Computational protocol: Bacterial diversity and community structure in the rhizosphere of four Ferula species

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[…] Total genomic DNA was extracted from the samples using a centrifugal-type soil genomic DNA extraction kit. The concentration and purity of the DNA was monitored on 1% agarose gels. The DNA was diluted to a concentration of 1 ng μL−1 using sterile water. The 16S rRNA genes of the V4 region were amplified using 515F-806R (5′-GTGCCAGCMGCCGCGGTAA-3′ and 5′-GGACTA CHVGGGTWTCTAAT-3′) with barcodes. All PCR reactions were carried out with Phusion® High-Fidelity PCR Master Mix (New England Biolabs).The X1 loading buffer containing SYB green was mixed in equal volumes with the PCR products and then electrophoresed on 2% agarose gel for detection. Samples with a bright main strip between 400 and 450 bp were chosen for further experiments. The PCR products were mixed in equidensity ratios. The mixed PCR products were then purified with a Qiagen Gel Extraction Kit (Qiagen, Germany). Sequencing libraries were generated using a TruSeq® DNA PCR-Free Sample Preparation Kit (Illumina, USA) following the manufacturer’s recommendations and index codes were added. The library quality was assessed on a Qubit 2.0 Fluorometer (Thermo Scientific) and an Agilent Bioanalyzer 2100 system. Finally, the library was sequenced on an Illumina HiSeq. 2500 platform which generated 250 bp paired-end reads.Paired-end reads were assigned to samples based on their unique barcode and truncated by cutting off the barcode and primer sequence. Paired-end reads were merged using FLASH (V1.2.7), a very fast and accurate analysis tool designed to merge paired-end reads when at least some of the reads overlap the read generated from the opposite end of the same DNA fragment. The splicing sequences were called raw tags. Quality filtering of the raw tags was performed under specific filtering conditions to obtain high-quality clean tags according to the QIIME (V1.7.0) quality control process. The tags were compared with Genomes OnLine Database (GOLD). The UCHIME algorithm was used to detect chimera sequences, and then the chimera sequences were removed to obtain the Effective Tags.Uparse software (Uparse v7.0.1001) was used for sequence analysis. The OTUs with > 97% similarity were screened for further annotation. The taxonomic information for each representative sequence was annotated using the Green Gene Database based on the RDP classifier (Version 2.2) algorithm. Multiple sequence alignment was conducted using MUSCLE software (Version 3.8.31) to study the phylogenetic relationship of different OTUs and the differences among the dominant species among the 36 samples. [...] The OTU-abundance information was normalized using the sequence number corresponding to the sample with the fewest sequences (i.e., XJAW2.2). The alpha diversity and beta diversity were subsequently performed using the normalized data.Alpha diversity was applied to analyze species diversity in a sample through six indices: observed-species, Chao1, Shannon, Simpson, ACE, and Good’s coverage. All of these indices were calculated with QIIME (Version 1.7.0). Community richness was identified using the Chao1, ACE, Shannon and Simpson estimators. Community diversity was identified using the Shannon and Simpson indexes. Sequencing depth was characterized by Good’s coverage.Beta diversity analysis was used to evaluate differences in species complexity among the samples. Beta diversities based on both weighted and unweighted Unifrac were calculated by QIIME software (Version 1.7.0). Unweighted Pair-group Method with Arithmetic Means (UPGMA) clustering was performed as a type of hierarchical clustering method to interpret the distance matrix using average linkage. The UPGMA clustering was conducted by QIIME software (Version 1.7.0). Linear discriminant analysis (LDA) effect size (LEfSe) was calculated online, the web site for online analysis is: analysis was carried out with SPSS 19.0 (IBM Inc., Armonk, USA). Two-way ANOVA was used to analyze the effects of different soil depths and Ferula species on the soil physicochemical properties and bacterial abundance data. Pearson correlations (r) were also run among the soil physicochemical factors and bacterial abundance data. The data sets generated during this study are publicly available. More data can be obtained from the corresponding author. […]

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