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PyroTRF-ID: A Novel Bioinformatics Methodology for the Affiliation of Terminal-Restriction Fragments Using 16S rRNA Gene Pyrosequencing Data

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PyroTRF-ID: A Novel Bioinformatics Methodology for the Affiliation of Terminal-Restriction Fragments Using 16S rRNA Gene Pyrosequencing Data

David G Weissbrodt et al. BMC Microbiol.

Abstract

Background: In molecular microbial ecology, massive sequencing is gradually replacing classical fingerprinting techniques such as terminal-restriction fragment length polymorphism (T-RFLP) combined with cloning-sequencing for the characterization of microbiomes. Here, a bioinformatics methodology for pyrosequencing-based T-RF identification (PyroTRF-ID) was developed to combine pyrosequencing and T-RFLP approaches for the description of microbial communities. The strength of this methodology relies on the identification of T-RFs by comparison of experimental and digital T-RFLP profiles obtained from the same samples. DNA extracts were subjected to amplification of the 16S rRNA gene pool, T-RFLP with the HaeIII restriction enzyme, 454 tag encoded FLX amplicon pyrosequencing, and PyroTRF-ID analysis. Digital T-RFLP profiles were generated from the denoised full pyrosequencing datasets, and the sequences contributing to each digital T-RF were classified to taxonomic bins using the Greengenes reference database. The method was tested both on bacterial communities found in chloroethene-contaminated groundwater samples and in aerobic granular sludge biofilms originating from wastewater treatment systems.

Results: PyroTRF-ID was efficient for high-throughput mapping and digital T-RFLP profiling of pyrosequencing datasets. After denoising, a dataset comprising ca. 10'000 reads of 300 to 500 bp was typically processed within ca. 20 minutes on a high-performance computing cluster, running on a Linux-related CentOS 5.5 operating system, enabling parallel processing of multiple samples. Both digital and experimental T-RFLP profiles were aligned with maximum cross-correlation coefficients of 0.71 and 0.92 for high- and low-complexity environments, respectively. On average, 63±18% of all experimental T-RFs (30 to 93 peaks per sample) were affiliated to phylotypes.

Conclusions: PyroTRF-ID profits from complementary advantages of pyrosequencing and T-RFLP and is particularly adapted for optimizing laboratory and computational efforts to describe microbial communities and their dynamics in any biological system. The high resolution of the microbial community composition is provided by pyrosequencing, which can be performed on a restricted set of selected samples, whereas T-RFLP enables simultaneous fingerprinting of numerous samples at relatively low cost and is especially adapted for routine analysis and follow-up of microbial communities on the long run.

Figures

Figure 1
Figure 1
Data workflow in the PyroTRF-ID bioinformatics methodology. Experimental pyrosequencing and T-RFLP input datasets (black parallelograms), reference input databases (white parallelograms), data processing (white rectangles), output files (grey sheets).
Figure 2
Figure 2
Density plots displaying the repartition of T-RFs along the 0–500 bp domain with different endonucleases. The effect of the different restriction endonucleases HaeIII, AluI, MspI, HhaI, RsaI and TaqI was tested on pyrosequencing datasets collected from the samples GRW01 (A) and AGS01 (B). Histograms represent the number of T-RFs produced per class of 50 bp (to read on the left y-axes). Thick black lines represent the cumulated number of T-RFs over the 500-bp fingerprints (to read on the right y-axes). The total cumulated number of T-RFs corresponds to the richness index. The number given in brackets corresponds to the Shannon′s diversity index.
Figure 3
Figure 3
Mirror plot displaying the cross-correlation between digital and experimental T-RFLP profiles. This mirror plot was generated for the complex bacterial community of sample GRW01. Comparison of mirror plots constructed with raw (A) and denoised sequences (B). Relative abundances are displayed up to 5% absolute values. For those T-RFs exceeding these limits, the actual relative abundance is displayed beside the peak.
Figure 4
Figure 4
Assessment of the impact of data processing on dT-RFLP profiles, and comparison with eT-RFLP profiles. Richness and Shannon′s H′ diversity indices were calculated in a way to quantify the impact of the pyrosequencing data processing parameters on the resulting dT-RFLP profiles. Two examples are given for samples pyrosequenced with the HighRA (GRW01) and LowRA methods (GRW07).
Figure 5
Figure 5
Amount of bacterial affiliations contributing to T-RFs. The absolute (A) and relative numbers (B) of T-RFs that comprised one to several bacterial affiliations is given for the samples GRW01 and AGS01.

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