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The Gene Ontology (GO) knowledgebase (http://geneontology.org) is a comprehensive resource concerning the functions of genes and gene products (proteins and noncoding RNAs). GO annotations cover genes from organisms across the tree of life as well as viruses, though most gene function knowledge currently derives from experiments carried out in a relatively small number of model organisms. Here, we provide an updated overview of the GO knowledgebase, as well as the efforts of the broad, international consortium of scientists that develops, maintains, and updates the GO knowledgebase. The GO knowledgebase consists of three components: (1) the GO—a computational knowledge structure describing the functional characteristics of genes; (2) GO annotations—evidence-supported statements asserting that a specific gene product has a particular functional characteristic; and (3) GO Causal Activity Models (GO-CAMs)—mechanistic models of molecular “pathways” (GO biological processes) created by linking multiple GO annotations using defined relations. Each of these components is continually expanded, revised, and updated in response to newly published discoveries and receives extensive QA checks, reviews, and user feedback. For each of these components, we provide a description of the current contents, recent developments to keep the knowledgebase up to date with new discoveries, and guidance on how users can best make use of the data that we provide. We conclude with future directions for the project.

The Gene Ontology (GO) knowledgebase (http://geneontology.org) is a comprehensive resource concerning the functions of genes and gene products (proteins and noncoding RNAs). GO annotations cover genes from organisms across the tree of life as well as viruses, though most gene function knowledge currently derives from experiments carried out in a relatively small number of model organisms. Here, we provide an updated overview of the GO knowledgebase, as well as the efforts of the broad, international consortium of scientists that develops, maintains, and updates the GO knowledgebase. The GO knowledgebase consists of three components: (1) the GO-a computational knowledge structure describing the functional characteristics of genes; (2) GO annotations-evidence-supported statements asserting that a specific gene product has a particular functional characteristic; and (3) GO Causal Activity Models (GO-CAMs)-mechanistic models of molecular "pathways" (GO biological processes) created by linking multiple GO annotations using defined relations. Each of these components is continually expanded, revised, and updated in response to newly published discoveries and receives extensive QA checks, reviews, and user feedback. For each of these components, we provide a description of the current contents, recent developments to keep the knowledgebase up to date with new discoveries, and guidance on how users can best make use of the data that we provide. We conclude with future directions for the project.

The Orthology Benchmark Service (https://orthology.benchmarkservice.org) is the gold standard for orthology inference evaluation, supported and maintained by the Quest for Orthologs consortium. It is an essential resource to compare existing and new methods of orthology inference (the bedrock for many comparative genomics and phylogenetic analysis) over a standard dataset and through common procedures. The Quest for Orthologs Consortium is dedicated to maintaining the resource up to date, through regular updates of the Reference Proteomes and increasingly accessible data through the OpenEBench platform. For this update, we have added a new benchmark based on curated orthology assertion from the Vertebrate Gene Nomenclature Committee, and provided an example meta-analysis of the public predictions present on the platform.

Achieving high accuracy in orthology inference is essential for many comparative, evolutionary and functional genomic analyses, yet the true evolutionary history of genes is generally unknown and orthologs are used for very different applications across phyla, requiring different precision-recall trade-offs. As a result, it is difficult to assess the performance of orthology inference methods. Here, we present a community effort to establish standards and an automated web-based service to facilitate orthology benchmarking. Using this service, we characterize 15 well-established inference methods and resources on a battery of 20 different benchmarks. Standardized benchmarking provides a way for users to identify the most effective methods for the problem at hand, sets a minimum requirement for new tools and resources, and guides the development of more accurate orthology inference methods.

A document containing a subset of CAFA2 analyses that are equivalent to those provided about the CAFA1 experiment in the CAFA1 supplement. (PDF 11100 kb)

Life science data is evolving to be ever larger, more distributed, and more natively web-based. However, our collective handling of identifiers has lagged behind these advances. Diverse identifier issues (for instance “link rot” and “content drift”) have hampered our ability to integrate data and derive new knowledge from it. Optimizing web-based identifiers is harder than it appears and no single scheme is perfect: Identifiers are reused in different ways for different reasons, by different consumers. Moreover, digital entities (e.g., files), physical entities (e.g., biosamples), and descriptive entities (e.g., ‘mitosis’) have different requirements for identifiers. Nevertheless, there is substantial room for improvement throughout the life sciences and several other groups have been converging on identifier standards that are broadly applicable. Building on these efforts and drawing on our experience, we focus on the use case of large-scale data integration: we outline the identifier qualities and best practices that we feel are most important in this context. Specifically, we propose actions that providers of online databases (repositories, registries, and knowledgebases) should take when designing new identifiers or maintaining existing ones (Rules 1-9). In Rule 10, we conclude with guidance to data integrators and redistributors on how best to reference identifiers from these diverse sources. This article may also be useful to data generators and end users as it offers insight into the issues associated with data provision in a web environment. We call upon data providers to take a long-term view of their entities’ scope and lifecycle, and to consider existing identifier platforms and services. Rule 1. Use established identifiers Rule 2. Design identifiers for use by others Rule 3. Help local identifiers travel well: document Prefix and Namespace Rule 4. Opt for simple durable web resolution Rule 5. Avoid embedding meaning Rule 6. Make URIs clear and findable Rule 7. Implement a version management policy Rule 8. Do not re-assign or delete identifiers Rule 9. Document the identifiers you issue and use Rule 10. Reference responsibly

In the life sciences, problems with identifiers impede the flow and integrity of information. This is especially challenging within “synthesis research” disciplines such as systems biology, translational medicine, and ecology. Implementation-driven initiatives such as ELIXIR, BD2K, and others have therefore been actively working to understand and address underlying problems with identifiers. Good, global-scale, persistent identifier design is harder than it appears, and is essential for data to be Findable, Accessible, Interoperable, and Reusable (Data FAIRport principles). Here, we build on emerging conventions and existing general recommendations and summarise the identifier characteristics most important to optimising the utility of life-science data. We propose actions to take in the identifier ‘green field’ and offer guidance for using real-world identifiers from diverse sources.

Prior to the “genomic era,” when the acquisition of DNA sequence involved significant labor and expense, the sequencing of genes was strongly linked to the experimental characterization of their products. Sequencing at that time directly resulted from the need to understand an experimentally determined phenotype or biochemical activity. Now that DNA sequencing has become orders of magnitude faster and less expensive, focus has shifted to sequencing entire genomes. Since biochemistry and genetics have not, by and large, enjoyed the same improvement of scale, public sequence repositories now predominantly contain putative protein sequences for which there is no direct experimental evidence of function. Computational approaches attempt to leverage evidence associated with the ever-smaller fraction of experimentally analyzed proteins to predict function for these putative proteins. Maximizing our understanding of function over the universe of proteins in toto requires not only robust computational methods of inference but also a judicious allocation of experimental resources, focusing on proteins whose experimental characterization will maximize the number and accuracy of follow-on predictions. COMBREX (COMputational BRidges to EXperiments, http://combrex.bu.edu) is an NIH-funded enterprise that has brought computational and experimental biologists together, with the goal of greatly improving our overall understanding of microbial protein function [1],[2]. Since its inception, it has made significant progress toward the following goals: identifying the minority of proteins that have already been experimentally characterized, serving as a public repository of novel protein function predictions made by diverse methods, producing a clear chain of evidence from experiment to prediction, identifying (“recommending”) those functional predictions whose verification will contribute most to our overall understanding of protein function, and actually funding the experiments to test function. The recommendation system is a proof of concept based on active learning principles and includes, for a given protein, criteria including phylogenetic distribution of its protein family, biological and clinical phenotypes associated with it, the availability of protein structure data, and its sequence distance from experimentally determined proteins or from the other proteins in its family. COMBREX comprises several interrelated efforts. First, the project is building a community of researchers (the COMBREX Community) committed to achieving the goals above. Second, the project maintains a web-accessible database (the COMBREX Database) of known and predicted functions for microbial proteins. The database search features enable biologists to identify predictions whose experimental verification is particularly important. Finally, the project issues small monetary awards (COMBREX grants) to biologists to fund the experimental testing of such predictions. In this paper, we provide a brief review of COMBREX, focusing on its overall design, its computational resources, and the experimental results from the first phase of the project.

In the life sciences, problems with identifiers impede the flow and integrity of information. This is especially challenging within “synthesis research” disciplines such as systems biology, translational medicine, and ecology. Implementation-driven initiatives such as ELIXIR, BD2K, and others have therefore been actively working to understand and address underlying problems with identifiers. Good, global-scale, persistent identifier design is harder than it appears, and is essential for data to be Findable, Accessible, Interoperable, and Reusable (Data FAIRport principles). Here, we build on emerging conventions and existing general recommendations and summarise the identifier characteristics most important to optimising the utility of life-science data. We propose actions to take in the identifier ‘green field’ and offer guidance for using real-world identifiers from diverse sources., ORCIDs corresponding to the authors are: http://orcid.org/0000-0002-9353-5498 http://orcid.org/0000-0003-4155-5910 http://orcid.org/0000-0002-2513-5396 http://orcid.org/0000-0002-1010-3121 http://orcid.org/0000-0003-4727-9435 http://orcid.org/0000-0002-9091-5938 http://orcid.org/0000-0003-3499-8262 http://orcid.org/0000-0001-9823-1621 http://orcid.org/0000-0002-3469-4923 http://orcid.org/0000-0001-9114-8737 http://orcid.org/0000-0001-8479-0262 http://orcid.org/0000-0001-6867-9425 http://orcid.org/0000-0001-6666-1520 http://orcid.org/0000-0001-5404-7670 http://orcid.org/0000-0002-0643-3144 http://orcid.org/0000-0002-2036-8350 http://orcid.org/0000-0002-4625-743X http://orcid.org/0000-0002-6309-7327 http://orcid.org/0000-0002-1615-2899 http://orcid.org/0000-0001-5454-2815 http://orcid.org/0000-0002-1611-6935 http://orcid.org/0000-0002-9533-5684 http://orcid.org/0000-0002-1034-5171 http://orcid.org/0000-0002-4980-3512 http://orcid.org/0000-0002-6601-2165 http://orcid.org/0000-0001-9853-5668 http://orcid.org/0000-0001-5306-5690 http://orcid.org/0000-0002-5595-689X http://orcid.org/0000-0002-0405-8854 http://orcid.org/0000-0003-4958-0184 http://orcid.org/0000-0001-7020-1236 http://orcid.org/0000-0001-8936-9143 http://orcid.org/0000-0003-3156-2105 http://orcid.org/0000-0002-1279-5133 http://orcid.org/0000-0003-1219-2137 http://orcid.org/0000-0003-3035-4195