scholarly journals Unifying Themes in Microbial Associations with Animal and Plant Hosts Described Using the Gene Ontology

2010 ◽  
Vol 74 (4) ◽  
pp. 479-503 ◽  
Author(s):  
Trudy Torto-Alalibo ◽  
Candace W. Collmer ◽  
Michelle Gwinn-Giglio ◽  
Magdalen Lindeberg ◽  
Shaowu Meng ◽  
...  
Keyword(s):  
Gene Ontology ◽  
Effector Proteins ◽  
Biological Processes ◽  
Gene Products ◽  
Host Defenses ◽  
Host Interactions ◽  
Microbial Associations ◽  
Bioinformatic Approaches ◽  
Go Terms ◽  
Diverse Plant

SUMMARY Microbes form intimate relationships with hosts (symbioses) that range from mutualism to parasitism. Common microbial mechanisms involved in a successful host association include adhesion, entry of the microbe or its effector proteins into the host cell, mitigation of host defenses, and nutrient acquisition. Genes associated with these microbial mechanisms are known for a broad range of symbioses, revealing both divergent and convergent strategies. Effective comparisons among these symbioses, however, are hampered by inconsistent descriptive terms in the literature for functionally similar genes. Bioinformatic approaches that use homology-based tools are limited to identifying functionally similar genes based on similarities in their sequences. An effective solution to these limitations is provided by the Gene Ontology (GO), which provides a standardized language to describe gene products from all organisms. The GO comprises three ontologies that enable one to describe the molecular function(s) of gene products, the biological processes to which they contribute, and their cellular locations. Beginning in 2004, the Plant-Associated Microbe Gene Ontology (PAMGO) interest group collaborated with the GO consortium to extend the GO to accommodate terms for describing gene products associated with microbe-host interactions. Currently, over 900 terms that describe biological processes common to diverse plant- and animal-associated microbes are incorporated into the GO database. Here we review some unifying themes common to diverse host-microbe associations and illustrate how the new GO terms facilitate a standardized description of the gene products involved. We also highlight areas where new terms need to be developed, an ongoing process that should involve the whole community.

scholarly journals Term Matrix: a novel Gene Ontology annotation quality control system based on ontology term co-annotation patterns

Open Biology ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 200149 ◽  
Author(s):  
Valerie Wood ◽  
Seth Carbon ◽  
Midori A. Harris ◽  
Antonia Lock ◽  
Stacia R. Engel ◽  
...  
Keyword(s):  
Quality Control ◽  
Gene Ontology ◽  
Biological Processes ◽  
Gene Products ◽  
Model Species ◽  
Ontology Term ◽  
Protein Coding ◽  
Ontology Structure ◽  
Exclusive Processes ◽  
Annotation Quality

Biological processes are accomplished by the coordinated action of gene products. Gene products often participate in multiple processes, and can therefore be annotated to multiple Gene Ontology (GO) terms. Nevertheless, processes that are functionally, temporally and/or spatially distant may have few gene products in common, and co-annotation to unrelated processes probably reflects errors in literature curation, ontology structure or automated annotation pipelines. We have developed an annotation quality control workflow that uses rules based on mutually exclusive processes to detect annotation errors, based on and validated by case studies including the three we present here: fission yeast protein-coding gene annotations over time; annotations for cohesin complex subunits in human and model species; and annotations using a selected set of GO biological process terms in human and five model species. For each case study, we reviewed available GO annotations, identified pairs of biological processes which are unlikely to be correctly co-annotated to the same gene products (e.g. amino acid metabolism and cytokinesis), and traced erroneous annotations to their sources. To date we have generated 107 quality control rules, and corrected 289 manual annotations in eukaryotes and over 52 700 automatically propagated annotations across all taxa.

A GRAPH-BASED SEMANTIC SIMILARITY MEASURE FOR THE GENE ONTOLOGY

2011 ◽  
Vol 09 (06) ◽  
pp. 681-695 ◽  
Author(s):  
MARCO A. ALVAREZ ◽  
CHANGHUI YAN
Keyword(s):  
Gene Ontology ◽  
Semantic Similarity ◽  
Common Ancestor ◽  
State Of The Art ◽  
Sequence Similarity ◽  
Similarity Score ◽  
Gene Products ◽  
Semantic Similarity Measure ◽  
Similarity Algorithm ◽  
Go Terms

Existing methods for calculating semantic similarities between pairs of Gene Ontology (GO) terms and gene products often rely on external databases like Gene Ontology Annotation (GOA) that annotate gene products using the GO terms. This dependency leads to some limitations in real applications. Here, we present a semantic similarity algorithm (SSA), that relies exclusively on the GO. When calculating the semantic similarity between a pair of input GO terms, SSA takes into account the shortest path between them, the depth of their nearest common ancestor, and a novel similarity score calculated between the definitions of the involved GO terms. In our work, we use SSA to calculate semantic similarities between pairs of proteins by combining pairwise semantic similarities between the GO terms that annotate the involved proteins. The reliability of SSA was evaluated by comparing the resulting semantic similarities between proteins with the functional similarities between proteins derived from expert annotations or sequence similarity. Comparisons with existing state-of-the-art methods showed that SSA is highly competitive with the other methods. SSA provides a reliable measure for semantics similarity independent of external databases of functional-annotation observations.

scholarly journals Analysis of Important Gene Ontology Terms and Biological Pathways Related to Pancreatic Cancer

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Hang Yin ◽  
ShaoPeng Wang ◽  
Yu-Hang Zhang ◽  
Yu-Dong Cai ◽  
Hailin Liu
Keyword(s):  
Pancreatic Cancer ◽  
Gene Ontology ◽  
Feature Selection Method ◽  
Selection Method ◽  
Biological Processes ◽  
Kegg Pathways ◽  
Extensive Analysis ◽  
The World ◽  
Serious Disease ◽  
Go Terms

Pancreatic cancer is a serious disease that results in more than thirty thousand deaths around the world per year. To design effective treatments, many investigators have devoted themselves to the study of biological processes and mechanisms underlying this disease. However, it is far from complete. In this study, we tried to extract important gene ontology (GO) terms and KEGG pathways for pancreatic cancer by adopting some existing computational methods. Genes that have been validated to be related to pancreatic cancer and have not been validated were represented by features derived from GO terms and KEGG pathways using the enrichment theory. A popular feature selection method, minimum redundancy maximum relevance, was employed to analyze these features and extract important GO terms and KEGG pathways. An extensive analysis of the obtained GO terms and KEGG pathways was provided to confirm the correlations between them and pancreatic cancer.

scholarly journals GAPGOM—an R package for gene annotation prediction using GO Metrics

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Casper van Mourik ◽  
Rezvan Ehsani ◽  
Finn Drabløs
Keyword(s):  
Gene Ontology ◽  
Gene Annotation ◽  
Enrichment Analysis ◽  
R Package ◽  
Prediction Performance ◽  
Gene Products ◽  
Limited Information ◽  
Non Coding Rnas ◽  
User Friendly ◽  
Go Terms

Abstract Objective Properties of gene products can be described or annotated with Gene Ontology (GO) terms. But for many genes we have limited information about their products, for example with respect to function. This is particularly true for long non-coding RNAs (lncRNAs), where the function in most cases is unknown. However, it has been shown that annotation as described by GO terms to some extent can be predicted by enrichment analysis on properties of co-expressed genes. Results GAPGOM integrates two relevant algorithms, lncRNA2GOA and TopoICSim, into a user-friendly R package. Here lncRNA2GOA does annotation prediction by co-expression, whereas TopoICSim estimates similarity between GO graphs, which can be used for benchmarking of prediction performance, but also for comparison of GO graphs in general. The package provides an improved implementation of the original tools, with substantial improvements in performance and documentation, unified interfaces, and additional features.

scholarly journals Mapping the Gene Ontology Into the Unified Medical Language System

2004 ◽  
Vol 5 (4) ◽  
pp. 354-361 ◽  
Author(s):  
Jane Lomax ◽  
Alexa T. McCray
Keyword(s):  
Gene Ontology ◽  
Clinical Medicine ◽  
Gene Products ◽  
National Library ◽  
Web Based ◽  
Language System ◽  
Unified Medical Language System ◽  
Medical Language ◽  
Large Numbers ◽  
Go Terms

We have recently mapped the Gene Ontology (GO), developed by the Gene Ontology Consortium, into the National Library of Medicine's Unified Medical Language System (UMLS). GO has been developed for the purpose of annotating gene products in genome databases, and the UMLS has been developed as a framework for integrating large numbers of disparate terminologies, primarily for the purpose of providing better access to biomedical information sources. The mapping of GO to UMLS highlighted issues in both terminology systems. After some initial explorations and discussions between the UMLS and GO teams, the GO was integrated with the UMLS. Overall, a total of 23% of the GO terms either matched directly (3%) or linked (20%) to existing UMLS concepts. All GO terms now have a corresponding, official UMLS concept, and the entire vocabulary is available through the web-based UMLS Knowledge Source Server. The mapping of the Gene Ontology, with its focus on structures, processes and functions at the molecular level, to the existing broad coverage UMLS should contribute to linking the language and practices of clinical medicine to the language and practices of genomics.

scholarly journals Improving the Gene Ontology Resource to Facilitate More Informative Analysis and Interpretation of Alzheimer’s Disease Data

Genes ◽  
2018 ◽  
Vol 9 (12) ◽  
pp. 593 ◽  
Author(s):  
Barbara Kramarz ◽  
Paola Roncaglia ◽  
Birgit H. M. Meldal ◽  
Rachael P. Huntley ◽  
Maria J. Martin  ◽  
...  
Keyword(s):  
Gene Ontology ◽  
Amyloid Beta ◽  
Protein Complexes ◽  
Enrichment Analysis ◽  
Ontology Development ◽  
Biological Knowledge ◽  
Gene Products ◽  
Associated Proteins ◽  
Go Terms ◽  
Relevant Gene

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.

scholarly journals HiG2Vec: Hierarchical Representations of Gene Ontology and Genes in the Poincaré Ball

2020 ◽  
Author(s):  
Jaesik Kim ◽  
Dokyoon Kim ◽  
Kyung-Ah Sohn
Keyword(s):  
Gene Ontology ◽  
Euclidean Space ◽  
Biological Knowledge ◽  
Gene Products ◽  
Step Procedure ◽  
The Hierarchical Structure ◽  
Or Gene ◽  
Hierarchical Representations ◽  
Go Terms ◽  
Better Than

AbstractKnowledge manipulation of gene ontology (GO) and gene ontology annotation (GOA) can be done primarily by using vector representation of GO terms and genes for versatile applications such as deep learning. Previous studies have represented GO terms and genes or gene products to measure their semantic similarity using the Word2Vec-based method, which is an embedding method to represent entities as numeric vectors in Euclidean space. However, this method has the limitation that embedding large graph-structured data in the Euclidean space cannot prevent a loss of information of latent hierarchies, thus precluding the semantics of GO and GOA from being captured optimally. In this paper, we propose hierarchical representations of GO and genes (HiG2Vec) that apply Poincaré embedding specialized in the representation of hierarchy through a two-step procedure: GO embedding and gene embedding. Through experiments, we show that our model represents the hierarchical structure better than other approaches and predicts the interaction of genes or gene products similar to or better than previous studies. The results indicate that HiG2Vec is superior to other methods in capturing the GO and gene semantics and in data utilization as well. It can be robustly applied to manipulate various biological knowledge.Availabilityhttps://github.com/JaesikKim/[email protected], [email protected]

scholarly journals Term Matrix: A novel Gene Ontology annotation quality control system based on ontology term co-annotation patterns

2020 ◽  
Author(s):  
Valerie Wood ◽  
Seth Carbon ◽  
Midori A. Harris ◽  
Antonia Lock ◽  
Stacia R. Engel ◽  
...  
Keyword(s):  
Quality Control ◽  
Gene Ontology ◽  
Biological Processes ◽  
Gene Products ◽  
Model Species ◽  
Ontology Term ◽  
Protein Coding ◽  
Ontology Structure ◽  
Exclusive Processes ◽  
Annotation Quality

AbstractBiological processes are accomplished by the coordinated action of gene products. Gene products often participate in multiple processes, and can therefore be annotated to multiple Gene Ontology (GO) terms. Nevertheless, processes that are functionally, temporally, and/or spatially distant may have few gene products in common, and co-annotation to unrelated processes likely reflects errors in literature curation, ontology structure, or automated annotation pipelines. We have developed an annotation quality control workflow that uses rules based on mutually exclusive processes to detect annotation errors, based on and validated by case studies including the three we present here: fission yeast protein-coding gene annotations over time; annotations for cohesin complex subunits in human and model species; and annotations using a selected set of GO biological process terms in human and five model species. For each case study, we reviewed available GO annotations, identified pairs of biological processes which are unlikely to be correctly co-annotated to the same gene products (e.g., amino acid metabolism and cytokinesis), and traced erroneous annotations to their sources. To date we have generated 107 quality control rules, and corrected 289 manual annotations in eukaryotes and over 2.5 million automatically propagated annotations across all taxa.

scholarly journals MicroRNAs: Biological Regulators in Pathogen–Host Interactions

Cells ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 113 ◽  
Author(s):  
Stephanie Maia Acuña ◽  
Lucile Maria Floeter-Winter ◽  
Sandra Marcia Muxel
Keyword(s):  
Immune Response ◽  
Infectious Diseases ◽  
Small Rnas ◽  
Immune Cell ◽  
Fine Tuning ◽  
Biological Processes ◽  
Host Interactions ◽  
The Past ◽  
Biological Aspects

An inflammatory response is essential for combating invading pathogens. Several effector components, as well as immune cell populations, are involved in mounting an immune response, thereby destroying pathogenic organisms such as bacteria, fungi, viruses, and parasites. In the past decade, microRNAs (miRNAs), a group of noncoding small RNAs, have emerged as functionally significant regulatory molecules with the significant capability of fine-tuning biological processes. The important role of miRNAs in inflammation and immune responses is highlighted by studies in which the regulation of miRNAs in the host was shown to be related to infectious diseases and associated with the eradication or susceptibility of the infection. Here, we review the biological aspects of microRNAs, focusing on their roles as regulators of gene expression during pathogen–host interactions and their implications in the immune response against Leishmania, Trypanosoma, Toxoplasma, and Plasmodium infectious diseases.

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