyzed using the MG-RAST server. The diversity profile revealed that the most abundant domain was Bacteria (97.8%) followed by Archaea (1.12%), Eukaryota (1.02%) and Viruses (0.03%). Out of the 50 phyla present, the most abundant was Proteobacteria (41.6%) followed by Actinobacteria (18.7%) and Acidobacteria (7.06%). Moreover, a total of 213 orders, 384 families and 791 genus were identified. The functional profile showed abundance of genes related to Carbohydrates (13.16%), Clustering-based subsystems (13.0%), Amino Acids and Derivatives (9.9%) and Protein Metabolism (8.24%). Furthermore, more specific grouping showed that NULL (21.5%) was the most abundant function group, followed by Plant-Prokaryote DOE project (6.05%), Protein biosynthesis (4.82%), Central carbohydrate metabolism (3.98%), DNA repair (2.72%) and Resistance to antibiotics and toxic compounds (2.66%). This dataset is useful in bioprospecting studies with application in biomedical sciences, biotechnology and microbial, population and applied ecology fields.