MitoMiner v4.0: an updated database of mitochondrial localization evidence, phenotypes and diseases.
Nucleic acids research
Oxford University Press
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Smith, A., & Robinson, A. (2019). MitoMiner v4.0: an updated database of mitochondrial localization evidence, phenotypes and diseases.. Nucleic acids research, 47 (D1), D1225-D1228. https://doi.org/10.1093/nar/gky1072
Increasing numbers of diseases are associated with mitochondrial dysfunction. This is unsurprising given mitochondria have major roles in bioenergy generation, signalling, detoxification, apoptosis, and biosynthesis. However, fundamental questions of mitochondrial biology remain, including: which nuclear genes encode mitochondrial proteins; how their expression varies with tissue; and which are associated with disease. But experiments to catalogue the mitochondrial proteome are incomplete and sometimes contradictory. This arises because the mitochondrial proteome has tissue- and stage-specific variability, plus differences among experimental techniques and localisation evidence types used. This leads to limitations in each technique’s coverage and inevitably conflicting results. To support identification of mitochondrial proteins, we developed MitoMiner (http://mitominer.mrc-mbu.cam.ac.uk/), a database combining evidence of mitochondrial localisation with information from public resources. Here we report upgrades to MitoMiner, including its re-engineering to be gene-centric to enable easier sharing of evidence among orthologs and support next generation sequencing, plus new data sources, including expression in different tissues, information on phenotypes and diseases of genetic mutations, and a new mitochondrial proteome catalogue. MitoMiner is a powerful platform to investigate mitochondrial localisation by providing a unique combination of experimental sub-cellular localisation datasets, tissue expression, predictions of mitochondrial targetting sequences, gene annotation, and links to phenotype and disease.
Mitochondria, Humans, Mitochondrial Diseases, Mitochondrial Proteins, Proteome, Proteomics, Computational Biology, Phenotype, Internet, Databases, Factual, High-Throughput Nucleotide Sequencing, Data Management
External DOI: https://doi.org/10.1093/nar/gky1072
This record's URL: https://www.repository.cam.ac.uk/handle/1810/286405
Attribution-NonCommercial 4.0 International
Licence URL: https://creativecommons.org/licenses/by-nc/4.0/