The Gene Ontology resource: enriching a GOld mine

The Gene Ontology Consortium (GOC) provides the most comprehensive resource currently available for computable knowledge regarding the functions of genes and gene products. Here, we report the advances of the consortium over the past two years. The new GO-CAM annotation framework was notably improved, and we formalized the model with a computational schema to check and validate the rapidly increasing repository of 2838 GO-CAMs. In addition, we describe the impacts of several collaborations to refine GO and report a 10% increase in the number of GO annotations, a 25% increase in annotated gene products, and over 9,400 new scientific articles annotated. As the project matures, we continue our efforts to review older annotations in light of newer findings, and, to maintain consistency with other ontologies. As a result, 20 000 annotations derived from experimental data were reviewed, corresponding to 2.5% of experimental GO annotations. The website (http://geneontology.org) was redesigned for quick access to documentation, downloads and tools. To maintain an accurate resource and support traceability and reproducibility, we have made available a historical archive covering the past 15 years of GO data with a consistent format and file structure for both the ontology and annotations

Gene Ontology Consortium: going forward

The Gene Ontology (GO; http://www.geneontology.org) is a community-based bioinformatics resource that supplies information about gene product function using ontologies to represent biological knowledge. Here we describe improvements and expansions to several branches of the ontology, as well as updates that have allowed us to more efficiently disseminate the GO and capture feedback from the research community. The Gene Ontology Consortium (GOC) has expanded areas of the ontology such as cilia-related terms, cell-cycle terms and multicellular organism processes. We have also implemented new tools for generating ontology terms based on a set of logical rules making use of templates, and we have made efforts to increase our use of logical definitions. The GOC has a new and improved web site summarizing new developments and documentation, serving as a portal to GO data. Users can perform GO enrichment analysis, and search the GO for terms, annotations to gene products, and associated metadata across multiple species using the all-new AmiGO 2 browser. We encourage and welcome the input of the research community in all biological areas in our continued effort to improve the Gene Ontology

Comparing different NTCP models that predict the incidence of radiation pneumonitis

Purpose: To compare different normal tissue complication probability (NTCP) models to predict the incidence of radiation pneumonitis on the basis of the dose distribution in the lung. Methods and Materials: The data from 382 breast cancer, malignant lymphoma, and inoperable non-small-cell lung cancer patients from two centers were studied. Radiation pneumonitis was scored using the Southwestern Oncology Group criteria. Dose-volume histograms of the lungs were calculated from the dose distributions that were corrected for dose per fraction effects. The dose-volume histogram of each patient was reduced to a single parameter using different local dose-effect relationships. Examples of single parameters were the mean lung dose (MLD) and the volume of lung receiving more than a threshold dose (V-Dth). The parameters for the different NTCP models were fit to patient data using a maximum likelihood analysis. Results: The best fit resulted in a linear local dose-effect relationship, with the MLD as the resulting single parameter. The relationship between the MLD and NTCP could be described with a median toxic dose (TD50) of 30.8 Gy and a steepness parameter m of 0.37. The best fit for the relationship between the V-Dth and the NTCP was obtained with a D-th of 13 Gy. The MLD model was found to be significantly better than the VIth model (p 35%. For arbitrary dose distributions, an estimate of the uncertainty in the NTCP could be determined using the probability distribution of the parameter values of the Lyman-Kutcher-Burman model. Conclusion: The maximum likelihood method revealed that the underlying local dose-effect relation for radiation pneumonitis was linear (the MLD model), rather than a step function (the V-Dth model). Thus, for the studied patient population, the MLD was the most accurate predictor for the incidence of radiation pneumonitis. (C) 2003 Elsevier Science Inc

RIPLET, and not TRIM25, is required for endogenous RIG-I-dependent antiviral responses

© 2019 Australian and New Zealand Society for Immunology Inc. The innate immune system is our first line of defense against viral pathogens. Host cell pattern recognition receptors sense viral components and initiate immune signaling cascades that result in the production of an array of cytokines to combat infection. Retinoic acid–inducible gene-I (RIG-I) is a pattern recognition receptor that recognizes viral RNA and, when activated, results in the production of type I and III interferons (IFNs) and the upregulation of IFN-stimulated genes. Ubiquitination of RIG-I by the E3 ligases tripartite motif-containing 25 (TRIM25) and Riplet is thought to be requisite for RIG-I activation; however, recent studies have questioned the relative importance of these two enzymes for RIG-I signaling. In this study, we show that deletion of Trim25 does not affect the IFN response to either influenza A virus (IAV), influenza B virus, Sendai virus or several RIG-I agonists. This is in contrast to deletion of either Rig-i or Riplet, which completely abrogated RIG-I-dependent IFN responses. This was consistent in both mouse and human cell lines, as well as in normal human bronchial cells. With most of the current TRIM25 literature based on exogenous expression, these findings provide critical evidence that Riplet, and not TRIM25, is required endogenously for the ubiquitination of RIG-I. Despite this, loss of TRIM25 results in greater susceptibility to IAV infection in vivo, suggesting that it may have an alternative role in host antiviral defense. This study refines our understanding of RIG-I signaling in viral infections and will inform future studies in the field