## 2 Materials and Methods
### Data Processing and Statistical Analysis  
The online metagenomics rapid annotation server MG-RAST (www.mg-rast.org, accessed on 11 July 2019) was used for the quality control of the raw shotgun metagenomic sequences [23]. Low-quality sequences were removed using a modified DynamicTrim (where sequences with >5 ambiguous base pairs with 15 phred score cutoff were removed) and a length filtering (where sequences with a length of >2 standard deviations from the mean were removed) according to [PERSON] et al. [24]. Artificial replication reads that occurred during sequencing were removed [25]. After performing quality control (QC), the BLAT (the BLAST-like alignment tool) algorithm was used to annotate the sequences [26] against the M5 NR database [27], which encompasses nonredundant integration of many databases. The microbial community groupings were identified using the SEED subsystem. An e-valve of 5, with a maximum identity of 60%, maximum alignment length of 15 base pairs, and maximum abundance of 1 were used when mapped against the subsystems database that integrated SILVA, Greengenes, and RDP (ribosomal database project) databases [28]. The relative abundance of the four samples was determined and used for statistical analysis.  
After the dataset for each of three replicates of the rhizosphere and bulk soil samples of the two locations was normalized by finding the average and relative abundance, one-way analysis of variance (ANOVA) was used to determine if there was a significant difference in rhizosphere and bulk soil samples, and [PERSON]'s pairwise comparison was used for the means at a significant level (\(p\) value < 0.05). The diversity indices (alpha and beta diversity) of the structural and beneficial functional genes were also determined. The analysis of similarity (ANOSIM) with 999 random permutations was employed to test for significance for beta diversity. These analyses were done using the PAST software version 2.17c [29]. The distribution of microbial communities' diversity and structure between the sunflower rhizosphere and the bulk soil was ascertained using principal component analysis (PCA) based on a Bray-Curtis dissimilarity matrix. Likewise, principal coordinates analysis (PCoA) based on Bray Curtis dissimilarity matrix was used to visualize the beta diversity of the structural and functional genes diversity from both rhizosphere and bulk soil habitats.  
To determine the environmental variable that best shaped the structures of the microbiome, canonical correspondence analysis (CCA) was performed based on a forward selection of environmental variables, and the Monte Carlo permutation test, with 999 random permutations, was employed to test for significance. The relative abundance of the plant-beneficial functional gene was plotted using circos ([[http://circos.ca/](http://circos.ca/)]([http://circos.ca/](http://circos.ca/)), accessed on 26 June 2020), while CANOCO 5 (Microcomputer Power, Ithaca, NY, USA) was used to view the PCA and PcoA. The raw sequence has been deposited into the GenBank database, where the SRA (sequence read archive) accession number for R1 is SRR10426233, R2 SRR10418054, while B1 and B2 have accession numbers of SRR10426310 and SRR10418081, respectively.