Welcome to Microme
Microme is a resource for bacterial metabolism, whose aim is to support the large scale inference of metabolic flux directly from genome sequence. It is populated with data generated by direct import from external sources and by automatic inference from these seed data onto additional genomes; data is subject to quality assurance (i.e. for chemical balance, consistency of cross-references, and ultimately, for their ability to define a viable model mathematically and their consistency with experimental evidence), and revised manually as appropriate.
Microme will ultimately provide an infrastructure for the integration of a curated repository of reference pathways and reactions, genome-scale constraint-based models, and downstream applications in comparative genomics and biotechnology.
Genomes: browse, analyse, annotate
The Ensembl Bacteria genome browser can be used for the display and analyses of the 24 bacterial species in our priority list. It provides information on genes, transcripts and comparative genomics views such as gene trees, orthologues, paralogues, multiple alignments and synteny views. Gene entries are cross-referenced to pathways stored in the bacterial Reactome knowledge-base.
Explore Microscope, a platform for microbial comparative genome analysis and manual functional annotation. MicroCyc, developed in the context of Microscope, is a collection of currently ~800 microbial Pathway/Genome databases supported by the Pathway tools software suite.
Pathways: browse, analyse
Our pathway browser provides a interactive visual interface to the Microme database of metabolic pathways and processes, together with links to/from our Ensembl Genomes browser to access the genes encoding catalysts of metabolic reactions. The interface and the database are developed as an adaptation of the open source Reactome platform.
UniPathway is a fully manually curated resource for the representation and annotation of metabolic pathways. All of the pathway data in UniPathway has been extensively cross-linked to existing pathway resources such as KEGG and MetaCyc, as well as sequence resources such as the UniProt KnowledgeBase, for which UniPathway provides a controlled vocabulary for pathway annotation.
The Network Analysis Tools (NeAT) provide a series of modular computer programs specifically designed for the analysis of biological networks, such as network visualisation and comparison, node analyses, clustering, metabolic path finding and pathway extraction.
TeBacten (text mining for bacterial enzymes) is a tool designed to facilitate the retrieval, extraction and annotation of bacterial enzymatic reactions and pathways from the literature.
This release includes complete sets of reactions inferred over thousands of genomes (bacteria and archaea) integrated with data from the first release, and an initial set of 10 tested constraint-based models. Several sources are used for the reaction set: the international nucleotide archives, InterPro annotations, curated associations of InterPro entries and GO terms, curated associations of GO functions to reactions in the RhEA database and curated/predicted genome-protein-reaction associations in the Microscope platform. Read here to know more about the methodology employed.
Summary of statistics:
- Number of genomes: 5575
- Number of CDSs associated to reactions: 4249948
- Number of unique gene-reaction associations: 4889117
- Number of genome-reaction associations: 2214967
- Average number of reactions per genome: 397.3
- Maximum number of reactions in a genome: 1428 (GCA_000019645.1 - Escherichia coli SMS-3-5)
New! Explore the Genome-Reaction Matrix Browsser, a prototype visual interface to the inferred reactions and pathways in the genomes.
- Psomopoulos FE et al. Detection of Genomic Idiosyncrasies using Fuzzy Phylogenetic Profiles. PLoS ONE, 2013
- Bernard T et al. Reconciliation of metabolites and biochemical reactions for metabolic networks. Brief. Bioinformatics, 2012
- Vallenet D et al. MicroScope - an integrated microbial resource for the curation and comparative analysis of genomic and metabolic data Nucl. Acids Res., 2012
- Lobel L et al. Integrative genomic analysis identifies isoleucine and CodY as regulators of Listeria monocytogenes virulence PLoS Genet., 2012