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. 2016 Mar 22:17:135.
doi: 10.1186/s12859-016-0992-y.

Sensitivity and correlation of hypervariable regions in 16S rRNA genes in phylogenetic analysis

Bo Yang  1 , Yong Wang  2 , Pei-Yuan Qian  3
Affiliations

Sensitivity and correlation of hypervariable regions in 16S rRNA genes in phylogenetic analysis

Bo Yang et al. BMC Bioinformatics. .

Abstract

Background: Prokaryotic 16S ribosomal RNA (rRNA) sequences are widely used in environmental microbiology and molecular evolution as reliable markers for the taxonomic classification and phylogenetic analysis of microbes. Restricted by current sequencing techniques, the massive sequencing of 16S rRNA gene amplicons encompassing the full length of genes is not yet feasible. Thus, the selection of the most efficient hypervariable regions for phylogenetic analysis and taxonomic classification is still debated. In the present study, several bioinformatics tools were integrated to build an in silico pipeline to evaluate the phylogenetic sensitivity of the hypervariable regions compared with the corresponding full-length sequences.

Results: The correlation of seven sub-regions was inferred from the geodesic distance, a parameter that is applied to quantitatively compare the topology of different phylogenetic trees constructed using the sequences from different sub-regions. The relationship between different sub-regions based on the geodesic distance indicated that V4-V6 were the most reliable regions for representing the full-length 16S rRNA sequences in the phylogenetic analysis of most bacterial phyla, while V2 and V8 were the least reliable regions.

Conclusions: Our results suggest that V4-V6 might be optimal sub-regions for the design of universal primers with superior phylogenetic resolution for bacterial phyla. A potential relationship between function and the evolution of 16S rRNA is also discussed.

Keywords: 16S rRNA; 16S rRNA gene; Geodesic distance; Phylogenetic; Primer; Variable regions.

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Figures

Fig. 1
Fig. 1
Workflow of the data processing. As described in Materials and methods, the sequences downloaded from the SILVA database were filtered, randomly selected and grouped. Phylogenetic trees were then built, and geodesic distances were calculated
Fig. 2
Fig. 2
Geodesic distance between trees based on sub-regions and trees based on VT
Fig. 3
Fig. 3
Illustration of different variable regions. Red regions (V2, V8) have a poor phylogenetic resolution at the phylum level. Green regions (V4, V5, V6) are associated with the shortest geodesic distance, which suggests that they may be the best choice for phylogeny-related analyses and the phylogenetic analysis of novel bacterial phyla. The figure refers to the primer map from Lutzonilab (http://lutzonilab.org/16s-ribosomal-dna/). Use of this information was approved by the original authors of the website
Fig. 4
Fig. 4
AHC results for different regions based on the geodesic distances of the phylogenetic trees
Fig. 5
Fig. 5
The 2D-3D structures of the 16S rRNA gene. Individual regions are identified by the same color in both the 2D and 3D structures. Some important structures are colored with blocks
See this image and copyright information in PMC

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