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Open Access Methodology article

PyroTRF-ID: a novel bioinformatics methodology for the affiliation of terminal-restriction fragments using 16S rRNA gene pyrosequencing data

David G Weissbrodt1, Noam Shani1, Lucas Sinclair235, Grégory Lefebvre236, Pierre Rossi4, Julien Maillard1, Jacques Rougemont23 and Christof Holliger1*

Author Affiliations

1 Ecole Polytechnique Fédérale de Lausanne, School of Architecture, Civil and Environmental Engineering, Laboratory for Environmental Biotechnology, Station 6, Lausanne, 1015, Switzerland

2 Ecole Polytechnique Fédérale de Lausanne, School of Life Sciences, Bioinformatics and Biostatistics Core Facility, Lausanne, Switzerland

3 Swiss Institute of Bioinformatics, Lausanne, Switzerland

4 Ecole Polytechnique Fédérale de Lausanne, School of Architecture, Civil and Environmental Engineering, Central Environmental Molecular Biology Laboratory, Lausanne, Switzerland

5 Uppsala University, Limnology Department, Evolutionary Biology Centre, Uppsala, Sweden

6 Nestlé Institute of Health Sciences, Lausanne, Switzerland

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BMC Microbiology 2012, 12:306  doi:10.1186/1471-2180-12-306

Published: 27 December 2012

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.

Keywords:
Microbial ecology; T-RFLP; Pyrosequencing; Digital T-RFLP; Bioinformatics methodology