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Virtual Metagenome

Allows the reconstruction of metagenomes. Virtual Metagenome reflects real functional compositions and actual transitions of gene pools even though they were virtually reconstructed from denaturing gradient gel electrophoresis (DGGE). This tool provides an opportunity to re-evaluate massive volumes of information on species diversity by using 16S rRNA gene sequence data accumulated in previous experiments performed by microbial ecologists. It allows also to re-analyse the data in terms of genes/genomes, in order to provide a deeper view to the inside of the microbial functions.

RAMBL / reference-based ribosome assembly

Integrates taxonomic tree search and Dirichlet process clustering to reconstruct full-length 16S gene sequences from metagenomic sequencing data with high accuracy. RAMBL realizes the access to full-length 16S gene sequences in the near-terabase-scale metagenomic shotgun sequences. It is able to generate a more accurate determination of environmental microbial diversity and yield better disease classification, suggesting that full-length 16S gene assemblies are a powerful alternative to marker gene set and 16S short reads.

REAGO / REconstruct 16S ribosomal RNA Genes from metagenOmic data

Reconstructs 16S rRNA genes from metagenomic data. REAGO is able to accurately identify 16S rRNA from error-containing metagenomic datasets at sequence level. The algorithms are robust even if the genera of the underlying genes are not included in the covariance model (CM) training set. It can be readily applied to any metagenomic dataset containing paired-end reads. Several components in REAGO work better with increasing read length. In particular, the homology search stage and the bad edge removal part can all benefit from increased sequence length, which is the trend for next-generation sequencing technologies.