between any of the key data types can be provided for easily. Data sources from collaborators are mirrored to ensure that canSAR is up-to-date. In order to process these exter- nal sources into a format suitable for integration into canSAR, a suite of pipelines have been created which allows the seamless integration of new data from existing sources, and most importantly, check for uniqueness among the different data sources. A fraction of the data within canSAR is duplicated in several data sources and it is imperative that this redundancy is recorded and that the useful information for each duplicate is extracted. In some cases, this is reasonably trivial, but in others (compounds and molecular targets are good example), accurate and appropriate tests for uniqueness and data storage are challenging. Most of the data pipelines involved the fol- lowing steps: acquisitions of local copy, pre-processing into suitable format, uniqueness testing of existing data, loading of data into canSAR and rebuilding of data source logs. Some of the data sources are updated monthly, however some are updated more frequently or at variable intervals. Each of the speciﬁc pipelines is adapted to suit the needs of the speciﬁc data source. Where possible and appropriate, a source’s API is utilized through the use of simple REST or SOAP services. This has the beneﬁt of ensuring that the data are as up-to-date as possible and removes steps of mirroring and integrating. To ensure long-term growth and utility, canSAR provides ﬂexibility for new data and data types. Providing the centralization and ability to rapidly interro- gate such disparate data is of immediate beneﬁt to users who can now access data in seconds that would have taken days or weeks to compile. Figure 2 illustrates some common information requests taken into consideration in the design of canSAR. Nucleic Acids Research, 2012, Vol. 40, Database issue D949 Data content canSAR contains data pertaining to the entire human proteome as represented by UniProt (8) human sequences and RefSeq (9) human transcriptome. Additionally, a large number of model organism proteins are included where useful data are available for them. Family annota- tion from Pfam (10) or manual curation by the canSAR team is provided. Predicted secretion signals, transmem- brane regions and secondary structures are calculated using standard methods [SignalP (11), TMHMM (12) and PSIPRED (13)]. Currently, canSAR contains data from four publicly available chemical screening data sources. The most ex- pansive is ChEMBL, which contains 5.4 million activities from 42 500 different publications (correct as of August 2011) and is updated approximately once a month. The next largest data set is the NCI-60 panel of 60 diverse human cancer cell lines screened against more than 100 000 compounds and natural products (14). This cur- rently supplies canSAR with 2 663 013 activities. Other sources include Binding DB (15) (620 198 activities) and the Genomics of Drug Sensitivity project (8498 activities). Compound structures obtained from the resources men- tioned above, or manually entered by the canSAR team (e.g. some clinical candidates), are all assessed, standard- ized and registered against a strict pipeline that handles chemical duplicates, identiﬁes alternative salt forms and stereoisomers. The compounds are stored in the database using the Accelrys Direct chemical cartridge for Oracle to allow compound searching. A large set of descriptors is calculated for each of the compounds including molecular weight, polar surface area, number of hydrogen-bond donors and acceptors and whether they contain toxico- phores or reactive groups (16). To enable clustering of Figure 2. Common requested information in cancer translational research used to design the user interface. Query can be from a biological or chemical starting point. Users often require complex, interconnected but concise information. The data reports generated in canSAR are used to underpin subsequent discussions and the design of the next experiment.