Other riffs on cooperation are already showing how well a wiki could work

What is a wiki, and how can it be used in resident education?

Training as a radiology resident is a complex task. Residents frequently encounter multiple hospital systems, each with unique workflow patterns and heterogenous information systems. We identified an opportunity to ease some of the resulting anxiety and frustration by centralizing high-quality resources using a wiki. In this manuscript, we describe our choice of wiki software, give basic information about hardware requirements, detail steps for configuration, outline information included on the wiki, and present the results of a resident acceptance survey.

Visual presentation as a welcome alternative to textual presentation of gene annotation information

The functions of a gene are traditionally annotated textually using either free text (Gene Reference Into Function or GeneRIF) or controlled vocabularies (e.g., Gene Ontology or Disease Ontology). Inspired by the latest word cloud tools developed by the Information Visualization Group at IBM Research, we have prototyped a visual system for capturing gene annotations, which we named Gene Graph Into Function or GeneGIF. Fully developing the GeneGIF system would be a significant effort. To justify the necessity and to specify the design requirements of GeneGIF, we first surveyed the end-user preferences. From 53 responses, we found that a majority (64%, p < 0.05) of the users were either positive or neutral toward using GeneGIF in their daily work (acceptance); in terms of preference, a slight majority (51%, p > 0.05) of the users favored visual presentation of information (GeneGIF) compared to textual (GeneRIF) information. The results of this study indicate that a visual presentation tool, such as GeneGIF, can complement standard textual presentation of gene annotations. Moreover, the survey participants provided many constructive comments that will specify the development of a phase-two project (http://128.248.174.241/) to visually annotate each gene in the human genome.

Annotating the human genome with Disease Ontology

Background

The human genome has been extensively annotated with Gene Ontology for biological functions, but minimally computationally annotated for diseases.

Results

We used the Unified Medical Language System (UMLS) MetaMap Transfer tool (MMTx) to discover gene-disease relationships from the GeneRIF database. We utilized a comprehensive subset of UMLS, which is disease-focused and structured as a directed acyclic graph (the Disease Ontology), to filter and interpret results from MMTx. The results were validated against the Homayouni gene collection using recall and precision measurements. We compared our results with the widely used Online Mendelian Inheritance in Man (OMIM) annotations.

Conclusion

The validation data set suggests a 91% recall rate and 97% precision rate of disease annotation using GeneRIF, in contrast with a 22% recall and 98% precision using OMIM. Our thesaurus-based approach allows for comparisons to be made between disease containing databases and allows for increased accuracy in disease identification through synonym matching. The much higher recall rate of our approach demonstrates that annotating human genome with Disease Ontology and GeneRIF for diseases dramatically increases the coverage of the disease annotation of human genome.

Seeking health information online: does Wikipedia matter?

OBJECTIVE To determine the significance of the English Wikipedia as a source of online health information. DESIGN The authors measured Wikipedia's ranking on general Internet search engines by entering keywords from MedlinePlus, NHS Direct Online, and the National Organization of Rare Diseases as queries into search engine optimization software. We assessed whether article quality influenced this ranking. The authors tested whether traffic to Wikipedia coincided with epidemiological trends and news of emerging health concerns, and how it compares to MedlinePlus. MEASUREMENTS Cumulative incidence and average position of Wikipedia compared to other Web sites among the first 20 results on general Internet search engines (Google, Google UK, Yahoo, and MSN, and page view statistics for selected Wikipedia articles and MedlinePlus pages. RESULTS Wikipedia ranked among the first ten results in 71-85% of search engines and keywords tested. Wikipedia surpassed MedlinePlus and NHS Direct Online (except for queries from the latter on Google UK), and ranked higher with quality articles. Wikipedia ranked highest for rare diseases, although its incidence in several categories decreased. Page views increased parallel to the occurrence of 20 seasonal disorders and news of three emerging health concerns. Wikipedia articles were viewed more often than MedlinePlus Topic (p = 0.001) but for MedlinePlus Encyclopedia pages, the trend was not significant (p = 0.07-0.10). CONCLUSIONS Based on its search engine ranking and page view statistics, the English Wikipedia is a prominent source of online health information compared to the other online health information providers studied.

dictyBase--a Dictyostelium bioinformatics resource update

dictyBase (http://dictybase.org) is the model organism database for Dictyostelium discoideum. It houses the complete genome sequence, ESTs and the entire body of literature relevant to Dictyostelium. This information is curated to provide accurate gene models and functional annotations, with the goal of fully annotating the genome. This dictyBase update describes the annotations and features implemented since 2006, including improved strain and phenotype representation, integration of predicted transcriptional regulatory elements, protein domain information, biochemical pathways, improved searching and a wiki tool that allows members of the research community to provide annotations.

PiQSi: protein quaternary structure investigation

PiQSi facilitates the manual investigation of the quaternary structure of protein complexes in the Protein Data Bank (PDB). Users can browse and obtain an overview of the quaternary structure information of a given protein together with its evolutionary relatives, which helps in the determination of the biological quaternary state. I have used this framework to annotate over 10,000 structures from the PDB Biological Unit and corrected the quaternary state of approximately 15% of them. A benchmark shows that the annotations are of high quality and stresses the need for manual curation, in particular for ambiguous cases such as proteins in equilibrium between two quaternary states. The approximately 10,000 annotations already in the database can be used to improve the accuracy of analyses on protein structure or to benchmark methods that predict protein quaternary structure. In addition, PiQSi incorporates a community-based curation system, which I hope will allow us to reach an accurate and complete description of the biological quaternary state of proteins in PDB. PiQSi is accessible at http://www.PiQSi.org/.