A database of mammalian mitochondrial localisation evidence, phenotypes and diseases
MitoMiner stores experimental mitochondrial localisation evidence from 52 published large-scale GFP tagging and mass-spectrometry studies, targeting sequence predictions from iPSORT, MitoProt, TargetP and MitoFates, and immunological staining results from the Human Protein Atlas. This is integrated with annotation from the Gene Ontology project and UniProt, homology data from Ensembl Compara, phenotype data from ZFIN, SGD and MGI, metabolic pathway data from KEGG, tissue and cancer expression data from the Human Protein Atlas, disease data from OMIM, and interaction data from BioGRID. MitoMiner allows sophisticated data mining queries spanning these many different sources.
MitoMiner can help you determine:
- Whether your gene encodes a mitochondrial protein
- The function of your gene's product
- The homologs of your gene in other species
- The tissue specific expression of your gene
- How your gene's product is involved in cellular metabolism
- Whether your gene is associated with a human disease
- The phenotype of your gene in model organisms
MitoMiner currently contains data for mammals (H. sapiens, M. musculus, R. norvegicus), yeasts (S. pombe, S. cerevisiae) as well as zebrafish (D. rerio).
Attempting to describe which genes in an organism encode proteins that are localised within the mitochondria is important for the investigation of mitochondrial diseases as well as general mitochondrial biology. MitoMiner includes two reference sets of the mitochondrial proteomes: the Broad's MitoCarta Inventory and the new MRC-MBU Integrated Mitochondrial Protein Index (IMPI). The IMPI reference set is a collection of genes that encode proteins with strong evidence for mitochondrial localisation gathered from the MitoMiner database and objectively appraised by machine learning techniques. Many clues to mitochondrial localisation exist such as targeting sequences and mitochondrial specific protein domains, as well as direct evidence for mitochondrial localisation such as mass spectrometry and antibody staining. As no one indicator is as accurate as all indicators combined, IMPI uses machine learning to objectively combine all evidence available for a gene and select those that have similar properties to characterised mitochondrial genes. This approach solves the problem of deciding an arbitrary threshold for what level of evidence can be considered mitochondrial.
MitoMiner, an integrated database for the storage and analysis of mitochondrial proteomics data. Mol Cell Proteomics 8, 1324-37(2009)
MitoMiner: a data warehouse for mitochondrial proteomics data. Nucleic Acids Res 40, D1160-7(2012)
MitoMiner v3.1, an update on the mitochondrial proteomics database. Nucleic Acids Res 44, D1258-61(2016)