Publications by authors named "Rachael P Huntley"

25 Publications

  • Page 1 of 1

Gene Ontology Curation of Neuroinflammation Biology Improves the Interpretation of Alzheimer's Disease Gene Expression Data.

J Alzheimers Dis 2020 ;75(4):1417-1435

Functional Gene Annotation, Preclinical and Fundamental Science, UCL Institute of Cardiovascular Science, University College London, London, UK.

Background: Gene Ontology (GO) is a major bioinformatic resource used for analysis of large biomedical datasets, for example from genome-wide association studies, applied universally across biological fields, including Alzheimer's disease (AD) research.

Objective: We aim to demonstrate the applicability of GO for interpretation of AD datasets to improve the understanding of the underlying molecular disease mechanisms, including the involvement of inflammatory pathways and dysregulated microRNAs (miRs).

Methods: We have undertaken a systematic full article GO annotation approach focused on microglial proteins implicated in AD and the miRs regulating their expression. PANTHER was used for enrichment analysis of previously published AD data. Cytoscape was used for visualizing and analyzing miR-target interactions captured from published experimental evidence.

Results: We contributed 3,084 new annotations for 494 entities, i.e., on average six new annotations per entity. This included a total of 1,352 annotations for 40 prioritized microglial proteins implicated in AD and 66 miRs regulating their expression, yielding an average of twelve annotations per prioritized entity. The updated GO resource was then used to re-analyze previously published data. The re-analysis showed novel processes associated with AD-related genes, not identified in the original study, such as 'gliogenesis', 'regulation of neuron projection development', or 'response to cytokine', demonstrating enhanced applicability of GO for neuroscience research.

Conclusions: This study highlights ongoing development of the neurobiological aspects of GO and demonstrates the value of biocuration activities in the area, thus helping to delineate the molecular bases of AD to aid the development of diagnostic tools and treatments.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3233/JAD-200207DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7369085PMC
January 2020

Annotation of gene product function from high-throughput studies using the Gene Ontology.

Database (Oxford) 2019 01 1;2019. Epub 2019 Jan 1.

Zebrafish Information Network, University of Oregon, Eugene, OR, USA.

High-throughput studies constitute an essential and valued source of information for researchers. However, high-throughput experimental workflows are often complex, with multiple data sets that may contain large numbers of false positives. The representation of high-throughput data in the Gene Ontology (GO) therefore presents a challenging annotation problem, when the overarching goal of GO curation is to provide the most precise view of a gene's role in biology. To address this, representatives from annotation teams within the GO Consortium reviewed high-throughput data annotation practices. We present an annotation framework for high-throughput studies that will facilitate good standards in GO curation and, through the use of new high-throughput evidence codes, increase the visibility of these annotations to the research community.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/database/baz007DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6355445PMC
January 2019

Improving the Gene Ontology Resource to Facilitate More Informative Analysis and Interpretation of Alzheimer's Disease Data.

Genes (Basel) 2018 Nov 29;9(12). Epub 2018 Nov 29.

UCL Institute of Cardiovascular Science, University College London, Rayne Building, 5 University Street, London WC1E 6JF, UK.

The analysis and interpretation of high-throughput datasets relies on access to high-quality bioinformatics resources, as well as processing pipelines and analysis tools. Gene Ontology (GO, geneontology.org) is a major resource for gene enrichment analysis. The aim of this project, funded by the Alzheimer's Research United Kingdom (ARUK) foundation and led by the University College London (UCL) biocuration team, was to enhance the GO resource by developing new neurological GO terms, and use GO terms to annotate gene products associated with dementia. Specifically, proteins and protein complexes relevant to processes involving amyloid-beta and tau have been annotated and the resulting annotations are denoted in GO databases as 'ARUK-UCL'. Biological knowledge presented in the scientific literature was captured through the association of GO terms with dementia-relevant protein records; GO itself was revised, and new GO terms were added. This literature biocuration increased the number of Alzheimer's-relevant gene products that were being associated with neurological GO terms, such as 'amyloid-beta clearance' or 'learning or memory', as well as neuronal structures and their compartments. Of the total 2055 annotations that we contributed for the prioritised gene products, 526 have associated proteins and complexes with neurological GO terms. To ensure that these descriptive annotations could be provided for Alzheimer's-relevant gene products, over 70 new GO terms were created. Here, we describe how the improvements in ontology development and biocuration resulting from this initiative can benefit the scientific community and enhance the interpretation of dementia data.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3390/genes9120593DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6315915PMC
November 2018

GWAS and colocalization analyses implicate carotid intima-media thickness and carotid plaque loci in cardiovascular outcomes.

Authors:
Nora Franceschini Claudia Giambartolomei Paul S de Vries Chris Finan Joshua C Bis Rachael P Huntley Ruth C Lovering Salman M Tajuddin Thomas W Winkler Misa Graff Maryam Kavousi Caroline Dale Albert V Smith Edith Hofer Elisabeth M van Leeuwen Ilja M Nolte Lingyi Lu Markus Scholz Muralidharan Sargurupremraj Niina Pitkänen Oscar Franzén Peter K Joshi Raymond Noordam Riccardo E Marioni Shih-Jen Hwang Solomon K Musani Ulf Schminke Walter Palmas Aaron Isaacs Adolfo Correa Alan B Zonderman Albert Hofman Alexander Teumer Amanda J Cox André G Uitterlinden Andrew Wong Andries J Smit Anne B Newman Annie Britton Arno Ruusalepp Bengt Sennblad Bo Hedblad Bogdan Pasaniuc Brenda W Penninx Carl D Langefeld Christina L Wassel Christophe Tzourio Cristiano Fava Damiano Baldassarre Daniel H O'Leary Daniel Teupser Diana Kuh Elena Tremoli Elmo Mannarino Enzo Grossi Eric Boerwinkle Eric E Schadt Erik Ingelsson Fabrizio Veglia Fernando Rivadeneira Frank Beutner Ganesh Chauhan Gerardo Heiss Harold Snieder Harry Campbell Henry Völzke Hugh S Markus Ian J Deary J Wouter Jukema Jacqueline de Graaf Jacqueline Price Janne Pott Jemma C Hopewell Jingjing Liang Joachim Thiery Jorgen Engmann Karl Gertow Kenneth Rice Kent D Taylor Klodian Dhana Lambertus A L M Kiemeney Lars Lind Laura M Raffield Lenore J Launer Lesca M Holdt Marcus Dörr Martin Dichgans Matthew Traylor Matthias Sitzer Meena Kumari Mika Kivimaki Mike A Nalls Olle Melander Olli Raitakari Oscar H Franco Oscar L Rueda-Ochoa Panos Roussos Peter H Whincup Philippe Amouyel Philippe Giral Pramod Anugu Quenna Wong Rainer Malik Rainer Rauramaa Ralph Burkhardt Rebecca Hardy Reinhold Schmidt Renée de Mutsert Richard W Morris Rona J Strawbridge S Goya Wannamethee Sara Hägg Sonia Shah Stela McLachlan Stella Trompet Sudha Seshadri Sudhir Kurl Susan R Heckbert Susan Ring Tamara B Harris Terho Lehtimäki Tessel E Galesloot Tina Shah Ulf de Faire Vincent Plagnol Wayne D Rosamond Wendy Post Xiaofeng Zhu Xiaoling Zhang Xiuqing Guo Yasaman Saba Abbas Dehghan Adrie Seldenrijk Alanna C Morrison Anders Hamsten Bruce M Psaty Cornelia M van Duijn Deborah A Lawlor Dennis O Mook-Kanamori Donald W Bowden Helena Schmidt James F Wilson James G Wilson Jerome I Rotter Joanna M Wardlaw John Deanfield Julian Halcox Leo-Pekka Lyytikäinen Markus Loeffler Michele K Evans Stéphanie Debette Steve E Humphries Uwe Völker Vilmundur Gudnason Aroon D Hingorani Johan L M Björkegren Juan P Casas Christopher J O'Donnell

Nat Commun 2018 12 3;9(1):5141. Epub 2018 Dec 3.

Intramural Administration Management Branch, National Heart, Lung, and Blood Institute, NIH, Bethesda, MD, 20892, USA.

Carotid artery intima media thickness (cIMT) and carotid plaque are measures of subclinical atherosclerosis associated with ischemic stroke and coronary heart disease (CHD). Here, we undertake meta-analyses of genome-wide association studies (GWAS) in 71,128 individuals for cIMT, and 48,434 individuals for carotid plaque traits. We identify eight novel susceptibility loci for cIMT, one independent association at the previously-identified PINX1 locus, and one novel locus for carotid plaque. Colocalization analysis with nearby vascular expression quantitative loci (cis-eQTLs) derived from arterial wall and metabolic tissues obtained from patients with CHD identifies candidate genes at two potentially additional loci, ADAMTS9 and LOXL4. LD score regression reveals significant genetic correlations between cIMT and plaque traits, and both cIMT and plaque with CHD, any stroke subtype and ischemic stroke. Our study provides insights into genes and tissue-specific regulatory mechanisms linking atherosclerosis both to its functional genomic origins and its clinical consequences in humans.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41467-018-07340-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6277418PMC
December 2018

Expanding the horizons of microRNA bioinformatics.

RNA 2018 08 5;24(8):1005-1017. Epub 2018 Jun 5.

Institute of Cardiovascular Science, University College London, London WC1E 6JF, United Kingdom.

MicroRNA regulation of key biological and developmental pathways is a rapidly expanding area of research, accompanied by vast amounts of experimental data. This data, however, is not widely available in bioinformatic resources, making it difficult for researchers to find and analyze microRNA-related experimental data and define further research projects. We are addressing this problem by providing two new bioinformatics data sets that contain experimentally verified functional information for mammalian microRNAs involved in cardiovascular-relevant, and other, processes. To date, our resource provides over 4400 Gene Ontology annotations associated with over 500 microRNAs from human, mouse, and rat and over 2400 experimentally validated microRNA:target interactions. We illustrate how this resource can be used to create microRNA-focused interaction networks with a biological context using the known biological role of microRNAs and the mRNAs they regulate, enabling discovery of associations between gene products, biological pathways and, ultimately, diseases. This data will be crucial in advancing the field of microRNA bioinformatics and will establish consistent data sets for reproducible functional analysis of microRNAs across all biological research areas.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1261/rna.065565.118DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6049505PMC
August 2018

Improving Interpretation of Cardiac Phenotypes and Enhancing Discovery With Expanded Knowledge in the Gene Ontology.

Circ Genom Precis Med 2018 02;11(2):e001813

From the Institute of Cardiovascular Science (R.C.L., V.K.K., R.E.F., N.H.C., R.P.H., P.J.T., P.D.L., P.M.E., L.C.) and Metabolism and Experimental Therapeutics, Division of Medicine (R.B.), University College London, United Kingdom; European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Hinxton, United Kingdom (P.R., D.O.-S.); Gene Ontology Consortium (P.R., T.Z.B., D.O.-S., J.A.B., D.P.H.); The Zebrafish Model Organism Database, University of Oregon, Eugene (D.G.H.); Rat Genome Database, Human Molecular Genetics Center, Medical College of Wisconsin, Milwaukee (S.J.F.L.); Arabidopsis Information Resource, Phoenix Bioinformatics, Fremont, CA (T.Z.B.); FlyBase, University of Cambridge, United Kingdom (S.T.); Mouse Genome Informatics, The Jackson Laboratory, Bar Harbor, ME (J.A.B., D.P.H.); Oxbridge BHF Centre of Regenerative Medicine, Department of Physiology, Anatomy and Genetics, University of Oxford, United Kingdom (P.R.R.); and William Harvey Heart Centre, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, United Kingdom (A.T.).

Background: A systems biology approach to cardiac physiology requires a comprehensive representation of how coordinated processes operate in the heart, as well as the ability to interpret relevant transcriptomic and proteomic experiments. The Gene Ontology (GO) Consortium provides structured, controlled vocabularies of biological terms that can be used to summarize and analyze functional knowledge for gene products.

Methods And Results: In this study, we created a computational resource to facilitate genetic studies of cardiac physiology by integrating literature curation with attention to an improved and expanded ontological representation of heart processes in the Gene Ontology. As a result, the Gene Ontology now contains terms that comprehensively describe the roles of proteins in cardiac muscle cell action potential, electrical coupling, and the transmission of the electrical impulse from the sinoatrial node to the ventricles. Evaluating the effectiveness of this approach to inform data analysis demonstrated that Gene Ontology annotations, analyzed within an expanded ontological context of heart processes, can help to identify candidate genes associated with arrhythmic disease risk loci.

Conclusions: We determined that a combination of curation and ontology development for heart-specific genes and processes supports the identification and downstream analysis of genes responsible for the spread of the cardiac action potential through the heart. Annotating these genes and processes in a structured format facilitates data analysis and supports effective retrieval of gene-centric information about cardiac defects.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1161/CIRCGEN.117.001813DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5821137PMC
February 2018

The Gene Ontology of eukaryotic cilia and flagella.

Cilia 2017 16;6:10. Epub 2017 Nov 16.

European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD UK.

Background: Recent research into ciliary structure and function provides important insights into inherited diseases termed ciliopathies and other cilia-related disorders. This wealth of knowledge needs to be translated into a computational representation to be fully exploitable by the research community. To this end, members of the Gene Ontology (GO) and SYSCILIA Consortia have worked together to improve representation of ciliary substructures and processes in GO.

Methods: Members of the SYSCILIA and Gene Ontology Consortia suggested additions and changes to GO, to reflect new knowledge in the field. The project initially aimed to improve coverage of ciliary parts, and was then broadened to cilia-related biological processes. Discussions were documented in a public tracker. We engaged the broader cilia community via direct consultation and by referring to the literature. Ontology updates were implemented via ontology editing tools.

Results: So far, we have created or modified 127 GO terms representing parts and processes related to eukaryotic cilia/flagella or prokaryotic flagella. A growing number of biological pathways are known to involve cilia, and we continue to incorporate this knowledge in GO. The resulting expansion in GO allows more precise representation of experimentally derived knowledge, and SYSCILIA and GO biocurators have created 199 annotations to 50 human ciliary proteins. The revised ontology was also used to curate mouse proteins in a collaborative project. The revised GO and annotations, used in comparative 'before and after' analyses of representative ciliary datasets, improve enrichment results significantly.

Conclusions: Our work has resulted in a broader and deeper coverage of ciliary composition and function. These improvements in ontology and protein annotation will benefit all users of GO enrichment analysis tools, as well as the ciliary research community, in areas ranging from microscopy image annotation to interpretation of high-throughput studies. We welcome feedback to further enhance the representation of cilia biology in GO.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1186/s13630-017-0054-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5688719PMC
November 2017

MicroRNA Biomarkers and Platelet Reactivity: The Clot Thickens.

Circ Res 2017 Jan;120(2):418-435

From the King's British Heart Foundation Centre, King's College London, United Kingdom (N.S., P.S., T.B., R.L., A.J., M.M.); and Centre for Cardiovascular Genetics, Institute of Cardiovascular Science, University College London, United Kingdom (R.P.H., R.C.L.).

Over the last few years, several groups have evaluated the potential of microRNAs (miRNAs) as biomarkers for cardiometabolic disease. In this review, we discuss the emerging literature on the role of miRNAs and other small noncoding RNAs in platelets and in the circulation, and the potential use of miRNAs as biomarkers for platelet activation. Platelets are a major source of miRNAs, YRNAs, and circular RNAs. By harnessing multiomics approaches, we may gain valuable insights into their potential function. Because not all miRNAs are detectable in the circulation, we also created a gene ontology annotation for circulating miRNAs using the gene ontology term extracellular space as part of blood plasma. Finally, we share key insights for measuring circulating miRNAs. We propose ways to standardize miRNA measurements, in particular by using platelet-poor plasma to avoid confounding caused by residual platelets in plasma or by adding RNase inhibitors to serum to reduce degradation. This should enhance comparability of miRNA measurements across different cohorts. We provide recommendations for future miRNA biomarker studies, emphasizing the need for accurate interpretation within a biological and methodological context.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1161/CIRCRESAHA.116.309303DOI Listing
January 2017

Annotation Extensions.

Methods Mol Biol 2017 ;1446:233-243

Functional Gene Annotation Initiative, Centre for Cardiovascular Genetics, Institute of Cardiovascular Science, University College London, 5 University Street, London, WC1E 6JF, UK.

The specificity of knowledge that Gene Ontology (GO) annotations currently can represent is still restricted by the legacy format of the GO annotation file, a format intentionally designed for simplicity to keep the barriers to entry low and thus encourage initial adoption. Historically, the information that could be captured in a GO annotation was simply the role or location of a gene product, although genetically interacting or binding partners could be specified. While there was no mechanism within the original GO annotation format for capturing additional information about the context of a GO term, such as the target gene of an activity or the location of a molecular function, the long-term vision for the GO Consortium was to provide greater expressivity in its annotations to capture physiologically relevant information.Thus, as a step forwards, the GO Consortium has introduced a new field into the annotation format, annotation extensions, which can be used to capture valuable contextual detail. This provides experimentally verified links between gene products and other physiological information that is crucial for accurate analysis of pathway and network data. This chapter will provide a simple overview of annotation extensions, illustrated with examples of their usage, and explain why they are useful for scientists and bioinformaticians alike.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1007/978-1-4939-3743-1_17DOI Listing
December 2017

An expanded evaluation of protein function prediction methods shows an improvement in accuracy.

Authors:
Yuxiang Jiang Tal Ronnen Oron Wyatt T Clark Asma R Bankapur Daniel D'Andrea Rosalba Lepore Christopher S Funk Indika Kahanda Karin M Verspoor Asa Ben-Hur Da Chen Emily Koo Duncan Penfold-Brown Dennis Shasha Noah Youngs Richard Bonneau Alexandra Lin Sayed M E Sahraeian Pier Luigi Martelli Giuseppe Profiti Rita Casadio Renzhi Cao Zhaolong Zhong Jianlin Cheng Adrian Altenhoff Nives Skunca Christophe Dessimoz Tunca Dogan Kai Hakala Suwisa Kaewphan Farrokh Mehryary Tapio Salakoski Filip Ginter Hai Fang Ben Smithers Matt Oates Julian Gough Petri Törönen Patrik Koskinen Liisa Holm Ching-Tai Chen Wen-Lian Hsu Kevin Bryson Domenico Cozzetto Federico Minneci David T Jones Samuel Chapman Dukka Bkc Ishita K Khan Daisuke Kihara Dan Ofer Nadav Rappoport Amos Stern Elena Cibrian-Uhalte Paul Denny Rebecca E Foulger Reija Hieta Duncan Legge Ruth C Lovering Michele Magrane Anna N Melidoni Prudence Mutowo-Meullenet Klemens Pichler Aleksandra Shypitsyna Biao Li Pooya Zakeri Sarah ElShal Léon-Charles Tranchevent Sayoni Das Natalie L Dawson David Lee Jonathan G Lees Ian Sillitoe Prajwal Bhat Tamás Nepusz Alfonso E Romero Rajkumar Sasidharan Haixuan Yang Alberto Paccanaro Jesse Gillis Adriana E Sedeño-Cortés Paul Pavlidis Shou Feng Juan M Cejuela Tatyana Goldberg Tobias Hamp Lothar Richter Asaf Salamov Toni Gabaldon Marina Marcet-Houben Fran Supek Qingtian Gong Wei Ning Yuanpeng Zhou Weidong Tian Marco Falda Paolo Fontana Enrico Lavezzo Stefano Toppo Carlo Ferrari Manuel Giollo Damiano Piovesan Silvio C E Tosatto Angela Del Pozo José M Fernández Paolo Maietta Alfonso Valencia Michael L Tress Alfredo Benso Stefano Di Carlo Gianfranco Politano Alessandro Savino Hafeez Ur Rehman Matteo Re Marco Mesiti Giorgio Valentini Joachim W Bargsten Aalt D J van Dijk Branislava Gemovic Sanja Glisic Vladmir Perovic Veljko Veljkovic Nevena Veljkovic Danillo C Almeida-E-Silva Ricardo Z N Vencio Malvika Sharan Jörg Vogel Lakesh Kansakar Shanshan Zhang Slobodan Vucetic Zheng Wang Michael J E Sternberg Mark N Wass Rachael P Huntley Maria J Martin Claire O'Donovan Peter N Robinson Yves Moreau Anna Tramontano Patricia C Babbitt Steven E Brenner Michal Linial Christine A Orengo Burkhard Rost Casey S Greene Sean D Mooney Iddo Friedberg Predrag Radivojac

Genome Biol 2016 09 7;17(1):184. Epub 2016 Sep 7.

Department of Computer Science and Informatics, Indiana University, Bloomington, IN, USA.

Background: A major bottleneck in our understanding of the molecular underpinnings of life is the assignment of function to proteins. While molecular experiments provide the most reliable annotation of proteins, their relatively low throughput and restricted purview have led to an increasing role for computational function prediction. However, assessing methods for protein function prediction and tracking progress in the field remain challenging.

Results: We conducted the second critical assessment of functional annotation (CAFA), a timed challenge to assess computational methods that automatically assign protein function. We evaluated 126 methods from 56 research groups for their ability to predict biological functions using Gene Ontology and gene-disease associations using Human Phenotype Ontology on a set of 3681 proteins from 18 species. CAFA2 featured expanded analysis compared with CAFA1, with regards to data set size, variety, and assessment metrics. To review progress in the field, the analysis compared the best methods from CAFA1 to those of CAFA2.

Conclusions: The top-performing methods in CAFA2 outperformed those from CAFA1. This increased accuracy can be attributed to a combination of the growing number of experimental annotations and improved methods for function prediction. The assessment also revealed that the definition of top-performing algorithms is ontology specific, that different performance metrics can be used to probe the nature of accurate predictions, and the relative diversity of predictions in the biological process and human phenotype ontologies. While there was methodological improvement between CAFA1 and CAFA2, the interpretation of results and usefulness of individual methods remain context-dependent.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1186/s13059-016-1037-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5015320PMC
September 2016

Gene regulation knowledge commons: community action takes care of DNA binding transcription factors.

Database (Oxford) 2016 5;2016. Epub 2016 Jun 5.

Department of Biology, Norwegian University of Science and Technology (NTNU), 7491 Trondheim, Norway

A large gap remains between the amount of knowledge in scientific literature and the fraction that gets curated into standardized databases, despite many curation initiatives. Yet the availability of comprehensive knowledge in databases is crucial for exploiting existing background knowledge, both for designing follow-up experiments and for interpreting new experimental data. Structured resources also underpin the computational integration and modeling of regulatory pathways, which further aids our understanding of regulatory dynamics. We argue how cooperation between the scientific community and professional curators can increase the capacity of capturing precise knowledge from literature. We demonstrate this with a project in which we mobilize biological domain experts who curate large amounts of DNA binding transcription factors, and show that they, although new to the field of curation, can make valuable contributions by harvesting reported knowledge from scientific papers. Such community curation can enhance the scientific epistemic process.Database URL: http://www.tfcheckpoint.org.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/database/baw088DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4911790PMC
January 2017

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

RNA 2016 May 25;22(5):667-76. Epub 2016 Feb 25.

Centre for Cardiovascular Genetics, Institute of Cardiovascular Science, University College London, London WC1E 6JF, United Kingdom.

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).
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1261/rna.055301.115DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4836642PMC
May 2016

The GOA database: gene Ontology annotation updates for 2015.

Nucleic Acids Res 2015 Jan 6;43(Database issue):D1057-63. Epub 2014 Nov 6.

European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.

The Gene Ontology Annotation (GOA) resource (http://www.ebi.ac.uk/GOA) provides evidence-based Gene Ontology (GO) annotations to proteins in the UniProt Knowledgebase (UniProtKB). Manual annotations provided by UniProt curators are supplemented by manual and automatic annotations from model organism databases and specialist annotation groups. GOA currently supplies 368 million GO annotations to almost 54 million proteins in more than 480,000 taxonomic groups. The resource now provides annotations to five times the number of proteins it did 4 years ago. As a member of the GO Consortium, we adhere to the most up-to-date Consortium-agreed annotation guidelines via the use of quality control checks that ensures that the GOA resource supplies high-quality functional information to proteins from a wide range of species. Annotations from GOA are freely available and are accessible through a powerful web browser as well as a variety of annotation file formats.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/nar/gku1113DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4383930PMC
January 2015

Standardized description of scientific evidence using the Evidence Ontology (ECO).

Database (Oxford) 2014 22;2014. Epub 2014 Jul 22.

Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201, USA, Department of Microbiology and Immunology, University of Maryland School of Medicine, Baltimore, MD 21201, USA, Genomics Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA, Saccharomyces Genome Database, Department of Genetics, Stanford University, Stanford, CA 94305, USA, Computational Biology and Bioinformatics, The Jackson Laboratory, Bar Harbor, ME 04609, USA, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD UK, Department of Epidemiology, University of Maryland School of Medicine, Baltimore, MD 21201, USA and Department of Medicine, University of Maryland School of Medicine, Baltimore, MD 21201, USAInstitute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201, USA, Department of Microbiology and Immunology, University of Maryland School of Medicine, Baltimore, MD 21201, USA, Genomics Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA, Saccharomyces Genome Database, Department of Genetics, Stanford University, Stanford, CA 94305, USA, Computational Biology and Bioinformatics, The Jackson Laboratory, Bar Harbor, ME 04609, USA, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD UK, Department of Epidemiology, University of Maryland School of Medicine, Baltimore, MD 21201, USA and Department of Medicine, University of Maryland School of Medicine, Baltimore, MD 21201, USA.

The Evidence Ontology (ECO) is a structured, controlled vocabulary for capturing evidence in biological research. ECO includes diverse terms for categorizing evidence that supports annotation assertions including experimental types, computational methods, author statements and curator inferences. Using ECO, annotation assertions can be distinguished according to the evidence they are based on such as those made by curators versus those automatically computed or those made via high-throughput data review versus single test experiments. Originally created for capturing evidence associated with Gene Ontology annotations, ECO is now used in other capacities by many additional annotation resources including UniProt, Mouse Genome Informatics, Saccharomyces Genome Database, PomBase, the Protein Information Resource and others. Information on the development and use of ECO can be found at http://evidenceontology.org. The ontology is freely available under Creative Commons license (CC BY-SA 3.0), and can be downloaded in both Open Biological Ontologies and Web Ontology Language formats at http://code.google.com/p/evidenceontology. Also at this site is a tracker for user submission of term requests and questions. ECO remains under active development in response to user-requested terms and in collaborations with other ontologies and database resources. Database URL: Evidence Ontology Web site: http://evidenceontology.org.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/database/bau075DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4105709PMC
February 2015

Representing kidney development using the gene ontology.

PLoS One 2014 18;9(6):e99864. Epub 2014 Jun 18.

European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom.

Gene Ontology (GO) provides dynamic controlled vocabularies to aid in the description of the functional biological attributes and subcellular locations of gene products from all taxonomic groups (www.geneontology.org). Here we describe collaboration between the renal biomedical research community and the GO Consortium to improve the quality and quantity of GO terms describing renal development. In the associated annotation activity, the new and revised terms were associated with gene products involved in renal development and function. This project resulted in a total of 522 GO terms being added to the ontology and the creation of approximately 9,600 kidney-related GO term associations to 940 UniProt Knowledgebase (UniProtKB) entries, covering 66 taxonomic groups. We demonstrate the impact of these improvements on the interpretation of GO term analyses performed on genes differentially expressed in kidney glomeruli affected by diabetic nephropathy. In summary, we have produced a resource that can be utilized in the interpretation of data from small- and large-scale experiments investigating molecular mechanisms of kidney function and development and thereby help towards alleviating renal disease.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0099864PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4062467PMC
July 2015

A method for increasing expressivity of Gene Ontology annotations using a compositional approach.

BMC Bioinformatics 2014 May 21;15:155. Epub 2014 May 21.

Lawrence Berkeley National Laboratory, Genomics Division, Berkeley, CA 94720, USA.

Background: The Gene Ontology project integrates data about the function of gene products across a diverse range of organisms, allowing the transfer of knowledge from model organisms to humans, and enabling computational analyses for interpretation of high-throughput experimental and clinical data. The core data structure is the annotation, an association between a gene product and a term from one of the three ontologies comprising the GO. Historically, it has not been possible to provide additional information about the context of a GO term, such as the target gene or the location of a molecular function. This has limited the specificity of knowledge that can be expressed by GO annotations.

Results: The GO Consortium has introduced annotation extensions that enable manually curated GO annotations to capture additional contextual details. Extensions represent effector-target relationships such as localization dependencies, substrates of protein modifiers and regulation targets of signaling pathways and transcription factors as well as spatial and temporal aspects of processes such as cell or tissue type or developmental stage. We describe the content and structure of annotation extensions, provide examples, and summarize the current usage of annotation extensions.

Conclusions: The additional contextual information captured by annotation extensions improves the utility of functional annotation by representing dependencies between annotations to terms in the different ontologies of GO, external ontologies, or an organism's gene products. These enhanced annotations can also support sophisticated queries and reasoning, and will provide curated, directional links between many gene products to support pathway and network reconstruction.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1186/1471-2105-15-155DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4039540PMC
May 2014

Understanding how and why the Gene Ontology and its annotations evolve: the GO within UniProt.

Gigascience 2014 Mar 18;3(1). Epub 2014 Mar 18.

European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.

The Gene Ontology Consortium (GOC) is a major bioinformatics project that provides structured controlled vocabularies to classify gene product function and location. GOC members create annotations to gene products using the Gene Ontology (GO) vocabularies, thus providing an extensive, publicly available resource. The GO and its annotations to gene products are now an integral part of functional analysis, and statistical tests using GO data are becoming routine for researchers to include when publishing functional information. While many helpful articles about the GOC are available, there are certain updates to the ontology and annotation sets that sometimes go unobserved. Here we describe some of the ways in which GO can change that should be carefully considered by all users of GO as they may have a significant impact on the resulting gene product annotations, and therefore the functional description of the gene product, or the interpretation of analyses performed on GO datasets. GO annotations for gene products change for many reasons, and while these changes generally improve the accuracy of the representation of the underlying biology, they do not necessarily imply that previous annotations were incorrect. We additionally describe the quality assurance mechanisms we employ to improve the accuracy of annotations, which necessarily changes the composition of the annotation sets we provide. We use the Universal Protein Resource (UniProt) for illustrative purposes of how the GO Consortium, as a whole, manages these changes.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1186/2047-217X-3-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3995153PMC
March 2014

Use of Gene Ontology Annotation to understand the peroxisome proteome in humans.

Database (Oxford) 2013 17;2013:bas062. Epub 2013 Jan 17.

EMBL-EBI, The Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK.

The Gene Ontology (GO) is the de facto standard for the functional description of gene products, providing a consistent, information-rich terminology applicable across species and information repositories. The UniProt Consortium uses both manual and automatic GO annotation approaches to curate UniProt Knowledgebase (UniProtKB) entries. The selection of a protein set prioritized for manual annotation has implications for the characteristics of the information provided to users working in a specific field or interested in particular pathways or processes. In this article, we describe an organelle-focused, manual curation initiative targeting proteins from the human peroxisome. We discuss the steps taken to define the peroxisome proteome and the challenges encountered in defining the boundaries of this protein set. We illustrate with the use of examples how GO annotations now capture cell and tissue type information and the advantages that such an annotation approach provides to users. Database URL: http://www.ebi.ac.uk/GOA/ and http://www.uniprot.org.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/database/bas062DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3548334PMC
June 2013

The impact of focused Gene Ontology curation of specific mammalian systems.

PLoS One 2011 9;6(12):e27541. Epub 2011 Dec 9.

EMBL-European Bioinformatics Institute, Hinxton, Cambridge, United Kingdom.

Unlabelled: The Gene Ontology (GO) resource provides dynamic controlled vocabularies to provide an information-rich resource to aid in the consistent description of the functional attributes and subcellular locations of gene products from all taxonomic groups (www.geneontology.org). System-focused projects, such as the Renal and Cardiovascular GO Annotation Initiatives, aim to provide detailed GO data for proteins implicated in specific organ development and function. Such projects support the rapid evaluation of new experimental data and aid in the generation of novel biological insights to help alleviate human disease. This paper describes the improvement of GO data for renal and cardiovascular research communities and demonstrates that the cardiovascular-focused GO annotations, created over the past three years, have led to an evident improvement of microarray interpretation. The reanalysis of cardiovascular microarray datasets confirms the need to continue to improve the annotation of the human proteome.

Availability: GO ANNOTATION DATA IS FREELY AVAILABLE FROM: ftp://ftp.geneontology.org/pub/go/gene-associations/
View Article and Find Full Text PDF

Download full-text PDF

Source
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0027541PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3235096PMC
April 2012

The UniProt-GO Annotation database in 2011.

Nucleic Acids Res 2012 Jan 28;40(Database issue):D565-70. Epub 2011 Nov 28.

European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.

The GO annotation dataset provided by the UniProt Consortium (GOA: http://www.ebi.ac.uk/GOA) is a comprehensive set of evidenced-based associations between terms from the Gene Ontology resource and UniProtKB proteins. Currently supplying over 100 million annotations to 11 million proteins in more than 360,000 taxa, this resource has increased 2-fold over the last 2 years and has benefited from a wealth of checks to improve annotation correctness and consistency as well as now supplying a greater information content enabled by GO Consortium annotation format developments. Detailed, manual GO annotations obtained from the curation of peer-reviewed papers are directly contributed by all UniProt curators and supplemented with manual and electronic annotations from 36 model organism and domain-focused scientific resources. The inclusion of high-quality, automatic annotation predictions ensures the UniProt GO annotation dataset supplies functional information to a wide range of proteins, including those from poorly characterized, non-model organism species. UniProt GO annotations are freely available in a range of formats accessible by both file downloads and web-based views. In addition, the introduction of a new, normalized file format in 2010 has made for easier handling of the complete UniProt-GOA data set.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/nar/gkr1048DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3245010PMC
January 2012

The Renal Gene Ontology Annotation Initiative.

Organogenesis 2010 Apr-Jun;6(2):71-5

EMBL-European Bioinformatics Institute, Hinxton, Cambridge UK.

The gene ontology (go) resource provides dynamic controlled vocabularies to aid in the description of the functional attributes and subcellular locations of gene products from all taxonomic groups (www.geneontology.org). A renal-focused curation initiative, funded by Kidney Research UK and supported by the GO Consortium, has started at the European Bioinformatics Institute and aims to provide a detailed GO resource for mammalian proteins implicated in renal development and function. This report outlines the aims of this initiative and explains how the renal community can become involved to help improve the availability, quality and quantity of GO terms and their association to specific proteins.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2901810PMC
http://dx.doi.org/10.4161/org.6.2.11294DOI Listing
August 2012

QuickGO: a user tutorial for the web-based Gene Ontology browser.

Database (Oxford) 2009 29;2009:bap010. Epub 2009 Sep 29.

European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.

The Gene Ontology (GO) has proven to be a valuable resource for functional annotation of gene products. At well over 27 000 terms, the descriptiveness of GO has increased rapidly in line with the biological data it represents. Therefore, it is vital to be able to easily and quickly mine the functional information that has been made available through these GO terms being associated with gene products. QuickGO is a fast, web-based tool for browsing the GO and all associated GO annotations provided by the GOA group. After undergoing a redevelopment, QuickGO is now able to offer many more features beyond simple browsing. Users have responded well to the new tool and given very positive feedback about its usefulness. This tutorial will demonstrate how some of these features could be useful to the researcher wanting to discover more about their dataset, particular areas of biology or to find new ways of directing their research.Database URL:http://www.ebi.ac.uk/QuickGO.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/database/bap010DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2794795PMC
September 2009

Dissecting regulatory pathways of G1/S control in Arabidopsis: common and distinct targets of CYCD3;1, E2Fa and E2Fc.

Plant Mol Biol 2009 Nov 7;71(4-5):345-65. Epub 2009 Aug 7.

Department of Chemical Engineering and Biotechnology, University of Cambridge, Tennis Court Road, Cambridge CB2 1QT, UK.

Activation of E2F transcription factors at the G1-to-S phase boundary, with the resultant expression of genes needed for DNA synthesis and S-phase, is due to phosphorylation of the retinoblastoma-related (RBR) protein by cyclin D-dependent kinase (CYCD-CDK), particularly CYCD3-CDKA. Arabidopsis has three canonical E2F genes, of which E2Fa and E2Fb are proposed to encode transcriptional activators and E2Fc a repressor. Previous studies have identified genes regulated in response to high-level constitutive expression of E2Fa and of CYCD3;1, but such plants display significant phenotypic abnormalities. We have sought to identify targets that show responses to lower level induced changes in abundance of these cell cycle regulators. Expression of E2Fa, E2Fc or CYCD3;1 was induced using dexamethasone and the effects analysed using microarrays in a time course allowing short and longer term effects to be observed. Overlap between CYCD3;1 and E2Fa modulated genes substantiates their action in a common pathway with a key role in controlling the G1/S transition, with additional targets for CYCD3;1 in chromatin modification and for E2Fa in cell wall biogenesis and development. E2Fc induction led primarily to gene downregulation, but did not antagonise E2Fa action and hence E2Fc appears to function outside the CYCD3-RBR pathway, does not have a direct effect on cell cycle genes, and promoter analysis suggests a distinct binding site preference.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1007/s11103-009-9527-5DOI Listing
November 2009

The GOA database in 2009--an integrated Gene Ontology Annotation resource.

Nucleic Acids Res 2009 Jan 27;37(Database issue):D396-403. Epub 2008 Oct 27.

Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.

The Gene Ontology Annotation (GOA) project at the EBI (http://www.ebi.ac.uk/goa) provides high-quality electronic and manual associations (annotations) of Gene Ontology (GO) terms to UniProt Knowledgebase (UniProtKB) entries. Annotations created by the project are collated with annotations from external databases to provide an extensive, publicly available GO annotation resource. Currently covering over 160 000 taxa, with greater than 32 million annotations, GOA remains the largest and most comprehensive open-source contributor to the GO Consortium (GOC) project. Over the last five years, the group has augmented the number and coverage of their electronic pipelines and a number of new manual annotation projects and collaborations now further enhance this resource. A range of files facilitate the download of annotations for particular species, and GO term information and associated annotations can also be viewed and downloaded from the newly developed GOA QuickGO tool (http://www.ebi.ac.uk/QuickGO), which allows users to precisely tailor their annotation set.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/nar/gkn803DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2686469PMC
January 2009

Cytokinins and gibberellins in sap exudate of the oil palm.

Phytochemistry 2002 May;60(2):117-27

Department of Plant Sciences, Cambridge University, Downing Street, CB2 3EA, Cambridge, UK.

Exudates were collected from stumps of pre-anthesis inflorescences of oil palm and analysed for cytokinin and gibberellin content using combined HPLC-ELISA techniques. Three antisera, for zeatin-type, dihydrozeatin-type and isopentenyladenine-type cytokinins, were used in ELISAs to identify members of these three groups of cytokinins. Ribotides, 9-glucosides, free bases and ribosides were detected for each of the groups with zeatin riboside the most abundant cytokinin identified in the exudate. Isopentenyladenine-type and dihydrozeatin-type cytokinins were also identified but at lower levels. In addition, two monoclonal antibodies were used in the development of novel ELISAs for members of the 13-hydroxylated and non-13-hydroxylated families of gibberellins. The new ELISAs allow the determination of gibberellins in smaller amounts of tissue than are required for GC-MS. The most abundant gibberellins identified in exudates were GA19 and GA44, as well as other members of the early 13-hydroxylation pathway. Gibberellins were confirmed by GC-MS. The presence of these types of growth regulators in exudate supplying immature inflorescences suggest they have a role in growth and development of these structures.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/s0031-9422(02)00099-7DOI Listing
May 2002