Egert and Friedrich [64] have attributed the presence of ′pseudo T-RFs′ to undigested single stranded DNA amplicons, and have cleared them by cleaving amplicons with single-strand-specific mung bean nuclease. An interesting

possibility to increase Selleckchem EPZ5676 considerably the number of long reads would be to use bidirectional reads as used by Pilloni et al. for the characterization of tar-oil-degrading microbial communities [65]. The majority of dT-RFs were affiliated to several phylotypes, revealing the underlying phylogenetic complexity, which was in agreement with Kitts [59]. PyroTRF-ID enabled assessing the relative contributions of each phylotype, and determining the most abundant ones. In most cases, Rabusertib one phylotype clearly displayed the highest number of reads for one dT-RF. However, for some dT-RFs several phylotypes contributed almost equally to the total number of reads. Although problematic while aiming at identifying T-RFs, this information is of primary importance if PyroTRF-ID is intended to be used for designing

the most adapted T-RFLP procedure for the study of a particular bacterial community. Finally, as exemplified by Additional file 2, the reference mapping database can have an impact on the identification of T-RFs. A fraction of 35 to 45% of the reads was unassigned during mapping in MG-RAST with the Greengenes database, while only 3-5% was unassigned with RDP. This aspect stresses the need of standardized Everolimus mouse databases and microbiome dataset processing approaches in the microbial ecology field. Conclusions This study presented the successful development of the PyroTRF-ID bioinformatics methodology for high-throughput generation of digital T-RFLP profiles from massive sequencing datasets and for assigning phylotypes to eT-RFs based on pyrosequences obtained from the same samples. In addition, this study leads to the following

conclusions: The combination of pyrosequencing and eT-RFLP data directly obtained from the same samples was a powerful characteristic of the PyroTRF-ID methodology, enabling generation of dT-RFLP profiles that integrate the whole complexity of microbiomes of interest. The LowRA and HighRA 454 pyrosequencing method did not impact on the final C1GALT1 results of the PyroTRF-ID procedure. As in any new generation sequencing analysis, denoising was a crucial step in the 454 pyrosequencing dataset processing pipeline in order to generate representative digital fingerprints. The PyroTRF-ID workflow could be applied to the screening of restriction enzymes for the optimization of favorably distributed eT-RFLP profiles by considering the entire underlying microbial communities. HaeIII, MspI and AluI were good candidates for T-RFLP profiling with high richness and diversity indices.