1 - 18 of 18 results

MEGAN / MEtaGenome ANalyzer

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Allows users to taxonomically and functionally explore and analyze large-scale microbiome sequencing data. MEGAN is a comprehensive microbiome analysis toolbox for metagenome, meta-transcriptome, amplicon and from other sources data. Users can perform taxonomic, functional or comparative analysis, map reads to reference sequences, reference-based multiple alignments and reference-guided assembly and integrate their own classifications.


Leverages pre-existing databases, annotations and frameworks, along with manual curation. BugBase is an algorithm with a user-friendly graphical user interface (GUI) for analyzing microbiome data developed to provide users with biologically relevant microbiome phenotype predictions at the organism level. This method is also unable to model and predict cell-to-cell heterogeneity within strains due to genetic mutations and horizontal gene transfer. BugBase is available as a desktop app and as a web app.


Classifies sequences into protein families and predicts the presence of important domains and sites. InterPro is an integrated resource of protein families, domains and sites which are combined from a number of different protein signature databases, including: Gene3D, Panther, PRSF, Pfam, PRINTS, ProSite, ProDom, SMART, SUPERFAMILY and TIGRFAMs. InterPro2GO creates annotations from data of InterPro. Gene Ontology terms assigned by InterPro2GO are cross-referenced more than 168 million times in UniProtKB, providing terms for almost 50 million individual proteins.

IMNGS / Integrated Microbial NGS

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Offers a complete workflow for de novo analysis of users’ own raw 16S rRNA gene amplicon datasets for the sake of comparison with existing data. IMNGS is an innovative platform that uniformly and systematically screens for and processes all prokaryotic 16S rRNA gene amplicon datasets available in sequence read archive (SRA) and uses them to build sample-specific sequence databases and OTU-based profiles. Via a web interface, this integrative sequence resource can easily be queried by users.


A powerful tool in accurate profiling functions in a metagenomic sample. metaFunction detects all possible functional roles at the low level from a metagenomic sample/community. In the first step a statistical mixture model is proposed at the base of gene codons to estimate the abundances for the candidate functional roles, with sequencing error being considered. As a gene could be involved in multiple biological processes the functional assignment is therefore adjusted by utilizing an error distribution in the second step.

MetaNetSam / Metabolic Network Sampling

A probabilistic sampling approach to profiling metabolic reactions in a microbial community from metagenomic shotgun reads, in an attempt to understand the metabolism within a microbial community and compare them across multiple communities. Different from the conventional pathway reconstruction approaches that aim at a definitive set of reactions, our method estimates how likely each annotated reaction can occur in the metabolism of the microbial community, given the shotgun sequencing data. This probabilistic measure improves our prediction of the actual metabolism in the microbial communities and can be used in the comparative functional analysis of metagenomic data.