The PyroTRF-ID bioinformatics methodology (http://bbcf.epfl.ch/PyroTRF-ID/) was developed to combine pyrosequencing and T-RFLP for describing microbial communities and identifying T-RFs by comparison of experimental and digital T-RFLP profiles obtained from the same biological 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 pyrosequencing datasets. Sequences contributing to each digital T-RF were classified to taxonomic bins using the Greengenes reference database. The method was tested on bacterial communities found in chloroethene-contaminated groundwater samples and in granular biofilms from lab-scale wastewater treatment systems. PyroTRF-ID was efficient for high-throughput mapping and digital T-RFLP profiling of pyrosequencing datasets. After denoising, multiple datasets comprising ca. 10'000 reads of 300-500 bp were processed in parallel within ca. 20 minutes on a high-performance computing cluster running on a Linux-related CentOS 5.5 operating system. 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. PyroTRF-ID profits from complementary advantages of massive sequencing and T-RFLP in order to optimize laboratory and computational efforts for investigating microbial community structures and dynamics in any biological system. Massive sequencing provides high resolution in the analysis of microbial communities, and can be performed on a restricted set of selected samples. T-RFLP enables simultaneous fingerprinting of numerous samples at low cost and is adapted for routine analysis and follow-up of microbial communities on long term.