Microbial abundance estimation and metagenomic studies of holcomb creosote superfund site soil sample

The use of genomic sequencing has greatly improved our ability to profile the microbial communities associated with the environment and host. Among the most common applications of metagenomics is assessing microbiome biodiversity. Holcomb Creosote Superfund Site Soil is located in the North Carolina area, 16s metagenomic Sequencing was carried out from the soil of Holcomb Creosote Superfund Site. To describe the taxonomic diversity and functional profiles of this environment, metagenomic DNA sequence was extracted from a metagenomics library generated from the Holcomb Creosote Superfund Site Soil by 16s metagenomic Sequencing. The DNA was shotgun-sequenced using Illumina and analysed using the MG-RAST server. A soil sample from a large metagenomes sequence collection acquired from shotgun sequencing was investigated for Duke University. Overall sequences in the dataset were 1,34,00,509 comprising a total read length of 4,03,05,03,377 base pairs. The categorization of 8 species was based on the analysis of taxonomic data. The metagenome sequence was submitted by Duke University, Alexander McCumber to NCBI database and can be accessed with the SRA accession number SRX8095153. An online metagenome server (MG RAST) using the subsystem database revealed bacteria had the highest diversity profile revealed that the most abundant domain was 92.3% of bacteria, 5.6% of Eukaryota, 0.1 % of viruses, 1.4% of archaea, and 0.6% of unclassified sequences.The most abundant were Firmicutes (20.5%), and Proteobacteria (10.3%) followed by Actinobacteria (18.4%) and Acidobacteria (7.5%). The functional profile showed an abundance of genes related to subsystems (16.9%), carbohydrates (19.5%), cell wall and capsule (6%), miscellaneous (8.8%), protein metabolism (10%), amino acids and derivatives (14.7%), DNA metabolism (3.4%), cofactors, vitamins, prosthetic groups, pigments (9.2%), membrane transport (1.9%), RNA metabolism (4.4%) and fatty acids, lipids, and isoprenoids (3.6%). This dataset is useful in bioprospecting studies with application in biomedical sciences, biotechnology and microbial, population, and applied ecology fields.