The cell cycle DB: a systems biology approach to cell cycle analysis

The cell cycle database is a biological resource that collects the most relevant information related to genes and proteins involved in human and yeast cell cycle processes. The database, which is accessible at the web site http://www.itb.cnr.it/cellcycle, has been developed in a systems biology context, since it also stores the cell cycle mathematical models published in the recent years, with the possibility to simulate them directly. The aim of our resource is to give an exhaustive view of the cell cycle process starting from its building-blocks, genes and proteins, toward the pathway they create, represented by the models

Reactome knowledgebase of human biological pathways and processes

Reactome (http://www.reactome.org) is an expert-authored, peer-reviewed knowledgebase of human reactions and pathways that functions as a data mining resource and electronic textbook. Its current release includes 2975 human proteins, 2907 reactions and 4455 literature citations. A new entity-level pathway viewer and improved search and data mining tools facilitate searching and visualizing pathway data and the analysis of user-supplied high-throughput data sets. Reactome has increased its utility to the model organism communities with improved orthology prediction methods allowing pathway inference for 22 species and through collaborations to create manually curated Reactome pathway datasets for species including Arabidopsis, Oryza sativa (rice), Drosophila and Gallus gallus (chicken). Reactome's data content and software can all be freely used and redistributed under open source terms

Reactome: a knowledgebase of biological pathways

Reactome, located at http://www.reactome.org is a curated, peer-reviewed resource of human biological processes. Given the genetic makeup of an organism, the complete set of possible reactions constitutes its reactome. The basic unit of the Reactome database is a reaction; reactions are then grouped into causal chains to form pathways. The Reactome data model allows us to represent many diverse processes in the human system, including the pathways of intermediary metabolism, regulatory pathways, and signal transduction, and high-level processes, such as the cell cycle. Reactome provides a qualitative framework, on which quantitative data can be superimposed. Tools have been developed to facilitate custom data entry and annotation by expert biologists, and to allow visualization and exploration of the finished dataset as an interactive process map. Although our primary curational domain is pathways from Homo sapiens, we regularly create electronic projections of human pathways onto other organisms via putative orthologs, thus making Reactome relevant to model organism research communities. The database is publicly available under open source terms, which allows both its content and its software infrastructure to be freely used and redistributed

Flexible network reconstruction from relational databases with Cytoscape and CytoSQL

<p>Abstract</p> <p>Background</p> <p>Molecular interaction networks can be efficiently studied using network visualization software such as Cytoscape. The relevant nodes, edges and their attributes can be imported in Cytoscape in various file formats, or directly from external databases through specialized third party plugins. However, molecular data are often stored in relational databases with their own specific structure, for which dedicated plugins do not exist. Therefore, a more generic solution is presented.</p> <p>Results</p> <p>A new Cytoscape plugin 'CytoSQL' is developed to connect Cytoscape to any relational database. It allows to launch SQL ('Structured Query Language') queries from within Cytoscape, with the option to inject node or edge features of an existing network as SQL arguments, and to convert the retrieved data to Cytoscape network components. Supported by a set of case studies we demonstrate the flexibility and the power of the CytoSQL plugin in converting specific data subsets into meaningful network representations.</p> <p>Conclusions</p> <p>CytoSQL offers a unified approach to let Cytoscape interact with relational databases. Thanks to the power of the SQL syntax, this tool can rapidly generate and enrich networks according to very complex criteria. The plugin is available at <url>http://www.ptools.ua.ac.be/CytoSQL</url>.</p

Reactome knowledgebase of human biological pathways and processes

Reactome (http://www.reactome.org) is an expert-authored, peer-reviewed knowledgebase of human reactions and pathways that functions as a data mining resource and electronic textbook. Its current release includes 2975 human proteins, 2907 reactions and 4455 literature citations. A new entity-level pathway viewer and improved search and data mining tools facilitate searching and visualizing pathway data and the analysis of user-supplied high-throughput data sets. Reactome has increased its utility to the model organism communities with improved orthology prediction methods allowing pathway inference for 22 species and through collaborations to create manually curated Reactome pathway datasets for species including Arabidopsis, Oryza sativa (rice), Drosophila and Gallus gallus (chicken). Reactome's data content and software can all be freely used and redistributed under open source terms

Formalization of taxon-based constraints to detect inconsistencies in annotation and ontology development

<p>Abstract</p> <p>Background</p> <p>The Gene Ontology project supports categorization of gene products according to their location of action, the molecular functions that they carry out, and the processes that they are involved in. Although the ontologies are intentionally developed to be taxon neutral, and to cover all species, there are inherent taxon specificities in some branches. For example, the process 'lactation' is specific to mammals and the location 'mitochondrion' is specific to eukaryotes. The lack of an explicit formalization of these constraints can lead to errors and inconsistencies in automated and manual annotation.</p> <p>Results</p> <p>We have formalized the taxonomic constraints implicit in some GO classes, and specified these at various levels in the ontology. We have also developed an inference system that can be used to check for violations of these constraints in annotations. Using the constraints in conjunction with the inference system, we have detected and removed errors in annotations and improved the structure of the ontology.</p> <p>Conclusions</p> <p>Detection of inconsistencies in taxon-specificity enables gradual improvement of the ontologies, the annotations, and the formalized constraints. This is progressively improving the quality of our data. The full system is available for download, and new constraints or proposed changes to constraints can be submitted online at <url>https://sourceforge.net/tracker/?atid=605890&group_id=36855</url>.</p

Literature-based discovery of diabetes- and ROS-related targets

Abstract Background Reactive oxygen species (ROS) are known mediators of cellular damage in multiple diseases including diabetic complications. Despite its importance, no comprehensive database is currently available for the genes associated with ROS. Methods We present ROS- and diabetes-related targets (genes/proteins) collected from the biomedical literature through a text mining technology. A web-based literature mining tool, SciMiner, was applied to 1,154 biomedical papers indexed with diabetes and ROS by PubMed to identify relevant targets. Over-represented targets in the ROS-diabetes literature were obtained through comparisons against randomly selected literature. The expression levels of nine genes, selected from the top ranked ROS-diabetes set, were measured in the dorsal root ganglia (DRG) of diabetic and non-diabetic DBA/2J mice in order to evaluate the biological relevance of literature-derived targets in the pathogenesis of diabetic neuropathy. Results SciMiner identified 1,026 ROS- and diabetes-related targets from the 1,154 biomedical papers (http://jdrf.neurology.med.umich.edu/ROSDiabetes/). Fifty-three targets were significantly over-represented in the ROS-diabetes literature compared to randomly selected literature. These over-represented targets included well-known members of the oxidative stress response including catalase, the NADPH oxidase family, and the superoxide dismutase family of proteins. Eight of the nine selected genes exhibited significant differential expression between diabetic and non-diabetic mice. For six genes, the direction of expression change in diabetes paralleled enhanced oxidative stress in the DRG. Conclusions Literature mining compiled ROS-diabetes related targets from the biomedical literature and led us to evaluate the biological relevance of selected targets in the pathogenesis of diabetic neuropathy.http://deepblue.lib.umich.edu/bitstream/2027.42/78315/1/1755-8794-3-49.xmlhttp://deepblue.lib.umich.edu/bitstream/2027.42/78315/2/1755-8794-3-49-S7.XLShttp://deepblue.lib.umich.edu/bitstream/2027.42/78315/3/1755-8794-3-49-S10.XLShttp://deepblue.lib.umich.edu/bitstream/2027.42/78315/4/1755-8794-3-49-S8.XLShttp://deepblue.lib.umich.edu/bitstream/2027.42/78315/5/1755-8794-3-49-S3.XLShttp://deepblue.lib.umich.edu/bitstream/2027.42/78315/6/1755-8794-3-49-S1.XLShttp://deepblue.lib.umich.edu/bitstream/2027.42/78315/7/1755-8794-3-49-S4.XLShttp://deepblue.lib.umich.edu/bitstream/2027.42/78315/8/1755-8794-3-49-S2.XLShttp://deepblue.lib.umich.edu/bitstream/2027.42/78315/9/1755-8794-3-49-S12.XLShttp://deepblue.lib.umich.edu/bitstream/2027.42/78315/10/1755-8794-3-49-S11.XLShttp://deepblue.lib.umich.edu/bitstream/2027.42/78315/11/1755-8794-3-49-S9.XLShttp://deepblue.lib.umich.edu/bitstream/2027.42/78315/12/1755-8794-3-49-S5.XLShttp://deepblue.lib.umich.edu/bitstream/2027.42/78315/13/1755-8794-3-49-S6.XLShttp://deepblue.lib.umich.edu/bitstream/2027.42/78315/14/1755-8794-3-49.pdfPeer Reviewe

RDFScape: Semantic Web meets Systems Biology

<p>Abstract</p> <p>Background</p> <p>The recent availability of high-throughput data in molecular biology has increased the need for a formal representation of this knowledge domain. New ontologies are being developed to formalize knowledge, e.g. about the functions of proteins. As the Semantic Web is being introduced into the Life Sciences, the basis for a distributed knowledge-base that can foster biological data analysis is laid. However, there still is a dichotomy, in tools and methodologies, between the use of ontologies in biological investigation, that is, in relation to experimental observations, and their use as a knowledge-base.</p> <p>Results</p> <p>RDFScape is a plugin that has been developed to extend a software oriented to biological analysis with support for reasoning on ontologies in the semantic web framework. We show with this plugin how the use of ontological knowledge in biological analysis can be extended through the use of inference. In particular, we present two examples relative to ontologies representing biological pathways: we demonstrate how these can be abstracted and visualized as interaction networks, and how reasoning on causal dependencies within elements of pathways can be implemented.</p> <p>Conclusions</p> <p>The use of ontologies for the interpretation of high-throughput biological data can be improved through the use of inference. This allows the use of ontologies not only as annotations, but as a knowledge-base from which new information relevant for specific analysis can be derived.</p

Protein Complex Evolution Does Not Involve Extensive Network Rewiring

The formation of proteins into stable protein complexes plays a fundamental role in the operation of the cell. The study of the degree of evolutionary conservation of protein complexes between species and the evolution of protein-protein interactions has been hampered by lack of comprehensive coverage of the high-throughput (HTP) technologies that measure the interactome. We show that new high-throughput datasets on protein co-purification in yeast have a substantially lower false negative rate than previous datasets when compared to known complexes. These datasets are therefore more suitable to estimate the conservation of protein complex membership than hitherto possible. We perform comparative genomics between curated protein complexes from human and the HTP data in Saccharomyces cerevisiae to study the evolution of co-complex memberships. This analysis revealed that out of the 5,960 protein pairs that are part of the same complex in human, 2,216 are absent because both proteins lack an ortholog in S. cerevisiae, while for 1,828 the co-complex membership is disrupted because one of the two proteins lacks an ortholog. For the remaining 1,916 protein pairs, only 10% were never co-purified in the large-scale experiments. This implies a conservation level of co-complex membership of 90% when the genes coding for the protein pairs that participate in the same protein complex are also conserved. We conclude that the evolutionary dynamics of protein complexes are, by and large, not the result of network rewiring (i.e. acquisition or loss of co-complex memberships), but mainly due to genomic acquisition or loss of genes coding for subunits. We thus reveal evidence for the tight interrelation of genomic and network evolution