Michigan Molecular Interactions (MiMI)

Category Cross-Omics>Knowledge Bases/Databases/Tools

Abstract Michigan Molecular Interactions (MiMI) is part of the NIH's National Center for Integrative Biomedical Informatics (NCIBI). It provides access to the knowledge and data merged and integrated from numerous 'protein interactions' databases.

It augments this information from many other biological sources. You can link out to these other databases and auxiliary sources from MiMI, as well. They include:

BIND, BioGRID, CCSB at Harvard, CPath (see G6G Abstract Number 20098), DIP, Gene Ontology (GO), HPRD, IntAct, InterPro, IPI, KEGG, Max Delbreuck Center, MiBLAST, NCBI Gene, OrganellDB, Ortho MCL, PFam, ProtoNet, PubMed, PubMed NLP Mining, and Reactome (see G6G Abstract Number 20267).

MiMI merges data from these sources with "deep integration" (see 'MiMI Merge Process' section below...), into its single database.

A simple yet advanced user interface enables you to query the database, freeing you from the onerous task of having to know the data format or having to learn a query language.

MiMI allows you to query all data, whether corroborative or contradictory, and specify which sources to utilize.

MiMI displays results of your queries in easy-to-browse interfaces and provides you with workspaces to explore and analyze the results.

Among these workspaces is an interactive network of protein-protein interactions displayed in Cytoscape (see G6G Abstract Number 20092) and accessed through MiMI via a MiMI Cytoscape plug-in.

Tasks you can perform with MiMI -- With MiMI you can explore publicly available data on genes and gene products and find relationships based on biological concepts, canonical pathways, and semantic text mining.

From this rich array of data, MiMI helps you uncover previously unknown knowledge within and across organisms. Insights you gain can lead to novel hypotheses about mechanisms of disease or other biological processes that you can test through further experimentation.

Specifically, with MiMI you can conduct the following analytical tasks:

1) Search - Specifiy target genes or keywords and move directly to information about them and link out to other sources, e.g. to PubMed articles, PFam, etc.

Streamline your search process to find confirmatory articles that you can cite in a manuscripts, proposals or presentations.

2) Browse - Enter queries and explore results to confirm your expectations, prior knowledge, or findings from other bioinformatics methodologies.

Uncover previously unknown information that pertains to your research - e.g. pairwise protein interactions that are new to you. Locate, skim and download potentially relevant articles quickly.

Scan Natural Language Processing (NLP)-extracted passages relevant to formulating a hypothesis.

3) Distinguish and validate relationships of interest - Find multidimensional relationships between genes or gene products, e.g. genes with similar molecular functions and/or shared pathways, other organisms in which genes of interest are found.

Gain confidence in potentially interesting relationships by seeing the number of interactions, types and counts of experiments identifying an interaction, articles discussing a relationship or interaction.

Filter to adjust the relationships of interest and either export or further explore that information.

Link out for additional details about the molecules and interactions relevant to your research, e.g. go to BLAST, OMIM, PubMed or semantically summarized extracts (through GIN), Gene2MeSH for enriched MeSH terms related to a selected gene.

4) Analyze multidimensional relationships - Move to Netbrowser, an interactive visualization workspace linked to MiMI Web, for a quick way of exploring such associations as direct and indirect interactions, conceptual similarities, shortest paths between proteins, and shared pathways.

Move to Cytoscape, an interactive visualization workspace linked to MiMI Web, for more in-depth exploratory analysis to uncover sub-graphs, map sub-graphs to associated canonical pathways [via SAGA (see G6G Abstract Number 20312)], to expand select neighbors, to cluster by biological concepts, to find the shortest paths.

Use filtering and perceptual encoding in Cytoscape (e.g. color, shape, size) to isolate relationships of interest and visually bring in more attributes.

5) Infer causal associations relevant to disease mechanisms - Move to Cytoscape to relate MiMI data to your own data for explanatory insights about causes and effects, e.g. expression data.

6) Save and export information - Use MiMI and Cytoscape save and export functions to share information with colleagues. Annotate genes, gene products and interactions in Cytoscape, save them for the next session(s), and if desired share them publicly.

What is Unique about MiMI -- MiMI gives you access to more information than you can get from any one 'protein interaction' source.

1) In MiMI, unlike in individual resources, you can use many different synonyms to find a protein and any number of protein identifiers.

2) MiMi lets you query all fields for your search term or only specified fields (e.g. p53 only in molecule name).

3) MiMI provides result sets on pair-wise interactions and presents information and link-outs to integrated tools such as Cytoscape that help you infer indirect interactions.

4) MiMI presents provenance to help you determine your confidence in displayed details and to make judgments about descriptive information that may be contradictory.

5) MiMI lets you quickly see the gene and gene product information that is available across source databases. For example, it presents GO annotations when any one of the databases includes it. A blank field shows you that this information is missing across biological resources.

The MiMI Merge Process -- Protein interaction data exists in a number of repositories. Each repository has its own data format, molecule identifier, and supplementary information.

MiMI gathers data from well-known protein interaction databases and deep-merges the information.

Utilizing an identity function, molecules that may have different identifiers but represent the same real-world object are merged.

Thus, MiMI allows the user to retrieve information from many different databases at once, highlighting complementary and contradictory information. There are several steps needed to create the final MiMI dataset. They are:

1) The original source datasets are obtained, and transformed into the MiMI schema, except KEGG, NCBI Gene, Uniprot, Ensembl.

2) Molecules that can be rolled into a gene are annotated to that gene record.

3) Using all known identifiers of a merged molecule, sources such as OrganelleDB or miBLAST, are queried to annotate specific molecular fields.

4) The resulting dataset is loaded into a relational database.

Because this is an automated process, and No curation occurs, any errors or misnomers in the original data sources will also exist in MiMI. For example, if a source indicates that the organism is unknown, MiMI will as well.

System Requirements

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The National Center for Integrative Biomedical Informatics (NCIBI). NCIBI is based at the University of Michigan as a part of The Center for Computational Medicine and Biology (CCMB).

For SAGA/TALE support and/or any questions regarding SAGA/TALE email mimi-help@umich.edu.

Manufacturer Web Site MiMI

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G6G Abstract Number 20311

G6G Manufacturer Number 101826