BACKGROUND: In the current era of scientific research, efficient communication of information is paramount. As such, the nature of scholarly and scientific communication is changing; cyberinfrastructure is now absolutely necessary and new media are allowing information and knowledge to be more interactive and immediate. One approach to making knowledge more accessible is the addition of machine-readable semantic data to scholarly articles. RESULTS: The Word add-in presented here will assist authors in this effort by automatically recognizing and highlighting words or phrases that are likely information-rich, allowing authors to associate semantic data with those words or phrases, and to embed that data in the document as XML. The add-in and source code are publicly available at http://www.codeplex.com/UCSDBioLit. CONCLUSIONS: The Word add-in for ontology term recognition makes it possible for an author to add semantic data to a document as it is being written and it encodes these data using XML tags that are effectively a standard in life sciences literature. Allowing authors to mark-up their own work will help increase the amount and quality of machine-readable literature metadata.

BACKGROUND: Biological data have traditionally been stored and made publicly available through a variety of on-line databases, whereas biological knowledge has traditionally been found in the printed literature. With journals now on-line and providing an increasing amount of open access content, often free of copyright restriction, this distinction between database and literature is blurring. To exploit this opportunity we present the integration of open access literature with the RCSB Protein Data Bank (PDB). RESULTS: BioLit provides an enhanced view of articles with markup of semantic data and links to biological databases, based on the content of the article. For example, words matching to existing biological ontologies are highlighted and database identifiers are linked to their database of origin. Among other functions, it identifies PDB IDs that are mentioned in the open access literature, by parsing the full text for all research articles in PubMed Central (PMC) and exposing the results as simple XML Web Services. Here, we integrate BioLit results with the RCSB PDB website by using these services to find PDB IDs that are mentioned in research articles and subsequently retrieving abstract, figures, and text excerpts for those articles. A new RCSB PDB literature view permits browsing through the figures and abstracts of the articles that mention a given structure. The BioLit Web Services that are providing the underlying data are publicly accessible. A client library is provided that supports querying these services (Java). CONCLUSIONS: The integration between literature and websites, as demonstrated here with the RCSB PDB, provides a broader view for how a given structure has been analyzed and used. This approach detects the mention of a PDB structure even if it is not formally cited in the paper. Other structures related through the same literature references can also be identified, possibly providing new scientific insight. To our knowledge this is the first time that database and literature have been integrated in this way and it speaks to the opportunities afforded by open and free access to both database and literature content.

We suspect many of our readers will be familiar with the cult TV show The Prisoner, in which actor Patrick McGoohan had his identity taken away by unknown assailants for unknown reasons, and his pleas of “I am not a number, I am a person” (http://www.youtube.com/watch?v=29JewlGsYxs&feature=related) were greeted with variants of “whatever you say, number six.” We would suggest that, as scientists, we are in a situation where the opposite will soon be true, at least for the purposes of scientific scholarship. Scientists will want to be assigned a number, or at least a unique identifier. Why? Imagine a time when you and your complete scholarly output—papers, grant applications, blog posts, etc.—could be identified online and in perpetuity and returned in a variety of easy-to-digest ways. While ego comes into it as a driver to make this happen, measuring scientific career advancement is something that lacks good metrics in a digital world. Unless one has a truly unique name, applying such a metric is not possible now. Even with a unique name, what is the guarantee that all of our scholarly output will be captured by one source of that information? In the end, we as individuals are the only ones who reliably track our scholarly output. This situation is beginning to change, and, as we shall see, new metrics have the promise of much more than simply returning references to our collective life's work as currently described by research papers, research proceedings, books, and book chapters. Although even a complete and current resume generated on demand would be a big step, if it could be returned in a variety of formats for a variety of purposes. These complete resumes are something many of us spend endless hours generating. The idea of having our scholarly output properly characterized is not out of reach, since the articles we write are already identified uniquely by a Digital Object Identifier (DOI; discussed further below). A book or journal is identified by an ISBN, and citations are identified by PubMed identifiers, and so on. The ideas discussed here simply take this identification process for individual publications and citations to the point of providing unique descriptors for each author and to uniquely identify all of each author's scholarly work.

This Journal and the Public Library of Science (PLoS) at large are standard bearers of the full potential offered through open access publication, but what of you, the reader?For most of you, open access may imply free access to read the journals, but nothing more.There is a far greater potential, but, up to now, little to point to that highlights its tangible benefits.We would argue that, as yet, the full promise of open access has not been realized.There are few persistent applications that collectively use the full on-line corpus, which for the biosciences at least is maintained in PubMed Central (http://www.pubmedcen-tral.nih.gov/).In short, there are no ''killer apps.''Since this readership, beyond any other, would seem to have the ability to change this situation at least in the biosciences, we are issuing a call to action.While, first and foremost, open access implies downloading and reading full papers for free, additional possibilities exist depending on how the open access material is licensed.

Innovation in computational biology research is predicated on the availability of published methods and computational resources. These resources facilitate the generation of new hypotheses and observations both on the part of the creators and the scientists who use them. These methods and resources include Web servers, databases, and software, both complex and simple, that implement a specific procedure or algorithm. Usually, a resource is maintained by the laboratory in which it was initially developed. We would assert that there is a growing level of frustration among scientists who attempt to use many of these resources and find that they no longer exist or are not properly maintained.

BACKGROUND: Protein kinases and protein phosphatases are the fundamental components of phosphorylation dependent protein regulatory systems. We have created a database for the protein kinase-like and phosphatase-like loci of mouse http://phosphoreg.imb.uq.edu.au that integrates protein sequence, interaction, classification and pathway information with the results of a systematic screen of their sub-cellular localization and tissue specific expression data mined from the GNF tissue atlas of mouse. RESULTS: The database lets users query where a specific kinase or phosphatase is expressed at both the tissue and sub-cellular levels. Similarly the interface allows the user to query by tissue, pathway or sub-cellular localization, to reveal which components are co-expressed or co-localized. A review of their expression reveals 30% of these components are detected in all tissues tested while 70% show some level of tissue restriction. Hierarchical clustering of the expression data reveals that expression of these genes can be used to separate the samples into tissues of related lineage, including 3 larger clusters of nervous tissue, developing embryo and cells of the immune system. By overlaying the expression, sub-cellular localization and classification data we examine correlations between class, specificity and tissue restriction and show that tyrosine kinases are more generally expressed in fewer tissues than serine/threonine kinases. CONCLUSION: Together these data demonstrate that cell type specific systems exist to regulate protein phosphorylation and that for accurate modelling and for determination of enzyme substrate relationships the co-location of components needs to be considered.

BACKGROUND: Determination of the subcellular location of a protein is essential to understanding its biochemical function. This information can provide insight into the function of hypothetical or novel proteins. These data are difficult to obtain experimentally but have become especially important since many whole genome sequencing projects have been finished and many resulting protein sequences are still lacking detailed functional information. In order to address this paucity of data, many computational prediction methods have been developed. However, these methods have varying levels of accuracy and perform differently based on the sequences that are presented to the underlying algorithm. It is therefore useful to compare these methods and monitor their performance. RESULTS: In order to perform a comprehensive survey of prediction methods, we selected only methods that accepted large batches of protein sequences, were publicly available, and were able to predict localization to at least nine of the major subcellular locations (nucleus, cytosol, mitochondrion, extracellular region, plasma membrane, Golgi apparatus, endoplasmic reticulum (ER), peroxisome, and lysosome). The selected methods were CELLO, MultiLoc, Proteome Analyst, pTarget and WoLF PSORT. These methods were evaluated using 3763 mouse proteins from SwissProt that represent the source of the training sets used in development of the individual methods. In addition, an independent evaluation set of 2145 mouse proteins from LOCATE with a bias towards the subcellular localization underrepresented in SwissProt was used. The sensitivity and specificity were calculated for each method and compared to a theoretical value based on what might be observed by random chance. CONCLUSION: No individual method had a sufficient level of sensitivity across both evaluation sets that would enable reliable application to hypothetical proteins. All methods showed lower performance on the LOCATE dataset and variable performance on individual subcellular localizations was observed. Proteins localized to the secretory pathway were the most difficult to predict, while nuclear and extracellular proteins were predicted with the highest sensitivity.

SUMMARY: 2HAPI (version 2 of High density Array Pattern Interpreter) is a web-based, publicly-available analytical tool designed to aid researchers in microarray data analysis. 2HAPI includes tools for searching, manipulating, visualizing, and clustering the large sets of data generated by microarray experiments. Other features include association of genes with NCBI information and linkage to external data resources. Unique to 2HAPI is the ability to retrieve upstream sequences of co-regulated genes for promoter analysis using MEME (Multiple Expectation-maximization for Motif Elicitation) AVAILABILITY: 2HAPI is freely available at http://array.sdsc.edu. Users can try 2HAPI anonymously with pre-loaded data or they can register as a 2HAPI user and upload their data.

Abstract Motivation: High-density microarray technology permits the quantitative and simultaneous monitoring of thousands of genes. The interpretation challenge is to extract relevant information from this large amount of data. A growing variety of statistical analysis approaches are available to identify clusters of genes that share common expression characteristics, but provide no information regarding the biological similarities of genes within clusters. The published literature provides a potential source of information to assist in interpretation of clustering results. Results: We describe a data mining method that uses indexing terms (‘keywords’) from the published literature linked to specific genes to present a view of the conceptual similarity of genes within a cluster or group of interest. The method takes advantage of the hierarchical nature of Medical Subject Headings used to index citations in the MEDLINE database, and the registry numbers applied to enzymes. Availability: We have created a publicly accessible website that provides this form of gene expression interpretation at http://www.array.ucsd.edu. Contact: [email protected] 8 To whom correspondence should be addressed at: University of California, San Diego School of Medicine, 9500 Gilman Drive, La Jolla, CA 92093-0602, USA. 7 Present address: Genomics Institute of the Novartis Research Foundation, 3115 Merryfield Row, San Diego, CA 92121, USA.