Egon Willighagen

Science, Vol. 335, No. 6069. (10 February 2012), pp. 651-653. Jocelyn Kaiser - Egon Willighagen

Nature Biotechnology, Vol. 30, No. 2. (15 January 2012), pp. 159-164. To better understand the molecular mechanisms and genetic basis of human disease, we systematically examine relationships between 3,949 genes, 62,663 mutations and 3,453 associated disorders by generating a three-dimensional, structurally resolved human interactome. This network consists of 4,222 high-quality binary protein-protein interactions with their atomic-resolution interfaces. We find that in-frame mutations (missense point mutations and in-frame insertions and deletions) are enriched on the interaction interfaces of proteins associated with the corresponding disorders, and that the disease specificity for different mutations of the same gene can be explained by their location within an interface. We also predict 292 candidate genes for 694 unknown disease-to-gene associations with proposed molecular mechanism hypotheses. This work indicates that knowledge of how in-frame disease mutations alter specific... - Egon Willighagen

PLoS Comput Biol, Vol. 7, No. 12. (29 December 2011), e1002323. Combinatorial therapy is a promising strategy for combating complex disorders due to improved efficacy and reduced side effects. However, screening new drug combinations exhaustively is impractical considering all possible combinations between drugs. Here, we present a novel computational approach to predict drug combinations by integrating molecular and pharmacological data. Specifically, drugs are represented by a set of their properties, such as their targets or indications. By integrating several of these features, we show that feature patterns enriched in approved drug combinations are not only predictive for new drug combinations but also provide insights into mechanisms underlying combinatorial therapy. Further analysis confirmed that among our top ranked predictions of effective combinations, 69% are supported by literature, while the others represent novel potential drug combinations. We believe that our proposed... - Egon Willighagen

Journal of Cheminformatics, Vol. 4, No. 1. (2012), 3. BACKGROUND:Representations of chemical datasets in spreadsheet format are important for ready data assimilation and manipulation. In addition to the normal spreadsheet facilities, chemical spreadsheets need to have visualisable chemical structures and data searchable by chemical as well as textual queries. Many such chemical spreadsheet tools are available, some operating in the familiar Microsoft Excel environment. However, within this group, the performance of Excel is often compromised, particularly in terms of the number of compounds which can usefully be stored on a sheet.SUMMARY:LICSS is a lightweight chemical spreadsheet within Microsoft Excel for Windows. LICSS stores structures solely as Smiles strings. Chemical operations are carried out by calling Java code modules which use the CDK, JChemPaint and OPSIN libraries to provide cheminformatics functionality. Compounds in sheets or charts may be visualised (individually or... - Egon Willighagen

BMC Bioinformatics, Vol. 13, No. Suppl 1. (2012), S3. BACKGROUND:Semantic Web technologies have been developed to overcome the limitations of the current Web and conventional data integration solutions. The Semantic Web is expected to link all the data present on the Internet instead of linking just documents. One of the foundations of the Semantic Web technologies is the knowledge representation language Resource Description Framework (RDF). Knowledge expressed in RDF is typically stored in so-called triple stores (also known as RDF stores), from which it can be retrieved with SPARQL, a language designed for querying RDF-based models. The Semantic Web technologies should allow federated queries over multiple triple stores. In this paper we compare the efficiency of a set of biologically relevant queries as applied to a number of different triple store implementations.RESULTS:Previously we developed a library of queries to guide the use of our knowledge base Cell Cycle Ontology... - Egon Willighagen

Journal of Computational Biology, Vol. 19, No. 2. (February 2012), pp. 163-174. Yael Silberberg, Assaf Gottlieb, Martin Kupiec, Eytan Ruppin, Roded Sharan - Egon Willighagen

how many submissions have there been so far? - Rajarshi Guha

Plant Molecular Biology, Vol. 48, No. 1. (1 January 2002), pp. 155-171. Metabolites are the end products of cellular regulatory processes, and their levels can be regarded as the ultimate response of biological systems to genetic or environmental changes. In parallel to the terms `transcriptome' and `proteome', the set of metabolites synthesized by a biological system constitute its `metabolome'. Yet, unlike other functional genomics approaches, the unbiased simultaneous identification and quantification of plant metabolomes has been largely neglected. Until recently, most analyses were restricted to profiling selected classes of compounds, or to fingerprinting metabolic changes without sufficient analytical resolution to determine metabolite levels and identities individually. As a prerequisite for metabolomic analysis, careful consideration of the methods employed for tissue extraction, sample preparation, data acquisition, and data mining must be taken. In this review, the... - Egon Willighagen

Nice overview of BMC's activities in Open Data. - Noel O'Boyle

Indeed !! - Graham Steel

if you're gonna bookmark - you need to get rid of the session information... something like isiknowledge.com or if you want the proxy http://ezproxy.ub.unimaas.nl/somethi... then http://isiknowledge.com - Christina Pikas

BMC Systems Biology, Vol. 6, No. 1. (2012), 8. BACKGROUND:The creation and modification of genome-scale metabolic models is a task that requires specialized software tools. While these are available, subsequently running or visualizing a model often relies on disjoint code, which adds additional actions to the analysis routine and, in our experience, renders these applications suboptimal for routine use by (systems) biologists.RESULTS:The Flux Analysis and Modeling Environment (FAME) is the first web-based modeling tool that combines the tasks of creating, editing, running, and analyzing/visualizing stoichiometric models into a single program. Analysis results can be automatically superimposed on familiar KEGG-like maps. FAME is written in PHP and uses the Python-based PySCeS-CBM for its linear solving capabilities. It comes with a comprehensive manual and a quick-start tutorial, and can be accessed online at http://f-a-m-e.org/ .CONCLUSIONS:With FAME, we present the community with an... - Egon Willighagen

Nature, Vol. 465, No. 7299. (10 June 2010), pp. 685-685. Chris Evelo, Andra Waagmeester - Egon Willighagen

Nature Genetics, Vol. 44, No. 2. (27 January 2012), pp. 127-130. Jonathan Derry, Lara Mangravite, Christine Suver, Matthew Furia, David Henderson, Xavier Schildwachter, Brian Bot, Jonathan Izant, Solveig Sieberts, Michael Kellen, Stephen Friend - Egon Willighagen

Douglas, I would recommend Jmol (Open Source). Bob implemented the DSSP algorithm, and Jmol has a such a large user base, that any irregularities have been detected by now. This is the scripting command listed there to calculate hydrogen bonding patterns with DSSP: calculate hBonds structure The scripting documentation provides more detail. - Egon Willighagen

Nucleic Acids Research, Vol. 40, No. D1. (1 January 2012), pp. D9-D12. Wikipedia, the online encyclopedia, is the most famous wiki in use today. It contains over 3.7 million pages of content; with many pages written on scientific subject matters that include peer-reviewed citations, yet are written in an accessible manner and generally reflect the consensus opinion of the community. In this, the 19th Annual Database Issue of Nucleic Acids Research, there are 11 articles that describe the use of a wiki in relation to a biological database. In this commentary, we discuss how biological databases can be integrated with Wikipedia, thereby utilising the pre-existing infrastructure, tools and above all, large community of authors (or Wikipedians). The limitations to the content that can be included in Wikipedia are highlighted, with examples drawn from articles found in this issue and other wiki-based resources, indicating why other wiki solutions are necessary. We discuss the merits of... - Egon Willighagen

Nature Genetics, Vol. 44, No. 2. (27 January 2012), pp. 121-126. Susanna-Assunta Sansone, Philippe Rocca-Serra, Dawn Field, Eamonn Maguire, Chris Taylor, Oliver Hofmann, Hong Fang, Steffen Neumann, Weida Tong, Linda Amaral-Zettler, Kimberly Begley, Tim Booth, Lydie Bougueleret, Gully Burns, Brad Chapman, Tim Clark, Lee-Ann Coleman, Jay Copeland, Sudeshna Das, Antoine de Daruvar, Paula de Matos, Ian Dix, Scott Edmunds, Chris Evelo, Mark Forster, Pascale Gaudet, Jack Gilbert, Carole Goble, Julian Griffin, Daniel Jacob, Jos Kleinjans, Lee Harland, Kenneth Haug, Henning Hermjakob, Shannan Sui, Alain Laederach, Shaoguang Liang, Stephen Marshall, Annette McGrath, Emily Merrill, Dorothy Reilly, Magali Roux, Caroline Shamu, Catherine Shang, Christoph Steinbeck, Anne Trefethen, Bryn Williams-Jones, Katherine Wolstencroft, Ioannis Xenarios, Winston Hide - Egon Willighagen

Yeah, I originally assumed generation of 2D coordinates was meant, but lost confidence in that... clarification most welcome. - Egon Willighagen

The Chemistry Development Kit can do this with the StructureDiagramGenerator. A full code example can be found in this blog post. The basic use looks like: StructureDiagramGenerator sdg = new StructureDiagramGenerator(); sdg.setMolecule(someMolecule); sdg.generateCoordinates(); Molecule layedOutMol = sdg.getMolecule(); - Egon Willighagen

That looks like a very interesting resource? Who's behind the wiki? Email address? The wiki is devoid of information of copyright/licensing info... - Egon Willighagen