BLAST Results (Metagenomic Analysis Reveals Enhanced Biodiversity and Composting Efficiency of Lignocellulosic Waste by Thermoacidophilic Effective Microorganism)

Published: 17 August 2020| Version 1 | DOI: 10.17632/8p274syn5k.1
, Kim Kil Yong


This data support the results of the article titled: Metagenomic Analysis Reveals Enhanced Biodiversity and Composting Efficiency of Lignocellulosic Waste by Thermoacidophilic Effective Microorganism (tEM) Following the described methodology, microbial groups given in research data were identified. Journal of Environmental Management


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Pyrosequencing and metagenomic analysis of compost microbial density and diversity To reveal changes in microbial structure following tEM treatment, metagenome amplicon sequencing and pyrosequencing of 16S RNA gene targeting the V3-V4 region for bacteria and archaea for all compost samples was conducted by Macrogen Inc. Geumcheon-gu, Seoul, Korea. Briefly, two replicates of at least 20 composite samples homogenously mixed together were sequenced. Similar sequences were pooled together to minimize computation (Mbareche et al., 2017). The reported number of sequences was an average of two replicates. DNA was extracted from 0.5g aliquots of each sample using Powersoil® DNA Isolation Kit (MO BIO Laboratories Inc., Carlsbad, CA, USA) according to the manufacturer’s protocol. Sequencing samples were prepared according to Illumina 16S Metagenomic Sequencing Library protocol. DNA quantification and quality were measured using PicoGreen and Nanodrop (Fisher Scientific, Leicestershire, England). 16S rRNA genes were amplified using 16S V3-V4 primers (16S Amplicon PCR Forward Primer: 5'TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG; 16S Amplicon PCR Reverse Primer: 5'GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACHVGGGTATCTAATCC). Amplification products were normalized and pooled using PicoGreen. DNA sizes in libraries were verified using tape D1000 (Agilent Technologies Ltd. Yeongdeungpo-gu, Seoul, Korea). Sequencing was done using Miseq™ platform (Illumina, San Diego, CA, USA). Miseq raw data were classified using an index sequence. FASTQ files were generated and adapter sequences were removed using seqpurge (BMC Bioinformatics, 2016) program (Sturm et al., 2016). Error-correction was performed for overlap regions. Paired-end sequencing data of each sample were assembled into one sequence using FLASH 1.2.11 program (Magoč and Salzberg, 2011). Sequences with size less than 400 bp were discarded. Remaining sequences were subjected to CD-HIT-EST OTU based cluster analysis (Li et al., 2012). All low-quality, ambiguous, and chimera sequences were removed. Sequences with > 97% similarity were clustered at species level to form operational taxonomic unit (OTU). OTU sequences were confirmed for similarity by performing BLASTN (v2.4.0) analysis (Zhang et al., 2000) using NCBI 16S Microbial dataset. Taxonomic assignment was obtained with the highest subject organism information. OTU best hit coverage was set to be > 85%. Identity of alignment length was set to be > 85%. Comparative analysis of various microbial communities was performed using QIIME (v1.9) as described previously (Caporaso et al., 2010). Shannon Index and Inverse Simpson Index were obtained. Alpha diversity was confirmed based on Rarefaction curve and Chao1 value. Weighted Beta diversity between samples was based on unifrac distance. PCOA and UPGMA tree were used to visualize soft relationships between samples (Caporaso et al., 2010; Rambaut and Drummond, 2015).


Chonnam National University College of Agriculture and Life Sciences


Secondary Database in Research