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What is MyProteinNet?
Why use MyProteinNet?
Which organisms are supported?
Which types of interactions are included?
Sometime you say gene and sometimes a protein, do you mean a gene or a protein? Do you support alternative spliced proteins?
How does your weighting algorithm assign weight?
How do you filter an interactome by expression data?
How often do you update your interactions database?
How can I contact you?
For additional information:
Contact us at: Esti Yeger-Lotem at estiyl@bgu.ac.il
MyProteinNet is an interactive database that enables users to (i) download current data of protein-protein interactions (PPIs) from a collection of up to 16 different PPI databases, (ii) consolidate these data and create an up-to-date, weighted interactome that favors interactions detected by multiple methods, and (iii) filter the interactome according to gene expression data to create a condition-specific interactome. MyProteinNet is applicable to eleven different organisms ranging from budding yeast to human. For each organism, users can add their own PPI data, and upload expression profiles and expression thresholds by which to filter the resulting interactome. Upon choosing to integrate expression datafor human, users can also filter the interactome by gene expression data of tissue and sub-tissue components from the Novartis gene atlas, the human protein atlas, and the Illumina BodyMap2.0 datasets. The output of MyProteinNet consists of a weighted interactome in the form of a downloadable file in a CSV format, for usage in downstream applications such as Cytoscape, and a summary of global network measures of the output interactome.
MyProteinNet is designed to help researchers obtain meaningful, up-to-date protein interaction networks that are relevant in the context of specific conditions, cellular processes, and tissues. The output networks can provide insight into proteins and interactions that are involved in the studied context (e.g., Barshir et al, PLoS Comp. Biol 2014) and can be analyzed further using other network tools such as Cytoscapeand ResponseNet(Basha et al, NAR 2013).
Which organisms are supported?
We support analysis of eleven organisms, including Homo sapiens, Saccharomyces cerevisiae, Mus musculus and Rattus norvegicus. Not all organisms have all of their protein-protein interactions analyzed for lack of data.
Which types of interactions are included?
MyProteinNet collects from the user-selected databases only protein-protein interactions that were detected experimentally.
Sometime you say gene and sometimes a protein, do you mean a gene or a protein? Do you support alternative spliced proteins?
Most of the available protein-protein interactions data are oblivious to alternatively-spliced isoforms. Therefore, MyProteinNet associates each protein-coding gene with a single protein product, and we use gene and protein interchangeably. We do not currently support alternative-spliced proteins for lack of relevant interactions data.
How does your weighting algorithm assign weight?
After downloading protein interactions data from all selected databases, the algorithm integrates them into a list of non-redundant interactions. The goal of the weighting schemes is to assign probabilistic weights (between 0-1) that reflect the reliability of an interaction based on the reliability of the methods by which it was detected. To assess the reliability of detection methods, we test their success rate in a positive set of interactions and in a negative set of interactions. The positive set contains interactions involving proteins that are reliably annotated to a common cellular process by The Gene Ontology(GO). The negative set contains interactions involving proteins that are not annotated to a common cellular process. The sets are created by downloading an up-to-date Gene Ontology information from the MyGene.infoweb-service. Users can limit the maximal size of the GO process (namely the number of genes annotated to it), can limit the GO evidence codes by which gene are annotated, and can limit the set of cellular processes to those that contain specific keywords. The latter option of using keywords is denoted as the ‘GO process biased’, and can be used to favor interactions that occur in specific cellular processes such as signaling or development. More details can be found in Yeger-Lotem et al., Nature Genetics 2009.
How do you filter an interactome by expression data?
When users select to integrate expression data they provide a threshold. Only genes whose expression is ≥ the user-defined threshold are considered to be expressed. MyProteinNet filters out interactions that involve partners that are not considered to be expressed. Thus, only interactions in which both partners are considered to be expressed will be included in the filtered interactome. The limitation by expression threshold does not guarantee that an interaction indeed occurs, however it is a necessary requirement. Both the filtered and unfiltered interactomes are downloadable.
How often do you update your interactions database?
Downloading databases takes roughly a day. To avoid a long waiting time for the user, we keep a local copy of all databases on our servers. The local copy is updated automatically every fortnight. The date of the last update is displayed in the summary table in the output.
Please email Esti Yeger-Lotem at estiyl@bgu.ac.il
We would love to hear from you!
References
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ResponseNet: revealing signaling and regulatory networks linking genetic and transcriptomic screening data. Nucleic Acids Res. 39 (suppl 2): W424-W429
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Yeger-Lotem E., Riva L., Su L.J., Gitler A., Cashikar A., King O.D., Auluck P.K., Geddie M.L., Valastyan J.S., Karger D.R., Lindquist S., Fraenkel E. (2009). Bridging the gap between high-throughput genetic and transcriptional data reveals cellular pathways responding to alpha-synuclein toxicity. Nature Genetics 41: 316 – 323.