This analysis showed that the multiple T-RF sizes observed were due to reads harboring insertions or deletions of nucleotides before the first HaeIII restriction site or to nucleotide modifications within HaeIII sites. Discussion Advantages and novelties LY2874455 of the PyroTRF-ID bioinformatics methodology This study describes the development of the PyroTRF-ID bioinformatics methodology for the analysis of microbial community structures, and its application on low- and high-complexity environments. PyroTRF-ID can be seen as the core of a high-throughput methodology for assessing microbial community structures and their dynamics

combining NGS technologies and more traditional community fingerprinting techniques such as T-RFLP. More than just predicting the most probable T-RF size of target phylotypes, PyroTRF-ID allows the generation of dT-RFLP profiles from 16S rRNA gene Selleck FK506 pyrosequencing selleck products datasets and the identification of experimental T-RFs by comparing dT-RFLP to eT-RFLP profiles constructed from the same DNA samples. At the initial stage of the assessment of a microbial community, PyroTRF-ID can be used for the design of an eT-RFLP procedure adapted to a given microbial community through digital screening of restriction enzymes. In contrast to previous studies involving in silico restriction of artificial microbial

communities compiled from selected reference sequences from public or cloning-sequencing databases [25, 29, 31], PyroTRF-ID works on sample-based pyrosequencing datasets. This requires the pyrosequencing of a limited number of initial samples. The number of T-RFs, the homogeneity in their distribution, and the number of phylotypes contributing to T-RFs should be used as criteria for the choice of the best suited enzyme. Combination

of pyrosequencing and eT-RFLP datasets obtained on the same initial set of samples enables the beginning of the study of new microbial systems with knowledge on T-RFs affiliation. The length of T-RFs and Bay 11-7085 their sequences are directly representative of the investigated sample rather than inferred from existing databases. In this sense, the complexity of the original environment is accurately investigated. For all types of low- and high-complexity environments assessed in this study, HaeIII, AluI and MspI were good candidates for the generation of rich and diverse dT-RFLP profiles. Subsequently, eT-RFLP can be used as a routine method to assess the dynamics of the stuctures of microbial communites, avoiding the need for systematic pyrosequencing analyses. We suggest that pyrosequencing should be applied at selected time intervals or on representative samples to ensure that the T-RFs still display the same phylogenetic composition.