New Year - New Major Release

Looks like 2009 will bring a lot of changes for both STRING and STITCH - and to lead the way, STRING has now been upgraded to version 8.0 !

This has been a major upgrade, and it has been some time in the making. We have almost doubled the number of organisms (again), and re-imported all the various pathways, protein-complexes and text-collections. We've also worked a lot behind the scenes, solidifying the API, further automating our data import and updating the way we display orthologous groups, to name just a few examples. All this has been possible only, really, because of our new sponsor - the Swiss Institute of Bioinformatics (SIB). Thanks guys !

More info about this new release is also available from here.

High-resolution images

We recently implemented a way to export high-res images (300 dpi, click the image below to see an example). This feature will go public with STRING 8 / STITCH 2, but if you're now using STRING or STITCH and want to prepare an image for publication, please get in touch with us (mkuhn embl de) and we can send you the image.

How we compute scores (Part 1: experiment channel)

This is in response to a a question that we get quite frequently.
Sorry, it's a bit long - but this way it should contain sufficient detail to roughly understand how our scores come about (for the 'experiments channel' at least, and limited to protein mode). Have fun reading !

Christian von Mering (and Lars Jensen).


Procedure to compute experimental scores

• first, we import information about which proteins have been shown to interact experimentally, from the following databases: INTACT, MINT, GRID, BIND, and DIP. To a small extent, this also includes experimental data that is not necessarily indicative of a direct physical interaction, such as genetic interaction data. Most, however, are from more-or-less direct, physical detection methods.

• then, we map the proteins mentioned in these database onto the proteins in the STRING database - using identifiers, or (if needed) sequences.

• next, we group all interactions by their supporting publication (PMID), and make them non-redundant (they might be reported under the same PMID from several databases). We also expand pulldowns of entire protein complexes using the 'spoke' model (i.e. assuming binary interactions from the tagged/immunoprecipitated protein to all of its co-purified partners).
  

• then, we subdivide all interactions into 'small-scale, medium-scale, and high-throughput', based on the number of interactions reported by a single publication. These three classes are delineated by the extent of overlap with benchmark information, see below.
  

• next, for each of these classes, we determine their 'reliability', by comparing them to our KEGG-benchmark. Briefly, an interaction between two proteins is counted as 'correct', when they are both annotated together in at least one 'KEGG-map', i.e. in at least one functional process / pathway. It is counted as 'incorrect', when the two proteins are annotated in KEGG, but never in the same pathway. Note that proteins that are not annotated at all in KEGG are not considered here).
  

• for the small-scale experiments, which are only very few (per paper), we cannot benchmark each paper separately. Therefor, all such papers are lumped, benchmarked together, and we usually find them to be of quite high quality. As a result, we fix their score to some high number, for example 0.900 in the case of STRING version 7.1
  

• for the medium-scale experiments, a separate score is computed for each publication, in a similar manner (some publications are found to report data of better reliability, other of lower reliability).
  

• for the high-throughput experiments (there are less than 20 of these currently), we have enough information to be even a bit more specific: for each interaction in these sets, we can compute a 'raw score' from the data, because there are so many measurements done. Usually, this would be a score that describes how often a measurement has been confirmed, or how specific a particular interaction is, given the occurence of the two protein elsewhere throughout the dataset. These 'raw scores' are then binned, and each bin benchmarked separately, to arrive at a 'calibration curve', again using the KEGG pathways as a benchmark as described above. Thus, for these large sets, some interactions get a higher score, and others a lower score, depending on the information in the entire dataset.

• this brings us to the cutoffs that determine whether something is small-scale, medium or high-throughput. This is defined on how many 'true-positives' are in the dataset: To be a large-scale dataset, we require at least 50 true positive interactions to enable the benchmarking. Otherwise, more than 20 true positive interactions will make it a medium-scale dataset, and the rest is small-scale. ("true positives" are defined as interactions where both proteins are in KEGG, and are sharing at least one KEGG map).
  

• then, we have to deal with interactions supported by more than one independent dataset (i.e. by more than one publication). For those, the scores are 'added up'. Of course, they are not literally added up, but rather in a probabilistic integration, like so:
  

new_score = 1 - (1 - score_a) * (1 - score_b).

• and finally, whe have to deal with interactions that are reported in multiple organisms, or in an organism other than the one of interest. This is called 'interaction transfer', and is a very important step to increase coverage. It is described in the 2005 STRING paper. Essentially, the better the orthology situation can be delineated (i.e. clear orthologs for both interacting partners can be identified), the bigger the score fraction that is transferred. Transferred interactions are integrated probabilistically as mentioned above, and interactions that are reported in two very similary organisms (say, mouse and rat), are considered redundant and transferred only once. Note that transferred scores are stored separately from the 'direct' scores in the database, so that all the transferred information can be discarded, if desired.

Downtime Wednesday morning

The STRING server will get a new disk tomorrow morning (European time), so there will be a downtime for STRING/STITCH. We hope everything will be working again in the early afternoon.

Update: We're back online, with enough room for the next version of STRING.

API also available on STRING

When I created the API, I only put it on STITCH. Of course there's no reason to not have it also on STRING, so here you go:

http://string.embl.de/api/tsv/interactors?identifier=DRD1_HUMAN

It's in the same state as the STITCH API: Still subject to change, and potentially unstable.

Getting identifiers for a list of genes

If you want to to quickly get identifiers for a long list of items you can use the following command, which uses wget to repeatedly query the API.

cat protein_names.txt | xargs -i wget -nv -O - \
'http://stitch.embl.de/api/tsv-no-header/resolve?identifier={}&species=4932&echo_query=1' \
> protein_identifiers.tsv

I've also introduced another parameter, echo_query, so that you can see your query item in the output.

No Downtime on Saturday, April 26

There'll be an EMBL-wide power cut on Saturday, April 26. Therefore, our servers won't be reachable at this time. We hope that the computer infrastructure will be re-activated by Monday.

Sorry, the plans were changed and not all of EMBL is affected, so we should stay online.