Perhaps most importantly, we now accept submissions of proteins using any identifiers understood by the EBI’S Protein Identifier Cross-Reference service (PICR). FuncNet uses PICR to automatically convert them into their corresponding UniProt primary accessions.

For best coverage and least ambiguity, we still recommend that you use UniProt identifiers of some kind, either accessions or IDs (e.g. Q5SRD3, AGAP8_HUMAN).

We’ve added informational mouseover tooltips to many of the elements of the user interface, e.g. protein accessions and service names, and provided links to further information.

Also, the results page has undergone a major overhaul.

Firstly, large result sets are now viewed one page at a time, where a page contains ten rows of results.

Secondly, you now have the option of viewing either:

1. The overall functional rankings of your query proteins against the reference set as a whole — the default, equivalent of the RetrieveSetwiseScores operation in the web services
2. The individual pairwise associations between query proteins and reference proteins — the equivalent of the RetrieveCompleteScores operation

Both result views are supported in the XML and CSV download links too.

These new features should be considered ’slightly experimental’, and if you have any problems with them, or suggestions for making them better, please let us know.

Firstly, we’ve redesigned the FuncNet site, which you’ve probably noticed, unless you’re reading this via RSS. Hope you like it.

Secondly, we’ve added a new suite of predictors using a method called GOSS. This is a GO-based semantic similarity measure, like engineDB, but allows individual queries by the three GO subtrees: biological process (BP), cellular component (CC) and molecular function (MF).

Thirdly, we’ve expanded the section of the site dedicated to human spindle protein prediction, with a new prediction service called DORA-Spindle (DORA = Domain Over-Representation Analysis). This spots proteins containing domains which also appear in known spindle proteins. The FuncNet-SpindleP integration package has now been replaced with a new version called just FuncNet-Spindle, which queries FuncNet, SpindleP and DORA-Spindle in parallel and integrates the results. See the spindle prediction page for more details.

New scoring method

We’ve changed quite a few things for this release. For a start, we’ve implemented a new scoring method that assigns every query protein a single overall score, rating its overall functional similarity to the reference set. This is calculated by forming an ‘optimal score profile’, a vector with one element in it for each predictor in the query:

So each query protein has a cell for each predictor, which will contain a p-value.

Then, we populate each cell with the best prediction (lowest p-value) from that predictor, between the query protein and any reference protein, leaving blanks where a predictor has made no predictions at all about a given query protein. For example:

Finally, using Fisher’s method (unweighted), we calculate an overall score for each query protein by combining these p-values:

The higher this score is, the more closely associated the query protein is with the biological function performed by the reference proteins.

The web interface now uses this method to score and rank the results, and the front-end web service supports an operation called RetrieveSetwiseScores so you can use it in your scripts.

More new features in web interface

We’ve also added a feature that lets you select a GO term if you want to supply a reference set automatically. This queries the EBI’s web services for all the terms matching your search string, and when you select one, it retrieves all the human proteins associated with that term or any of its descendants by high-quality annotations (IDA, IEP, IGI, IMP, RCA, TAS). We would be grateful for any feedback on using this new experimental feature.

Follow us on Twitter

We now have a Twitter account where regular status updates appear: http://twitter.com/FuncNet

http://funcnet.eu/mailing-lists/

See the SpindleP integration page for more details.

Funded by the ENFIN network and developed by the EBI’s Proteomics Services Team, EnVision 2 uses a rich data model that allows sets of proteins to be annotated with the results of successive ‘experiments’ (analysis jobs via different servers), with a complete audit trail maintained.

A wrapper service allowing FuncNet to understand the ENFIN XML format was developed by Heinz Stockinger, and its WSDL is available here.

This allows you to submit jobs, check on their status in real time, and retrieve the results when they’re done, in a tabular display with links out to UniProt, or as an XML file like those generated by the front-end web service.

In order to use it, you must first prepare two files on your local computer, one containing a set of query proteins (unknown/partially-understood function), and one with reference proteins (well-understood function). These should be plain text files, with one UniProt primary accession per line. Remember that FuncNet currently only supports human proteins.

This interface is really only intended for smaller queries — we recommend that you use the web services directly for queries over about a hundred proteins, although feel free to try anyway, we haven’t imposed any limits. If you need any help or advice, please contact us at the usual mailbox ‘info’ at ‘funcnet.eu’.