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a catalog of worldwide biological databases

There has been a large growth in the number of biomedical publications that report experimental results. Many of these results concern detection of protein-protein interactions (PPI). In BioCreative V, we participated in the BioC task and developed a PPI system to detect text passages with PPIs in the full-text articles. By adopting the BioC format, the output of the system can be seamlessly added to the biocuration pipeline with little effort required for the system integration. A distinctive feature of our PPI system is that it utilizes extended dependency graph, an intermediate level of representation that attempts to abstract away syntactic variations in text. As a result, we are able to use only a limited set of rules to extract PPI pairs in the sentences, and additional rules to detect additional passages for PPI pairs. For evaluation, we used the 95 articles that were provided for the BioC annotation task. We retrieved the unique PPIs from the BioGRID database for these articles and show that our system achieves a recall of 83.5%. In order to evaluate the detection of passages with PPIs, we further annotated Abstract and Results sections of 20 documents from the dataset and show that an f-value of 80.5% was obtained. To evaluate the generalizability of the system, we also conducted experiments on AIMed, a well-known PPI corpus. We achieved an f-value of 76.1% for sentence detection and an f-value of 64.7% for unique PPI detection.Database URL: http://proteininformationresource.org/iprolink/corpora.

The Protein Information Resource (PIR; http://www-nbrf.georgetown. edu/pir/) supports research on molecular evolution, functional genomics, and computational biology by maintaining a comprehensive, non-redundant, well-organized and freely available protein sequence database. Since 1988 the database has been maintained collaboratively by PIR-International, an international association of data collection centers cooperating to develop this resource during a period of explosive growth in new sequence data and new computer technologies. The PIR Protein Sequence Database entries are classified into superfamilies, families and homology domains, for which sequence alignments are available. Full-scale family classification supports comparative genomics research, aids sequence annotation, assists database organization and improves database integrity. The PIR WWW server supports direct on-line sequence similarity searches, information retrieval, and knowledge discovery by providing the Protein Sequence Database and other supplementary databases. Sequence entries are extensively cross-referenced and hypertext-linked to major nucleic acid, literature, genome, structure, sequence alignment and family databases. The weekly release of the Protein Sequence Database can be accessed through the PIR Web site. The quarterly release of the database is freely available from our anonymous FTP server and is also available on CD-ROM with the accompanying ATLAS database search program.