454 sequencing and bioinformatics analysis of the rhizosphere bacterial communities'             diversity of four contrasted Medicago truncatula genotypes
The aim of this experiment was to assess the rhizosphere bacterial                 communities' diversity of four contrasted Medicago truncatula genotypes using 454                 sequencing
A 16S rRNA gene fragment of the appropriate size and                     sequence variability for 454 pyrosequencing was amplified using the primers 530F                     (5'- ACTCCTACGGGAGGCAGCAG) (Acosta-Martinez et al., 2008) and 803R                     (5'-CTACCNGGGTATCTAAT-3'). Ten base pair tags (MID) at 5' position were added to                     the primers to specifically identify each sample as recommended by the                     manufacturer (Beckman Coulter Genomics). For each rhizosphere soil analyzed, the                     DNA extracted from the four replicates was pooled to perform the PCR. DNA                     aliquots (2 ng) from each pool were used for a 50 l PCR reaction under these                     conditions: 95C for 3 min, 30 cycles of 1 min at 95C (denaturation), 55C for 30                     s (annealing) and 72C for 30 s (extension), followed by 5 min at 72C. Adapter                     sequences were added to 300 ng per sample as recommended by the manufacturer and                     pyrosequencing was carried out on a Genome Sequencer FLX 454 (Beckman Coulter                     Genomics). The bioinformatics analysis follows those develop by Terrat et al.                     (2011) corresponding to (i) the orientation for each kept read was checked,                     reverse-complemented where necessary (average read length: 400) and sorted                     according to the MID sequences, (ii) the reads were then filtered and eliminated                     if the exact primer sequences were not found, or if the sequences contained any                     ambiguity (Ns) or if its length is below 300 bases, (iii) a Python program was                     applied to obtain strict dereplication (i.e. clustering of strictly identical                     sequences). This led to a 30 to 40% reduction in the number of sequences. The                     dereplicated reads were then aligned and clustered into OTUs defined by a 3                     0istance level using available tools from the RDP pyrosequencing pipeline (Cole                     et al., 2009). Then, another homemade filtering step was realized to potentially                     eliminate source of errors (PCR chimeras and sequencing errors) based on OTUs                     definition. Briefly, for each sample, 70f sequences were deleted based on the                     clustering (for example, single singletons were mainly removed, because they can                     be considered as a PCR or a sequencing error). Then, all high-quality sequence                     sets were another time aligned and clustered (at a 3 4.94066e-324enetic                     distance) using tools from the RDP pyrosequencing pipeline (Cole et al., 2009).                     Rarefaction curves and diversity indices were finally determined using personal                     programs. For taxonomy-based analysis, the Nave Bayesian rRNA classifier of the                     RDP project was used (Cole et al., 2009) (the bootstrap value was set to 80%).