CvManGO, a method for leveraging computational predictions to improve literature-based Gene Ontology annotations

BackgroundA large volume of data and information about genes and gene products has been stored in various molecular biology databases. A major challenge for knowledge discovery using these databases is to identify related genes and gene products in disparate databases. The development of Gene Ontology (GO) as a common vocabulary for annotation allows integrated queries across multiple databases and identification of semantically related genes and gene products (i.e., genes and gene products that have similar GO annotations). Meanwhile, dozens of tools have been developed for browsing, mining or editing GO terms, their hierarchical relationships, or their "associated" genes and gene products (i.e., genes and gene products annotated with GO terms). Tools that allow users to directly search and inspect relations among all GO terms and their associated genes and gene products from multiple databases are needed.ResultsWe present a standalone package called DynGO, which provides several advanced functionalities in addition to the standard browsing capability of the official GO browsing tool (AmiGO). DynGO allows users to conduct batch retrieval of GO annotations for a list of genes and gene products, and semantic retrieval of genes and gene products sharing similar GO annotations. The result are shown in an association tree organized according to GO hierarchies and supported with many dynamic display options such as sorting tree nodes or changing orientation of the tree. For GO curators and frequent GO users, DynGO provides fast and convenient access to GO annotation data. DynGO is generally applicable to any data set where the records are annotated with GO terms, as illustrated by two examples.ConclusionWe have presented a standalone package DynGO that provides functionalities to search and browse GO and its association databases as well as several additional functions such as batch retrieval and semantic retrieval. The complete documentation and software are freely available for download from the website .

BackgroundAutomatic functional annotation of proteins is an open research problem in bioinformatics. The growing number of protein entries in public databases, for example in UniProtKB, poses challenges in manual functional annotation. Manual annotation requires expert human curators to search and read related research articles, interpret the results, and assign the annotations to the proteins. Thus, it is a time-consuming and expensive process. Therefore, designing computational tools to perform automatic annotation leveraging the high quality manual annotations that already exist in UniProtKB/SwissProt is an important research problemResultsIn this paper, we extend and adapt the GrAPFI (graph-based automatic protein function inference) (Sarker et al. in BMC Bioinform 21, 2020; Sarker et al., in: Proceedings of 7th international conference on complex networks and their applications, Cambridge, 2018) method for automatic annotation of proteins with gene ontology (GO) terms renaming it as GrAPFI-GO. The original GrAPFI method uses label propagation in a similarity graph where proteins are linked through the domains, families, and superfamilies that they share. Here, we also explore various types of similarity measures based on common neighbors in the graph. Moreover, GO terms are arranged in a hierarchical manner according to semantic parent–child relations. Therefore, we propose an efficient pruning and post-processing technique that integrates both semantic similarity and hierarchical relations between the GO terms. We produce experimental results comparing the GrAPFI-GO method with and without considering common neighbors similarity. We also test the performance of GrAPFI-GO and other annotation tools for GO annotation on a benchmark of proteins with and without the proposed pruning and post-processing procedure.ConclusionOur results show that the proposed semantic hierarchical post-processing potentially improves the performance of GrAPFI-GO and of other annotation tools as well. Thus, GrAPFI-GO exposes an original efficient and reusable procedure, to exploit the semantic relations among the GO terms in order to improve the automatic annotation of protein functions

BackgroundAnnotation of proteins with gene ontology (GO) terms is ongoing work and a complex task. Manual GO annotation is precise and precious, but it is time-consuming. Therefore, instead of curated annotations most of the proteins come with uncurated annotations, which have been generated automatically. Text-mining systems that use literature for automatic annotation have been proposed but they do not satisfy the high quality expectations of curators.ResultsIn this paper we describe an approach that links uncurated annotations to text extracted from literature. The selection of the text is based on the similarity of the text to the term from the uncurated annotation. Besides substantiating the uncurated annotations, the extracted texts also lead to novel annotations. In addition, the approach uses the GO hierarchy to achieve high precision. Our approach is integrated into GOAnnotator, a tool that assists the curation process for GO annotation of UniProt proteins.ConclusionThe GO curators assessed GOAnnotator with a set of 66 distinct UniProt/SwissProt proteins with uncurated annotations. GOAnnotator provided correct evidence text at 93% precision. This high precision results from using the GO hierarchy to only select GO terms similar to GO terms from uncurated annotations in GOA. Our approach is the first one to achieve high precision, which is crucial for the efficient support of GO curators. GOAnnotator was implemented as a web tool that is freely available at .

The number of nucleotide and protein sequences deposited in public databases has rapidly increased in recent times. To provide consistent annotation of orthologous genes across organisms, annotation using Gene Ontology (GO) terms is shared among most of these databases. GO outlines a structured vocabulary used for describing biological properties of gene products. The illustration of GO terms using complete paths to the root term on the basis of a directed acyclic graph (DAG) approach aids in the systematic grasping of the functional information related to a gene product. However, the website provided by the GO Consortium does not present DAGs for two or more GO terms due to difficulties in the depiction of the complex relationships of such GO terms. To overcome these problems, we have constructed a DAG-based browser, termed DAGViz, that shows DAG-based information of multiple GO terms assigned to one or more genes within a single screen (http://www.pgb.kazusa.or.jp/dagviz/). In the current report, we illustrate the advantages of DAGViz in analyzing GO annotation.

The Gene Ontology (GO) is a framework designed to represent biological knowledge about gene products' biological roles and the cellular location in which they act. Biocuration is a complex process: the body of scientific literature is large and selection of appropriate GO terms can be challenging. Both these issues are compounded by the fact that our understanding of biology is still incomplete; hence it is important to appreciate that GO is inherently an evolving model. In this chapter, we describe how biocurators create GO annotations from experimental findings from research articles. We describe the current best practices for high-quality literature curation and how GO curators succeed in modeling biology using a relatively simple framework. We also highlight a number of difficulties when translating experimental assays into GO annotations.

A race through the maze of genomic evidence

Gene Ontology (GO) is a universal resource for analyses and interpretation of high-throughput biological datasets. GO is developed and curated by several different groups, based at scientific institutions around the world, working together under the auspices of the GO Consortium. GO annotations capture biological functional knowledge by associating gene products with GO terms. GO term and gene product records all have computer-readable accession numbers; therefore, these annotations can be easily used for analyses of large datasets while retaining human-readable labels. The UCL Functional Gene Annotation group focuses on GO annotation of human gene products. Our group has led initiatives to systematically annotate proteins and microRNAs across specific biomedical fields, and our current biocuration effort, funded by the Alzheimer's Research UK foundation, is focused on dementia and Alzheimer's disease. Our group has also contributed to the development and revision of the ontology describing neurological domains of biology. Here we present an overview of GO and explain how our work, as well as the work of other members of the GO Consortium, is improving the neurological domains of the GO resource. These biocuration efforts will benefit the dementia and Alzheimer's research community by rendering GO more suitable for analyses of neurological datasets.

BackgroundFamilies of related proteins and their different functions may be described systematically using common classifications and ontologies such as Pfam and GO (Gene Ontology), for example. However, many proteins consist of multiple domains, and each domain, or some combination of domains, can be responsible for a particular molecular function. Therefore, identifying which domains should be associated with a specific function is a non-trivial task.ResultsWe describe a general approach for the computational discovery of associations between different sets of annotations by formalising the problem as a bipartite graph enrichment problem in the setting of a tripartite graph. We call this approach “CODAC” (for COmputational Discovery of Direct Associations using Common Neighbours). As one application of this approach, we describe “GODomainMiner” for associating GO terms with protein domains. We used GODomainMiner to predict GO-domain associations between each of the 3 GO ontology namespaces (MF, BP, and CC) and the Pfam, CATH, and SCOP domain classifications. Overall, GODomainMiner yields average enrichments of 15-, 41- and 25-fold GO-domain associations compared to the existing GO annotations in these 3 domain classifications, respectively.ConclusionsThese associations could potentially be used to annotate many of the protein chains in the Protein Databank and protein sequences in UniProt whose domain composition is known but which currently lack GO annotation.

The study of protein–protein interaction and the determination of protein functions are important parts of proteomics. Computational methods are used to study the similarity between proteins based on Gene Ontology (GO) to explore their functions and possible interactions. GO is a series of standardized terms that describe gene products from molecular functions, biological processes, and cell components. Previous studies on assessing the similarity of GO terms were primarily based on Information Content (IC) between GO terms to measure the similarity of proteins. However, these methods tend to ignore the structural information between GO terms. Therefore, considering the structural information of GO terms, we systematically analyze the performance of the GO graph and GO Annotation (GOA) graph in calculating the similarity of proteins using different graph embedding methods. When applied to the actual Human and Yeast datasets, the feature vectors of GO terms and proteins are learned based on different graph embedding methods. To measure the similarity of the proteins annotated by different GO numbers, we used Dynamic Time Warping (DTW) and cosine to calculate protein similarity in GO graph and GOA graph, respectively. Link prediction experiments were then performed to evaluate the reliability of protein similarity networks constructed by different methods. It is shown that graph embedding methods have obvious advantages over the traditional IC-based methods. We found that random walk graph embedding methods, in particular, showed excellent performance in calculating the similarity of proteins. By comparing link prediction experiment results from GO(DTW) and GOA(cosine) methods, it is shown that GO(DTW) features provide highly effective information for analyzing the similarity among proteins.

The Gene Ontology (GO) provides a framework to systematically classify and annotate gene function. The annotations associated with GO play a critical role in modern biology and cover many organisms. For the human genome, over 10,000 GO terms are used to annotate gene function in an expansive database of over 200,000 annotations. Due to the importance of the GO annotations in modern biology, significant effort has been put into assessing the quality of the annotations. Providing measures of annotation completeness, accuracy, and precision is critical if researchers are to use the annotations in real-world applications with confidence. Here, we describe a task-based approach that examines the completeness and utility of GO annotations through the lens of gene enrichment analysis. Our approach can be used to model the progression of the GO annotations over time, either for a particular area of interest or for the body of annotations as a whole. Using this framework, we conducted a large-scale analysis of gene expression datasets from the NCBI Gene Expression Omnibus (GEO). In particular, we identified terms of interest for each dataset through semantic annotation of biomedical data, then tracked the behavior of these terms as a function of time. The preliminary results provide significant information about the progress and character of GO annotations over time. This framework is flexible enough to examine all or part of the GO annotations, across multiple species, and with various enrichment methods. We also discuss how this framework can be used to evaluate different annotation methods. For example, by comparing the performance of annotations generated with a particular method to the performance of canonical annotations, it is possible to determine their relative quality.

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.

Introduction. Ontologies as knowledgebases have been heavily applied in computational biological studies by implementing into ML models for purposes such as disease-gene associations identification. Method. We conduct a case study using gene ontology (GO) annotation data and three ML models to replicate the prediction of autism spectrum disorder (ASD)-causing genes. Analysis. Data were collected from GO and Simmons Foundation Autism Research Initiative (SFARI). The semantic similarities between GO annotation terms on gene products were calculated. Results. The best-performing model can reach an AUC of .85, which means using GO annotation data for ASD disease-gene prediction can receive a significantly accurate result. However, we stress the importance of constructing knowledgebases in adapting to LLMs and the role of LIS professionals in curating community knowledge for interoperability and reuse. Conclusions. Biomedical ontologies play a crucial role in the discovery of biomedical knowledge. Knowledge organization and computer science domains require more communication and synchronization in the face of emerging AI and ML technologies.

The standard method for comparing gene products (proteins or RNA) is to compare their DNA or amino acid sequences. Additional information about some gene products may come from multiple sources, including the set of Gene Ontology (GO) annotations and the set of journal abstracts related to each gene product. Gene product similarity measures can be based on evaluating sets of descriptor terms found in the GO taxonomy, and/or the index term sets of the related documents (MeSH annotations). While our techniques can be applied to term sets from any taxonomy, we restrict our examples in this article to GO annotations. We investigate the use of linear order statistics (LOS) to build similarity relations on pairs of terms that are used in the GO as linguistic descriptors of genes and gene products. One of our objectives is to investigate the construction and utility of visual assessments of relational data (in this case, dissimilarity matrices) for discovering tendencies of groups of gene products to "cluster together". We use gene product data derived from a group of 194 gene products representing three protein families extracted from ENSEMBL. Our examples suggest that LOS similarity measures are more effective than traditional sequence-based similarity measures at capturing relationships between pairs of gene products in ENSEMBL families when annotation information is available. We show examples of how these similarity measures can assist in knowledge discovery and gene product family validation.

To increase the utility of Gene Ontology (GO) annotations for interpretation of genome-wide experimental data, we have developed GO-CAM, a structured framework for linking multiple GO annotations into an integrated model of a biological system. We expect that GO-CAM will enable new applications in pathway and network analysis, as well as improve standard GO annotations for traditional GO-based applications.

FlyBase (http://flybase.org) is a database of Drosophila genetic and genomic information. Gene Ontology (GO) terms are used to describe three attributes of wild-type gene products: their molecular function, the biological processes in which they play a role, and their subcellular location. This article describes recent changes to the FlyBase GO annotation strategy that are improving the quality of the GO annotation data. Many of these changes stem from our participation in the GO Reference Genome Annotation Project—a multi-database collaboration producing comprehensive GO annotation sets for 12 diverse species.