MitoMiner Support

We have now fixed some intermittent problems with our mail server, which had caused the 'contact us' form to fail. If you have been having trouble with the form please try again! Our email address - mitominer@mrc-mbu.cam.ac.uk - which can also be used to contact us, was unaffected.

MitoMiner has been upgraded and now includes mitochondrial targeting sequence predictions from iPSORT, MitoProt and TargetP as well as 2 new GFP datasets for Saccharomyces cerevisiae. All of the experimental data has been remapped and the user interface has been altered to be more gene focused. The underlying version of the InterMine software has been upgraded to 1.2.2 and includes numerous bug fixes and enhancements to the user interface. Users with accounts in MitoMiner 3.0 should find their data is copied over to the new version.

If you have any questions, problems, bug reports or general feedback about the new release please contact us as MitoMiner [at] mrc-mbu.cam.ac.uk . 

MitoMiner will be offline from 1.30pm GMT for approximately 6 hours to allow for an exciting new update.

MitoMiner will be  down from 1.30pm GMT for approximately 30 mins for maintenance.

Apologies for any inconvenience this may cause.

MitoMiner has been updated to include the latest version of the IMPI reference set. 

MitoMiner has had a minor update on Saturday 22nd of June to update UniProt and the HomoloGene mappings.

MitoMiner now includes a new reference set - IMPI (Integrated Mitochondrial Protein Index) for Human, Mouse and Rat.

The IMPI reference set is a collection of proteins with strong evidence for mitochondrial localisation developed using machine learning. Many clues to mitochondrial localisation exist such as targeting sequences as well as direct evidence for mitochondrial localisation such as mass spectrometry and antibody staining. As no one indicator is as as accurate as all indicators combined, IMPI uses two machine learning techniques to objectively combine all evidence available for a protein. This includes the mass spectrometry and GFP tagging experimental data from the studies included in MitoMiner, targeting sequence predictions from 3 programs, and antibody staining from the Human Protein Atlas. The machine learning then selects proteins that have similar properties and evidence to characterised mitochondrial proteins whilst discarding those most similar to known non-mitochondrial proteins. The final list combines the list of characterised mitochondrial proteins with those from the machine learning to produce the IMPI reference set. This provides a comprehensive dataset and solves the problem of deciding an arbitrary threshold for what level of evidence can be considered "mitochondrial".

MitoMiner has had a significant upgrade and now includes data from the Human Protein Atlas and InterPro (full details below). This version also includes numerous bug fixes and enhancements to the user interface. Users with accounts in MitoMiner 2.1 should find their data is copied over to the new version. If you have any questions, problems, bug reports or general feedback about the new release please contact us as MitoMiner [at] mrc-mbu.cam.ac.uk . 

MitoMiner will be unavailable on Tuesday 14th of May to allow the deployment of MitoMiner version 3.0! This has been rescheduled from Monday to insure that all the new features are working as envisaged. 

Sorry for any inconvenience.