Phylogenies of the 16S rRNA gene and its hypervariable regions lack concordance with core genome phylogenies - Microbiome

]. We were unable to test the rRNA genes for HGT using HGTector because the program requires an amino acid sequence as input. As an alternative, we took a phylogenetic approach. Using the rRNA alignments containing all gene copies, a maximum likelihood phylogeny was produced using the GTR model implemented in PhyML. A monophyletic grouping of gene copies indicated vertical inheritance, whereas a polyphyletic grouping provided evidence for HGT.

To test if utilizing more genes, and thereby more SNPs, would increase concordance with the species phylogeny, we concatenated the alignments of the five and ten lowest scoring genes from each genus and produced two new ML phylogenies . We then compared these phylogenies to their respective species phylogeny to measure concordance.

For each of the SNP categories, SNPs were extracted from the single-copy core gene alignments using a custom python script . Stem and loop nucleotides were determined by predicting the secondary structure using rPredictorDB []. rPredictorDB uses a database of experimentally derived rRNA secondary structures as a template to predict those of individual input sequences.

For each genus and SNP category, we described the relationship between concordance with the species phylogeny and the number of SNPs. For seven of the eleven categories, the following procedure was followed . SNP columns were incrementally extracted at random from the core gene alignments, building 1000 separate alignments that ranged in size from 1 bp to 1000 bp . For each of these alignments, concordance was measured and plotted against the SNP count .

The four SNP categories with limited SNPs in their alignment were the rRNA categories . With the exception of the stem category for, which contained only two SNPs, we extracted the maximum number of SNPs available in each alignment . We then followed the same iterative procedure described above building as many alignments as possible. Again, concordance was plotted against the SNP count and cross-validation used to determine the model that best described the relationship.

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