Metagenomic data, which contains sequenced DNA reads of uncultured microbial species from environmental samples, provide a unique opportunity to thoroughly analyze microbial species that have never been identified before. Reconstructing 16S ribosomal RNA, a phylogenetic marker gene, is usually required to analyze the composition of the metagenomic data. However, massive volume of dataset, high sequence similarity between related species, skewed microbial abundance and lack of reference genes make 16S rRNA reconstruction difficult.
Reconstructs full-length small subunit (SSU) sequences from metagenomic data from a microbial community of interest, accurate to the species level. In addition, the method also provides accurate SSU sequence abundance estimates. EMIRGE is robust to errors and omissions in the reference database, and is broadly applicable to any dataset produced with short read sequencing technology.
Brings together many aspects of today’s cutting-edge genomic, metagenomic, and metatranscriptomic analysis practices to address a wide array of needs. Anvi’o is an advanced analysis and visualization platform that offers automated and human-guided characterization of microbial genomes in metagenomic assemblies, with interactive interfaces that can link ‘omics data from multiple sources into a single, intuitive display. It empowers researchers without extensive bioinformatics skills to perform and communicate in-depth analyses on large ‘omics datasets.
Allows users to identify and analyze small-subunit (SSU) rRNA gene fragments from shotgun metagenomic sequences. SSUsearch is a standalone software that can manage large datasets. The application allows users to perform a SSU rRNA gene fragment search and unsupervised operational taxonomic unit (OTU) analysis. It is able to process diversity analysis with copy number correction on multiple variable regions.
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.
Checks and collects defined hypervariable sequence segments (V1-V9) from bacterial, archaeal, and fungal small-subunit rRNA sequences. V-Xtractor is not sensitive to false-positives. It was created to simplify subsequent analysis in community assays. This tool employs a Hidden Markov Models method to proceed. It does not need prior multiple sequence alignments to obtain phylogenetically comparable regions.
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.