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This article reports the use of the BioC standard format in our sentence simplification system, iSimp, and demonstrates its general utility. iSimp is designed to simplify complex sentences commonly found in the biomedical text, and has been shown to improve existing text mining applications that rely on the analysis of sentence structures. By adopting the BioC format, we aim to make iSimp readily interoperable with other applications in the biomedical domain. To examine the utility of iSimp in BioC, we implemented a rule-based relation extraction system that uses iSimp as a preprocessing module and BioC for data exchange. Evaluation on the training corpus of BioNLP-ST 2011 GENIA Event Extraction (GE) task showed that iSimp sentence simplification improved the recall by 3.2% without reducing precision. The iSimp simplification-annotated corpora, both our previously used corpus and the GE corpus in the current study, have been converted into the BioC format and made publicly available at the project's Web site: http://research.bioinformatics.udel.edu/isimp/. Database URL:http://research.bioinformatics.udel.edu/isimp/, (© The Author(s) 2014. Published by Oxford University Press.)

The Protein Ontology (PRO; http://proconsortium.org) formally defines protein entities and explicitly represents their major forms and interrelations. Protein entities represented in PRO corresponding to single amino acid chains are categorized by level of specificity into family, gene, sequence and modification metaclasses, and there is a separate metaclass for protein complexes. All metaclasses also have organism-specific derivatives. PRO complements established sequence databases such as UniProtKB, and interoperates with other biomedical and biological ontologies such as the Gene Ontology (GO). PRO relates to UniProtKB in that PRO's organism-specific classes of proteins encoded by a specific gene correspond to entities documented in UniProtKB entries. PRO relates to the GO in that PRO's representations of organism-specific protein complexes are subclasses of the organism-agnostic protein complex terms in the GO Cellular Component Ontology. The past few years have seen growth and changes to the PRO, as well as new points of access to the data and new applications of PRO in immunology and proteomics. Here we describe some of these developments.

Protein phosphorylation is a central regulatory mechanism in signal transduction involved in most biological processes. Phosphorylation of a protein may lead to activation or repression of its activity, alternative subcellular location and interaction with different binding partners. Extracting this type of information from scientific literature is critical for connecting phosphorylated proteins with kinases and interaction partners, along with their functional outcomes, for knowledge discovery from phosphorylation protein networks. We have developed the Extracting Functional Impact of Phosphorylation (eFIP) text mining system, which combines several natural language processing techniques to find relevant abstracts mentioning phosphorylation of a given protein together with indications of protein-protein interactions (PPIs) and potential evidences for impact of phosphorylation on the PPIs. eFIP integrates our previously developed tools, Extracting Gene Related ABstracts (eGRAB) for document retrieval and name disambiguation, Rule-based LIterature Mining System (RLIMS-P) for Protein Phosphorylation for extraction of phosphorylation information, a PPI module to detect PPIs involving phosphorylated proteins and an impact module for relation extraction. The text mining system has been integrated into the curation workflow of the Protein Ontology (PRO) to capture knowledge about phosphorylated proteins. The eFIP web interface accepts gene/protein names or identifiers, or PubMed identifiers as input, and displays results as a ranked list of abstracts with sentence evidence and summary table, which can be exported in a spreadsheet upon result validation. As a participant in the BioCreative-2012 Interactive Text Mining track, the performance of eFIP was evaluated on document retrieval (F-measures of 78-100%), sentence-level information extraction (F-measures of 70-80%) and document ranking (normalized discounted cumulative gain measures of 93-100% and mean average precision of 0.86). The utility and usability of the eFIP web interface were also evaluated during the BioCreative Workshop. The use of the eFIP interface provided a significant speed-up (∼2.5-fold) for time to completion of the curation task. Additionally, eFIP significantly simplifies the task of finding relevant articles on PPI involving phosphorylated forms of a given protein.

Molecular biology has become heavily dependent on biological knowledge encoded in expert curated biological databases. As the volume of biological literature increases, biocurators need help in keeping up with the literature; (semi-) automated aids for biocuration would seem to be an ideal application for natural language processing and text mining. However, to date, there have been few documented successes for improving biocuration throughput using text mining. Our initial investigations took place for the workshop on 'Text Mining for the BioCuration Workflow' at the third International Biocuration Conference (Berlin, 2009). We interviewed biocurators to obtain workflows from eight biological databases. This initial study revealed high-level commonalities, including (i) selection of documents for curation; (ii) indexing of documents with biologically relevant entities (e.g. genes); and (iii) detailed curation of specific relations (e.g. interactions); however, the detailed workflows also showed many variabilities. Following the workshop, we conducted a survey of biocurators. The survey identified biocurator priorities, including the handling of full text indexed with biological entities and support for the identification and prioritization of documents for curation. It also indicated that two-thirds of the biocuration teams had experimented with text mining and almost half were using text mining at that time. Analysis of our interviews and survey provide a set of requirements for the integration of text mining into the biocuration workflow. These can guide the identification of common needs across curated databases and encourage joint experimentation involving biocurators, text mining developers and the larger biomedical research community.

InterPro (http://www.ebi.ac.uk/interpro/) is a database that integrates diverse information about protein families, domains and functional sites, and makes it freely available to the public via Web-based interfaces and services. Central to the database are diagnostic models, known as signatures, against which protein sequences can be searched to determine their potential function. InterPro has utility in the large-scale analysis of whole genomes and meta-genomes, as well as in characterizing individual protein sequences. Herein we give an overview of new developments in the database and its associated software since 2009, including updates to database content, curation processes and Web and programmatic interfaces.

High-throughput proteomic, microarray, protein interaction and other experimental methods all generate long lists of proteins and/or genes that have been identified or have varied in accumulation under the experimental conditions studied. These lists can be difficult to sort through for Biologists to make sense of. Here we describe a next step in data analysis--a bottom-up approach at data integration--starting with protein sequence identifications, mapping them to a common representation of the protein and then bringing in a wide variety of structural, functional, genetic, and disease information related to proteins derived from annotated knowledge bases and then using this information to categorize the lists using Gene Ontology (GO) terms and mappings to biological pathway databases. We illustrate with examples how this can aid in identifying important processes from large complex lists.

The rapid growth of protein sequence databases has necessitated the development of methods to computationally derive annotation for uncharacterized entries. Most such methods focus on "global" annotation, such as molecular function or biological process. Methods to supply high-accuracy "local" annotation to functional sites based on structural information at the level of individual amino acids are relatively rare. In this chapter we will describe a method we have developed for annotation of functional residues within experimentally-uncharacterized proteins that relies on position-specific site annotation rules (PIR Site Rules) derived from structural and experimental information. These PIR Site Rules are manually defined to allow for conditional propagation of annotation. Each rule specifies a tripartite set of conditions whereby candidates for annotation must pass a whole-protein classification test (that is, have end-to-end match to a whole-protein-based HMM), match a site-specific profile HMM and, finally, match functionally and structurally characterized residues of a template. Positive matches trigger the appropriate annotation for active site residues, binding site residues, modified residues, or other functionally important amino acids. The strict criteria used in this process have rendered high-confidence annotation suitable for UniProtKB/Swiss-Prot features.

Technologies and experimental strategies have improved dramatically in the field of genomics and proteomics facilitating analysis of cellular and biochemical processes, as well as of proteins networks. Based on numerous such analyses, there has been a significant increase of publications in life sciences and biomedicine. In this respect, knowledge bases are struggling to cope with the literature volume and they may not be able to capture in detail certain aspects of proteins and genes. One important aspect of proteins is their phosphorylated states and their implication in protein function and protein interacting networks. For this reason, we developed eFIP, a web-based tool, which aids scientists to find quickly abstracts mentioning phosphorylation of a given protein (including site and kinase), coupled with mentions of interactions and functional aspects of the protein. eFIP combines information provided by applications such as eGRAB, RLIMS-P, eGIFT and AIIAGMT, to rank abstracts mentioning phosphorylation, and to display the results in a highlighted and tabular format for a quick inspection. In this chapter, we present a case study of results returned by eFIP for the protein BAD, which is a key regulator of apoptosis that is posttranslationally modified by phosphorylation.

Genomic, proteomic, and other omic-based approaches are now broadly used in biomedical research to facilitate the understanding of disease mechanisms and identification of molecular targets and biomarkers for therapeutic and diagnostic development. While the Omics technologies and bioinformatics tools for analyzing Omics data are rapidly advancing, the functional analysis and interpretation of the data remain challenging due to the inherent nature of the generally long workflows of Omics experiments. We adopt a strategy that emphasizes the use of curated knowledge resources coupled with expert-guided examination and interpretation of Omics data for the selection of potential molecular targets. We describe a downstream workflow and procedures for functional analysis that focus on biological pathways, from which molecular targets can be derived and proposed for experimental validation.

In the past decades, a variety of publicly available data repositories and resources have been developed to support protein related information management, data-driven hypothesis generation and biological knowledge discovery. However, there is also an increasing confusion for the researchers who are trying to quickly find the appropriate resources to help them solve their problems. In this chapter, we present a comprehensive review (with categorization and description) of major protein bioinformatics databases and resources that are relevant to comparative proteomics research. We conclude the chapter by discussing the challenges and opportunities for developing new protein bioinformatics databases.

We analyzed the envelope proteins in pathogenic flaviviruses to determine whether there are sequence signatures associated with the tendency of viruses to produce hemorrhagic disease (H-viruses) or encephalitis (E-viruses). We found that, at the position corresponding to the glycosylated Asn-67 in dengue virus, asparagine (Asn) occurs in all seven viral species that cause hemorrhagic disease in humans. Furthermore, Asn was extremely rare at position 67 in six flaviviruses that cause encephalitis, being replaced by Asp in four of them. Of the 3,246 sequences from H- and E-viruses, we found that 2,916 sequences (90%) contained Asn in position 67 for H-viruses or Asp in position 67 for E-viruses. The change from Asn-67 that is prevalent in H-viruses to Asp-67 (common in E-viruses) contributes to a stronger electrostatically negative surface in the E-viruses as compared to the H-viruses. These findings should help predicting the disease potential of emerging and re-emerging flaviviruses and understanding the relationship between protein structure and disease outcome.

The InterPro database (http://www.ebi.ac.uk/interpro/) integrates together predictive models or 'signatures' representing protein domains, families and functional sites from multiple, diverse source databases: Gene3D, PANTHER, Pfam, PIRSF, PRINTS, ProDom, PROSITE, SMART, SUPERFAMILY and TIGRFAMs. Integration is performed manually and approximately half of the total approximately 58,000 signatures available in the source databases belong to an InterPro entry. Recently, we have started to also display the remaining un-integrated signatures via our web interface. Other developments include the provision of non-signature data, such as structural data, in new XML files on our FTP site, as well as the inclusion of matchless UniProtKB proteins in the existing match XML files. The web interface has been extended and now links out to the ADAN predicted protein-protein interaction database and the SPICE and Dasty viewers. The latest public release (v18.0) covers 79.8% of UniProtKB (v14.1) and consists of 16 549 entries. InterPro data may be accessed either via the web address above, via web services, by downloading files by anonymous FTP or by using the InterProScan search software (http://www.ebi.ac.uk/Tools/InterProScan/).

We have identified 72 completely conserved amino acid residues in the E protein of major groups of the Flavivirus genus by computational analyses. In the dengue species we have identified 12 highly conserved sequence regions, 186 negatively selected sites, and many dengue serotype-specific negatively selected sites. The flavivirus-conserved sites included residues involved in forming six disulfide bonds crucial for the structural integrity of the protein, the fusion motif involved in viral infectivity, and the interface residues of the oligomers. The structural analysis of the E protein showed 19 surface-exposed non-conserved residues, 128 dimer or trimer interface residues, and regions, which undergo major conformational change during trimerization. Eleven consensus T(h)-cell epitopes common to all four dengue serotypes were predicted. Most of these corresponded to dengue-conserved regions or negatively selected sites. Of special interest are six singular sites (N(37), Q(211), D(215), P(217), H(244), K(246)) in dengue E protein that are conserved, are part of the predicted consensus T(h)-cell epitopes and are exposed in the dimer or trimer. We propose these sites and corresponding epitopic regions as potential candidates for prioritization by experimental biologists for development of diagnostics and vaccines that may be difficult to circumvent by natural or man-made alteration of dengue virus.

In the post-genome era, researchers are systematically tackling gene functions and complex regulatory processes by studying organisms on a global scale; however, a major challenge lies in the voluminous, complex, and dynamic data being maintained in heterogeneous sources, especially from proteomics experiments. Advanced computational methods are needed for integration, mining, comparative analysis, and functional interpretation of high-throughput proteomic data. In the first part of this review, we discuss aspects of data integration important for capturing all data relevant to functional analysis. We provide a list of databases commonly used in genomics and proteomics and explain strategies to connect the source data, with especial emphasis on our ID mapping service. Next, we describe iProClass, a central data infrastructure that supports both data integration and functional annotation of proteins, and give a brief introduction to the data search/retrieval and analysis tools currently available at our website (http://pir.georgetown.edu) that researchers can use for large-scale functional analysis. In the last part, we introduce iProXpress (integrated Protein eXpression), an integrated research and discovery platform for large-scale expression data analysis, and we show a prototype that has been useful for organelle proteome analysis.

Motivation: Redundant protein sequences in biological databases hinder sequence similarity searches and make interpretation of search results difficult. Clustering of protein sequence space based on sequence similarity helps organize all sequences into manageable datasets and reduces sampling bias and overrepresentation of sequences., Results: The UniRef (UniProt Reference Clusters) provide clustered sets of sequences from the UniProt Knowledgebase (UniProtKB) and selected UniProt Archive records to obtain complete coverage of sequence space at several resolutions while hiding redundant sequences. Currently covering >4 million source sequences, the UniRef100 database combines identical sequences and subfragments from any source organism into a single UniRef entry. UniRef90 and UniRef50 are built by clustering UniRef100 sequences at the 90 or 50% sequence identity levels. UniRef100, UniRef90 and UniRef50 yield a database size reduction of approximately 10, 40 and 70%, respectively, from the source sequence set. The reduced redundancy increases the speed of similarity searches and improves detection of distant relationships. UniRef entries contain summary cluster and membership information, including the sequence of a representative protein, member count and common taxonomy of the cluster, the accession numbers of all the merged entries and links to rich functional annotation in UniProtKB to facilitate biological discovery. UniRef has already been applied to broad research areas ranging from genome annotation to proteomics data analysis., Availability: UniRef is updated biweekly and is available for online search and retrieval at http://www.uniprot.org, as well as for download at ftp://ftp.uniprot.org/pub/databases/uniprot/uniref., Supplementary Information: Supplementary data are available at Bioinformatics online.

The PIRSF protein classification system (http://pir.georgetown.edu/pirsf/) reflects evolutionary relationships of full-length proteins and domains. The primary PIRSF classification unit is the homeomorphic family, whose members are both homologous (evolved from a common ancestor) and homeomorphic (sharing full-length sequence similarity and a common domain architecture). PIRSF families are curated systematically based on literature review and integrative sequence and functional analysis, including sequence and structure similarity, domain architecture, functional association, genome context, and phyletic pattern. The results of classification and expert annotation are summarized in PIRSF family reports with graphical viewers for taxonomic distribution, domain architecture, family hierarchy, and multiple alignment and phylogenetic tree. The PIRSF system provides a comprehensive resource for bioinformatics analysis and comparative studies of protein function and evolution. Domain or fold-based searches allow identification of evolutionarily related protein families sharing domains or structural folds. Functional convergence and functional divergence are revealed by the relationships between protein classification and curated family functions. The taxonomic distribution allows the identification of lineage-specific or broadly conserved protein families and can reveal horizontal gene transfer. Here we demonstrate, with illustrative examples, how to use the web-based PIRSF system as a tool for functional and evolutionary studies of protein families.

Motivation: Our purpose is to develop a statistical modeling approach for cancer biomarker discovery and provide new insights into early cancer detection. We propose the concept of dependence network, apply it for identifying cancer biomarkers, and study the difference between the protein or gene samples from cancer and non-cancer subjects based on mass-spectrometry (MS) and microarray data., Results: Three MS and two gene microarray datasets are studied. Clear differences are observed in the dependence networks for cancer and non-cancer samples. Protein/gene features are examined three at one time through an exhaustive search. Dependence networks are constructed by binding triples identified by the eigenvalue pattern of the dependence model, and are further compared to identify cancer biomarkers. Such dependence-network-based biomarkers show much greater consistency under 10-fold cross-validation than the classification-performance-based biomarkers. Furthermore, the biological relevance of the dependence-network-based biomarkers using microarray data is discussed. The proposed scheme is shown promising for cancer diagnosis and prediction., Availability: See supplements: http://dsplab.eng.umd.edu/~genomics/dependencenetwork/

InterPro is an integrated resource for protein families, domains and functional sites, which integrates the following protein signature databases: PROSITE, PRINTS, ProDom, Pfam, SMART, TIGRFAMs, PIRSF, SUPERFAMILY, Gene3D and PANTHER. The latter two new member databases have been integrated since the last publication in this journal. There have been several new developments in InterPro, including an additional reading field, new database links, extensions to the web interface and additional match XML files. InterPro has always provided matches to UniProtKB proteins on the website and in the match XML file on the FTP site. Additional matches to proteins in UniParc (UniProt archive) are now available for download in the new match XML files only. The latest InterPro release (13.0) contains more than 13 000 entries, covering over 78% of all proteins in UniProtKB. The database is available for text- and sequence-based searches via a webserver (http://www.ebi.ac.uk/interpro), and for download by anonymous FTP (ftp://ftp.ebi.ac.uk/pub/databases/interpro). The InterProScan search tool is now also available via a web service at http://www.ebi.ac.uk/Tools/webservices/WSInterProScan.html.

Background: A large volume of data and information about genes and gene products has been stored in various molecular biology databases. A major challenge for knowledge discovery using these databases is to identify related genes and gene products in disparate databases. The development of Gene Ontology (GO) as a common vocabulary for annotation allows integrated queries across multiple databases and identification of semantically related genes and gene products (i.e., genes and gene products that have similar GO annotations). Meanwhile, dozens of tools have been developed for browsing, mining or editing GO terms, their hierarchical relationships, or their "associated" genes and gene products (i.e., genes and gene products annotated with GO terms). Tools that allow users to directly search and inspect relations among all GO terms and their associated genes and gene products from multiple databases are needed., Results: We present a standalone package called DynGO, which provides several advanced functionalities in addition to the standard browsing capability of the official GO browsing tool (AmiGO). DynGO allows users to conduct batch retrieval of GO annotations for a list of genes and gene products, and semantic retrieval of genes and gene products sharing similar GO annotations. The result are shown in an association tree organized according to GO hierarchies and supported with many dynamic display options such as sorting tree nodes or changing orientation of the tree. For GO curators and frequent GO users, DynGO provides fast and convenient access to GO annotation data. DynGO is generally applicable to any data set where the records are annotated with GO terms, as illustrated by two examples., Conclusion: We have presented a standalone package DynGO that provides functionalities to search and browse GO and its association databases as well as several additional functions such as batch retrieval and semantic retrieval. The complete documentation and software are freely available for download from the website http://biocreative.ifsm.umbc.edu/dyngo.

InterPro, an integrated documentation resource of protein families, domains and functional sites, was created to integrate the major protein signature databases. Currently, it includes PROSITE, Pfam, PRINTS, ProDom, SMART, TIGRFAMs, PIRSF and SUPERFAMILY. Signatures are manually integrated into InterPro entries that are curated to provide biological and functional information. Annotation is provided in an abstract, Gene Ontology mapping and links to specialized databases. New features of InterPro include extended protein match views, taxonomic range information and protein 3D structure data. One of the new match views is the InterPro Domain Architecture view, which shows the domain composition of protein matches. Two new entry types were introduced to better describe InterPro entries: these are active site and binding site. PIRSF and the structure-based SUPERFAMILY are the latest member databases to join InterPro, and CATH and PANTHER are soon to be integrated. InterPro release 8.0 contains 11 007 entries, representing 2573 domains, 8166 families, 201 repeats, 26 active sites, 21 binding sites and 20 post-translational modification sites. InterPro covers over 78% of all proteins in the Swiss-Prot and TrEMBL components of UniProt. The database is available for text- and sequence-based searches via a webserver (http://www.ebi.ac.uk/interpro), and for download by anonymous FTP (ftp://ftp.ebi.ac.uk/pub/databases/interpro).

With the accelerated accumulation of genomic sequence data, there is a pressing need to develop computational methods and advanced bioinformatics infrastructure for reliable and large-scale protein annotation and biological knowledge discovery. The Protein Information Resource (PIR) provides an integrated public resource of protein informatics to support genomic and proteomic research. PIR produces the Protein Sequence Database of functionally annotated protein sequences. The annotation problems are addressed by a classification-driven and rule-based method with evidence attribution, coupled with an integrated knowledge base system being developed. The approach allows sensitive identification, consistent and rich annotation, and systematic detection of annotation errors, as well as distinction of experimentally verified and computationally predicted features. The knowledge base consists of two new databases, sequence analysis tools, and graphical interfaces. PIR-NREF, a non-redundant reference database, provides a timely and comprehensive collection of all protein sequences, totaling more than 1,000,000 entries. iProClass, an integrated database of protein family, function, and structure information, provides extensive value-added features for about 830,000 proteins with rich links to over 50 molecular databases. This paper describes our approach to protein functional annotation with case studies and examines common identification errors. It also illustrates that data integration in PIR supports exploration of protein relationships and may reveal protein functional associations beyond sequence homology.

The Protein Information Resource (PIR) is an integrated public resource of protein informatics that supports genomic and proteomic research and scientific discovery. PIR maintains the Protein Sequence Database (PSD), an annotated protein database containing over 283 000 sequences covering the entire taxonomic range. Family classification is used for sensitive identification, consistent annotation, and detection of annotation errors. The superfamily curation defines signature domain architecture and categorizes memberships to improve automated classification. To increase the amount of experimental annotation, the PIR has developed a bibliography system for literature searching, mapping, and user submission, and has conducted retrospective attribution of citations for experimental features. PIR also maintains NREF, a non-redundant reference database, and iProClass, an integrated database of protein family, function, and structure information. PIR-NREF provides a timely and comprehensive collection of protein sequences, currently consisting of more than 1 000 000 entries from PIR-PSD, SWISS-PROT, TrEMBL, RefSeq, GenPept, and PDB. The PIR web site (http://pir.georgetown.edu) connects data analysis tools to underlying databases for information retrieval and knowledge discovery, with functionalities for interactive queries, combinations of sequence and text searches, and sorting and visual exploration of search results. The FTP site provides free download for PSD and NREF biweekly releases and auxiliary databases and files.

The iProClass database provides comprehensive, value-added descriptions of proteins and serves as a framework for data integration in a distributed networking environment. The protein information in iProClass includes family relationships as well as structural and functional classifications and features. The current version consists of about 830 000 non-redundant PIR-PSD, SWISS-PROT, and TrEMBL proteins organized with more than 36 000 PIR superfamilies, 145 000 families, 4000 domains, 1300 motifs and 550 000 FASTA similarity clusters. It provides rich links to over 50 database of protein sequences, families, functions and pathways, protein-protein interactions, post-translational modifications, protein expressions, structures and structural classifications, genes and genomes, ontologies, literature and taxonomy. Protein and superfamily summary reports present extensive annotation information and include membership statistics and graphical display of domains and motifs. iProClass employs an open and modular architecture for interoperability and scalability. It is implemented in the Oracle object-relational database system and is updated biweekly. The database is freely accessible from the web site at http://pir.georgetown.edu/iproclass/ and searchable by sequence or text string. The data integration in iProClass supports exploration of protein relationships. Such knowledge is fundamental to the understanding of protein evolution, structure and function and crucial to functional genomic and proteomic research.

We review recent results in literature data mining for biology and discuss the need and the steps for a challenge evaluation for this field. Literature data mining has progressed from simple recognition of terms to extraction of interaction relationships from complex sentences, and has broadened from recognition of protein interactions to a range of problems such as improving homology search, identifying cellular location, and so on. To encourage participation and accelerate progress in this expanding field, we propose creating challenge evaluations, and we describe two specific applications in this context.

The Protein Information Resource (PIR) serves as an integrated public resource of functional annotation of protein data to support genomic/proteomic research and scientific discovery. The PIR, in collaboration with the Munich Information Center for Protein Sequences (MIPS) and the Japan International Protein Information Database (JIPID), produces the PIR-International Protein Sequence Database (PSD), the major annotated protein sequence database in the public domain, containing about 250 000 proteins. To improve protein annotation and the coverage of experimentally validated data, a bibliography submission system is developed for scientists to submit, categorize and retrieve literature information. Comprehensive protein information is available from iProClass, which includes family classification at the superfamily, domain and motif levels, structural and functional features of proteins, as well as cross-references to over 40 biological databases. To provide timely and comprehensive protein data with source attribution, we have introduced a non-redundant reference protein database, PIR-NREF. The database consists of about 800 000 proteins collected from PIR-PSD, SWISS-PROT, TrEMBL, GenPept, RefSeq and PDB, with composite protein names and literature data. To promote database interoperability, we provide XML data distribution and open database schema, and adopt common ontologies. The PIR web site (http://pir.georgetown.edu/) features data mining and sequence analysis tools for information retrieval and functional identification of proteins based on both sequence and annotation information. The PIR databases and other files are also available by FTP (ftp://nbrfa.georgetown.edu/pir_databases).