The Gene Ontology in 2010: extensions and refinements

The Gene Ontology (GO) Consortium (http://www.geneontology.org) (GOC) continues to develop, maintain and use a set of structured, controlled vocabularies for the annotation of genes, gene products and sequences. The GO ontologies are expanding both in content and in structure. Several new relationship types have been introduced and used, along with existing relationships, to create links between and within the GO domains. These improve the representation of biology, facilitate querying, and allow GO developers to systematically check for and correct inconsistencies within the GO. Gene product annotation using GO continues to increase both in the number of total annotations and in species coverage. GO tools, such as OBO-Edit, an ontology-editing tool, and AmiGO, the GOC ontology browser, have seen major improvements in functionality, speed and ease of use

Weighted protein interaction network analysis of frontotemporal dementia

The genetic analysis of complex disorders has undoubtedly led to the identification of a wealth of associations between genes and specific traits. However, moving from genetics to biochemistry one gene at a time has, to date, rather proved inefficient and under-powered to comprehensively explain the molecular basis of phenotypes. Here we present a novel approach, weighted protein−protein interaction network analysis (W-PPI-NA), to highlight key functional players within relevant biological processes associated with a given trait. This is exemplified in the current study by applying W-PPI-NA to frontotemporal dementia (FTD): We first built the state of the art FTD protein network (FTD-PN) and then analyzed both its topological and functional features. The FTD-PN resulted from the sum of the individual interactomes built around FTD-spectrum genes, leading to a total of 4198 nodes. Twenty nine of 4198 nodes, called inter-interactome hubs (IIHs), represented those interactors able to bridge over 60% of the individual interactomes. Functional annotation analysis not only reiterated and reinforced previous findings from single genes and gene-coexpression analyses but also indicated a number of novel potential disease related mechanisms, including DNA damage response, gene expression regulation, and cell waste disposal and potential biomarkers or therapeutic targets including EP300. These processes and targets likely represent the functional core impacted in FTD, reflecting the underlying genetic architecture contributing to disease. The approach presented in this study can be applied to other complex traits for which risk-causative genes are known as it provides a promising tool for setting the foundations for collating genomics and wet laboratory data in a bidirectional manner. This is and will be critical to accelerate molecular target prioritization and drug discovery

Insights into Online microRNA Bioinformatics Tools

MicroRNAs (miRNAs) are members of the small non-coding RNA family regulating gene expression at the post-transcriptional level. MiRNAs have been found to have critical roles in various biological and pathological processes. Research in this field has significantly progressed, with increased recognition of the importance of miRNA regulation. As a result of the vast data and information available regarding miRNAs, numerous online tools have emerged to address various biological questions related to their function and influence across essential cellular processes. This review includes a brief introduction to available resources for an investigation covering aspects such as miRNA sequences, target prediction/validation, miRNAs associated with disease, pathway analysis and genetic variants within miRNAs

GOing Forward With the Cardiac Conduction System Using Gene Ontology

The cardiac conduction system (CCS) comprises critical components responsible for the initiation and coordination of the action potential. Aberrant CCS development can cause conduction abnormalities, including sick sinus syndrome and atrioventricular and bundle branch blocks. Gene Ontology (GO; http://geneontology.org/) is an invaluable global bioinformatics resource which can provide structured, computable knowledge describing the functions of gene products. Many gene products are known be involved in CCS development; however, this information is not comprehensively captured by GO. Our study aimed to describe the specific roles of essential proteins that have been reported in the literature to be involved with development and/or function of the CCS. 14 proteins were prioritised for GO annotation which led to the curation of 15 peer-reviewed primary experimental articles using carefully selected GO terms. 152 descriptive GO annotations, including those describing sinoatrial node and atrioventricular node development were created and submitted to the GO Consortium database. A functional enrichment analysis of the 35 proteins known to have a role in CCS confirmed that this work has improved the in silico interpretation of this CCS dataset. Our contribution to the GO database may help elucidate the key mechanisms involved in CCS disorders as previous annotation projects have focussed predominantly on development of the heart rather than that of the CCS. This work may improve future heart disease investigations applying high-throughput methods such as genome-wide association studies analysis, proteomics and transcriptomics

Student biocuration projects as a learning environment [version 2; peer review: 2 approved]

Background: Bioinformatics is becoming an essential tool for the majority of biological and biomedical researchers. Although bioinformatics data is exploited by academic and industrial researchers, limited focus is on teaching this area to undergraduates, postgraduates and senior scientists. Many scientists are developing their own expertise without formal training and often without appreciating the source of the data they are reliant upon. Some universities do provide courses on a variety of bioinformatics resources and tools, a few also provide biocuration projects, during which students submit data to annotation resources. Methods: To assess the usefulness and enjoyability of annotation projects a survey was sent to University College London (UCL) students who have undertaken Gene Ontology biocuration projects. Results: Analysis of survey responses suggest that these projects provide students with an opportunity not only to learn about bioinformatics resources but also to improve their literature analysis, presentation and writing skills. Conclusion: Biocuration student projects provide valuable annotations as well as enabling students to develop a variety of skills relevant to their future careers. It is also hoped that, as future scientists, these students will critically assess their own manuscripts and ensure that these are written with the biocurators of the future in min

Guidelines for the functional annotation of microRNAs using the Gene Ontology.

MicroRNA regulation of developmental and cellular processes is a relatively new field of study, and the available research data have not been organized to enable its inclusion in pathway and network analysis tools. The association of gene products with terms from the Gene Ontology is an effective method to analyze functional data, but until recently there has been no substantial effort dedicated to applying Gene Ontology terms to microRNAs. Consequently, when performing functional analysis of microRNA data sets, researchers have had to rely instead on the functional annotations associated with the genes encoding microRNA targets. In consultation with experts in the field of microRNA research, we have created comprehensive recommendations for the Gene Ontology curation of microRNAs. This curation manual will enable provision of a high-quality, reliable set of functional annotations for the advancement of microRNA research. Here we describe the key aspects of the work, including development of the Gene Ontology to represent this data, standards for describing the data, and guidelines to support curators making these annotations. The full microRNA curation guidelines are available on the GO Consortium wiki (http://wiki.geneontology.org/index.php/MicroRNA_GO_annotation_manual).R.P.H. and R.C.L are supported by funding from a British Heart Foundation grant (RG/13/5/30112) and the National Institute for Health Research University College London Hospitals Biomedical Research Centre. M.M. is a Senior Research Fellow of the British Heart Foundation (FS/13/2/29892). A.Z. is an Intermediate Fellow of the British Heart Foundation (FS/13/18/30207). D.S. is supported by a grant awarded to the Mouse Genome Database from the National Human Genome Research Institue at the US National Institutes of Health (HG-00330). P.D’E., M.G., M.O-M. are supported by grants from the US National Institutes of Health (P41 HG003751 and U54 GM114833), Ontario Research Fund, and the European Molecular Biology Laboratory. D.H. is supported by a grant awarded to the Zebrafish Information Network fromthe National Human Genome Research Institute at the US National Institutes of Health (HG002659). A.Z.K. is funded by a NIHR University College London Hospitals Biomedical Research Centre, Research Capability Funding award (RCF) (RCF123). L.M. is a Ragnar Söderberg fellow in Medicine (M-14/55), and received funding from Swedish Heart-Lung-Foundation (20120615, 20130664, 20140186). Huntley, RP 22 R.B. and D.O-S. are supported by R.B. and D.O-S. are supported by a grant awarded to The Gene Ontology Consortium (Principal Investigators: JA Blake, JM Cherry, S Lewis, PW Sternberg and P Thomas) by the National Human Genome Research Institute (NHGRI) (#U41 HG22073). V.P. and J.R.S. are supported by a grant from the National Heart, Lung, and Blood Institute on behalf of the National Institutes of Health (HL64541). K.V.A. is supported by a grant awarded to the Gene Ontology Consortium from the National Human Genome Research Institute at the US National Institutes of Health (HG002273). V.W. is supported by a Wellcome Trust grant (104967/Z/14/Z). We would like to thank Leonore Reiser and Tanya Berardini who provided guidance on the plant miRNA processing pathway. Also thanks to David Hill, Harold Drabkin, Judith Blake, Karen Christie, Donghui Li and Pascale Gaudet who contributed to discussions regarding GO curation procedures and to Lisa Matthews and Bruce May who provided helpful feedback on the manuscript. We are very grateful to Tony Sawford and Maria Martin from the European Bioinformatics Institute for access to the online GO curation tool, which is an essential component of this annotation project. Many thanks to members of the GO Editorial Office for useful discussions about the placement and definition of new GO terms. We also thank Alex Bateman and Anton Petrov for being responsive to our feedback regarding RNAcentral functionality. Author contributions: R.C.L. initiated discussions in the GO Consortium regarding miRNA curation guidelines and supervised the project, R.P.H. researched and constructed the guidelines and wrote the manuscript, R.P.H., R.C.L., D.S., R.B., P.D’E., M.G., M.O-M., D.H., V.P., J.R.S., K.V.A. and V.W. contributed to discussions regarding GO curation procedures and provided feedback on the manuscript. D.O-S. provided the expertise on definitions and placements of miRNA-related GO terms and performed the necessary updates and additions to both the GO and to the annotation extension relations used herein. M.M., A.Z., L.M. and A.Z.K. provided guidance with the scientific aspect of the guidelines and provided feedback on the manuscript.This is the final version of the article. It first appeared from Cold Spring Harbor Press via http://dx.doi.org/10.1261/rna.055301.11

Exploring autophagy with Gene Ontology.

Autophagy is a fundamental cellular process that is well conserved among eukaryotes. It is one of the strategies that cells use to catabolize substances in a controlled way. Autophagy is used for recycling cellular components, responding to cellular stresses and ridding cells of foreign material. Perturbations in autophagy have been implicated in a number of pathological conditions such as neurodegeneration, cardiac disease and cancer. The growing knowledge about autophagic mechanisms needs to be collected in a computable and shareable format to allow its use in data representation and interpretation. The Gene Ontology (GO) is a freely available resource that describes how and where gene products function in biological systems. It consists of 3 interrelated structured vocabularies that outline what gene products do at the biochemical level, where they act in a cell and the overall biological objectives to which their actions contribute. It also consists of \u27annotations\u27 that associate gene products with the terms. Here we describe how we represent autophagy in GO, how we create and define terms relevant to autophagy researchers and how we interrelate those terms to generate a coherent view of the process, therefore allowing an interoperable description of its biological aspects. We also describe how annotation of gene products with GO terms improves data analysis and interpretation, hence bringing a significant benefit to this field of study. Autophagy 2018 Feb 17; 1-18

Nomenclature for the human Arf family of GTP-binding proteins: ARF, ARL, and SAR proteins

The Ras superfamily is comprised of at least four large families of regulatory guanosine triphosphate–binding proteins, including the Arfs. The Arf family includes three different groups of proteins: the Arfs, Arf-like (Arls), and SARs. Several Arf family members have been very highly conserved throughout eukaryotic evolution and have orthologues in evolutionally diverse species. The different means by which Arf family members have been identified have resulted in an inconsistent and confusing array of names. This confusion is further compounded by differences in nomenclature between different species. We propose a more consistent nomenclature for the human members of the Arf family that may also serve as a guide for nomenclature in other species