in some well-characterized organisms like Vibrio cholera and Bacillus anthracis. Several data sets contained information on annotated but not translated pseudogenes. In the case of Vibrio cholera, 21 of 48 pseudogenes cloned and sequenced by the Harvard Institute of Proteomics did not contain the annotated point mutation or frame shift and appeared to produce full-length proteins [16]. In the case of Bacillus anthracis, microarrays containing probes for 82 of 192 annotated pseudogenes also showed significant changes in RNA expression in response to infection or other treatments [17,18,19]. In these cases, new database entries were created in the MPD to house the results. 4) Alternate species or strain representations: several experimental data sets reported sequence identifiers for strains or variants other than the one used in the experimental sample. This is not an uncommon situation as the genetically most characterized variant is often an attenuated laboratory strain while the more virulent strains are either not yet sequenced or the sequence is of lower quality. Often microarray chips or mass spectrometry databases are designed using the best available sequence from the research strain, yet then use RNA and protein samples from another similar virulent strain. The question here was what organism strain to map to and represent on our website: the strain the RNA or protein the sample came from, or alternatively, the strain that matched the identifier and sequence used in the research to detect the RNA or protein. For the MPD we chose to map to the sequence identifier reported in the data files and related publications, with the additional virulent strain information noted in the results summary. Search Design Data Mining Design Goals. In consultation with NIAID and PRCs within the project’s Interoperability Working Group, we developed 3 goals for data mining. 1) All project data and other deliverables should be available via browsing and simple keyword searches. 2) The data and information provided by the resource should be sufficient to allow a skilled researcher to download and reanalyze or mine the data for additional information. 3) Our target user was a biomedical scientist not expert in the technology used to produce the data or in bioinformatics, thus the data, procedures, publications and general results and conclusions of an analysis should be relatively easy to find on the project website for someone not familiar with the details of the particular technologies used to generate it. To allow both simple keyword searches and also Boolean searches of the project data, we did the following: 1) We included in the MPD only ‘‘validated’’ results that were determined by the research centers to be significant using their methods. To do otherwise would confuse users not familiar with the technology and how results are filtered. Results that fell below the significance threshold used by the research center were made available via download of data sets at the FTP site. 2) ‘‘RAW’’ unprocessed machine specific data would be stored by the PRC and available on request. 3) To facilitate and simplify searches across laboratories and data types, we omitted most data type or analysis specific numerical values and statistics from the general MPD search and display. These numerical values are usually platform, laboratory and method dependent and cannot easily be used to compare across datasets so including them might be confusing to users. Instead, we focused on providing simple, yet powerful, queries of experimental summaries where a user can query if a gene/protein was presented in the results and, in some cases, if it showed a reported increase or decrease in expression or accumulation based on the PRC’s criteria. Once a set of proteins of interest is identified, a user can then drill down to view the specific experimental values and methods employed to generate Pathogen-Host Omics Data the particular dataset. Links to details in the publications are provided and full data are available via FTP. Since all protein attributes are included as search options, one can query beyond simply protein names, accessions or project data and search pathways, protein families, Gene Ontology (GO) terms, database cross-references and many other attributes, providing many powerful options to the users. To provide a robust text search for the website, we used the PIR text indexing system [20] in which over 100 text fields and unique identifiers from the MPD database are indexed using Callable Personal Librarian (CPL) [21] which supports fast exact text search, substring & wildcard text search, range search and Boolean searches. Entry indexing and retrieval is supported by Oracle. I: Unstructured keyword search. A simple keyword search was implemented on every page of the resource’s website. This searches all fields in the MPD. To further facilitate searches, the protein name field is supplemented by also searching the BioThesaurus [10] containing all gene and protein name synonyms and textual variants for each protein from over 35 data sources. In addition, the default option searches all text from the PubMed abstract for all project publications, an abstract of each technology and all text in SOPs. The text indexed for publications, technologies and SOPs was annotated with additional standard keywords to facilitate searches. Figure 2 shows the results of a simple keyword search with hits for proteins, reagents, publications, technologies and SOPs. II: Structured text search. The MPD database contains over 100 fields derived from iProClass and Proteomics Research Center’s data. Currently 75 of these fields are available for individual searches and can be combined with Boolean operators as seen in some of the use case examples in this paper. The protein-centric search results are presented in a customizable tabular format where users can add or delete columns. Currently 62 fields can be customized. The tabular display has two modes: 1) a default mode which displays fields common to all the supported data types and 2) a data type specific mode which restricts the results to a particular data type and displays fields specific to that data type. See supplemental table S1 for a list of data type specific fields. Additional filters for Proteomic Research Center and Organism are available as pull down menus to aid browsing and viewing the results of queries. Examples of these functions are illustrated in the scientific use cases below and in help pages and tutorials available on the website. http:// www.proteomicsresource.org/Resources/Tutorials.aspx, http:// proteininformationresource.org/pirwww/support/help.shtml#30. Results Data Base Content – Browsing Pathogen-Host Omics Data The resource currently contains information on 35,112 proteins from 58 datasets, 35,819 reagents, 75 SOPs, 31 technologies and 88 manuscripts. Table 1 shows statistics on proteins in the MPD. Currently ,30% of the proteins are uncharacterized in that they are called either ‘‘Uncharacterized’’ or ‘‘Hypothetical’’ and have no other functional annotations or functional domains. Of these uncharacterized proteins, ,85% have experimental data, such as mass spectrometry, microarray, or protein interactions, associated with them. The remaining 15% of uncharacterized proteins are available as full length clones for further research. Though the program is focused on pathogen proteins, about 32% of the proteins are host proteins from mouse or human, as cell lines or tissue samples from both these organisms were used as infection models for multiple pathogens. PLoS ONE | www.plosone.org 4 September 2009 | Volume 4 | Issue 9 | e7162