Perspectives on tracking data reuse across biodata resources.

Motivation: Data reuse is a common and vital practice in molecular biology and enables the knowledge gathered over recent decades to drive discovery and innovation in the life sciences. Much of this knowledge has been collated into molecular biology databases, such as UniProtKB, and these resources derive enormous value from sharing data among themselves. However, quantifying and documenting this kind of data reuse remains a challenge. Results: The article reports on a one-day virtual workshop hosted by the UniProt Consortium in March 2023, attended by representatives from biodata resources, experts in data management, and NIH program managers. Workshop discussions focused on strategies for tracking data reuse, best practices for reusing data, and the challenges associated with data reuse and tracking. Surveys and discussions showed that data reuse is widespread, but critical information for reproducibility is sometimes lacking. Challenges include costs of tracking data reuse, tensions between tracking data and open sharing, restrictive licenses, and difficulties in tracking commercial data use. Recommendations that emerged from the discussion include: development of standardized formats for documenting data reuse, education about the obstacles posed by restrictive licenses, and continued recognition by funding agencies that data management is a critical activity that requires dedicated resources. Availability and implementation: Summaries of survey results are available at: https://docs.google.com/forms/d/1j-VU2ifEKb9C-sW6l3ATB79dgHdRk5v_lESv2hawnso/viewanalytics (survey of data providers) and https://docs.google.com/forms/d/18WbJFutUd7qiZoEzbOytFYXSfWFT61hVce0vjvIwIjk/viewanalytics (survey of users).

Lessons learned to boost a bioinformatics knowledge base reusability, the Bgee experience.

BACKGROUND: Enhancing interoperability of bioinformatics knowledge bases is a high-priority requirement to maximize data reusability and thus increase their utility such as the return on investment for biomedical research. A knowledge base may provide useful information for life scientists and other knowledge bases, but it only acquires exchange value once the knowledge base is (re)used, and without interoperability, the utility lies dormant. RESULTS: In this article, we discuss several approaches to boost interoperability depending on the interoperable parts. The findings are driven by several real-world scenario examples that were mostly implemented by Bgee, a well-established gene expression knowledge base. To better justify the findings are transferable, for each Bgee interoperability experience, we also highlight similar implementations by major bioinformatics knowledge bases. Moreover, we discuss ten general main lessons learned. These lessons can be applied in the context of any bioinformatics knowledge base to foster data reusability. CONCLUSIONS: This work provides pragmatic methods and transferable skills to promote reusability of bioinformatics knowledge bases by focusing on interoperability.

The Bgee suite: integrated curated expression atlas and comparative transcriptomics in animals.

Bgee is a database to retrieve and compare gene expression patterns in multiple animal species, produced by integrating multiple data types (RNA-Seq, Affymetrix, in situ hybridization, and EST data). It is based exclusively on curated healthy wild-type expression data (e.g., no gene knock-out, no treatment, no disease), to provide a comparable reference of normal gene expression. Curation includes very large datasets such as GTEx (re-annotation of samples as 'healthy' or not) as well as many small ones. Data are integrated and made comparable between species thanks to consistent data annotation and processing, and to calls of presence/absence of expression, along with expression scores. As a result, Bgee is capable of detecting the conditions of expression of any single gene, accommodating any data type and species. Bgee provides several tools for analyses, allowing, e.g., automated comparisons of gene expression patterns within and between species, retrieval of the prefered conditions of expression of any gene, or enrichment analyses of conditions with expression of sets of genes. Bgee release 14.1 includes 29 animal species, and is available at https://bgee.org/ and through its Bioconductor R package BgeeDB.

OncoMX: A Knowledgebase for Exploring Cancer Biomarkers in the Context of Related Cancer and Healthy Data.

PURPOSE: The purpose of OncoMX1 knowledgebase development was to integrate cancer biomarker and relevant data types into a meta-portal, enabling the research of cancer biomarkers side by side with other pertinent multidimensional data types. METHODS: Cancer mutation, cancer differential expression, cancer expression specificity, healthy gene expression from human and mouse, literature mining for cancer mutation and cancer expression, and biomarker data were integrated, unified by relevant biomedical ontologies, and subjected to rule-based automated quality control before ingestion into the database. RESULTS: OncoMX provides integrated data encompassing more than 1,000 unique biomarker entries (939 from the Early Detection Research Network [EDRN] and 96 from the US Food and Drug Administration) mapped to 20,576 genes that have either mutation or differential expression in cancer. Sentences reporting mutation or differential expression in cancer were extracted from more than 40,000 publications, and healthy gene expression data with samples mapped to organs are available for both human genes and their mouse orthologs. CONCLUSION: OncoMX has prioritized user feedback as a means of guiding development priorities. By mapping to and integrating data from several cancer genomics resources, it is hoped that OncoMX will foster a dynamic engagement between bioinformaticians and cancer biomarker researchers. This engagement should culminate in a community resource that substantially improves the ability and efficiency of exploring cancer biomarker data and related multidimensional data.

BgeeDB, an R package for retrieval of curated expression datasets and for gene list expression localization enrichment tests.

BgeeDB is a collection of functions to import into R re-annotated, quality-controlled and re-processed expression data available in the Bgee database. This includes data from thousands of wild-type healthy samples of multiple animal species, generated with different gene expression technologies (RNA-seq, Affymetrix microarrays, expressed sequence tags, and in situ hybridizations). BgeeDB facilitates downstream analyses, such as gene expression analyses with other Bioconductor packages. Moreover, BgeeDB includes a new gene set enrichment test for preferred localization of expression of genes in anatomical structures ("TopAnat"). Along with the classical Gene Ontology enrichment test, this test provides a complementary way to interpret gene lists. Availability: https://www.bioconductor.org/packages/BgeeDB/.

The Confidence Information Ontology: a step towards a standard for asserting confidence in annotations.

Biocuration has become a cornerstone for analyses in biology, and to meet needs, the amount of annotations has considerably grown in recent years. However, the reliability of these annotations varies; it has thus become necessary to be able to assess the confidence in annotations. Although several resources already provide confidence information about the annotations that they produce, a standard way of providing such information has yet to be defined. This lack of standardization undermines the propagation of knowledge across resources, as well as the credibility of results from high-throughput analyses. Seeded at a workshop during the Biocuration 2012 conference, a working group has been created to address this problem. We present here the elements that were identified as essential for assessing confidence in annotations, as well as a draft ontology--the Confidence Information Ontology--to illustrate how the problems identified could be addressed. We hope that this effort will provide a home for discussing this major issue among the biocuration community. Tracker URL: https://github.com/BgeeDB/confidence-information-ontology Ontology URL: https://raw.githubusercontent.com/BgeeDB/confidence-information-ontology/master/src/ontology/cio-simple.obo

Unification of multi-species vertebrate anatomy ontologies for comparative biology in Uberon.

BACKGROUND: Elucidating disease and developmental dysfunction requires understanding variation in phenotype. Single-species model organism anatomy ontologies (ssAOs) have been established to represent this variation. Multi-species anatomy ontologies (msAOs; vertebrate skeletal, vertebrate homologous, teleost, amphibian AOs) have been developed to represent 'natural' phenotypic variation across species. Our aim has been to integrate ssAOs and msAOs for various purposes, including establishing links between phenotypic variation and candidate genes. RESULTS: Previously, msAOs contained a mixture of unique and overlapping content. This hampered integration and coordination due to the need to maintain cross-references or inter-ontology equivalence axioms to the ssAOs, or to perform large-scale obsolescence and modular import. Here we present the unification of anatomy ontologies into Uberon, a single ontology resource that enables interoperability among disparate data and research groups. As a consequence, independent development of TAO, VSAO, AAO, and vHOG has been discontinued. CONCLUSIONS: The newly broadened Uberon ontology is a unified cross-taxon resource for metazoans (animals) that has been substantially expanded to include a broad diversity of vertebrate anatomical structures, permitting reasoning across anatomical variation in extinct and extant taxa. Uberon is a core resource that supports single- and cross-species queries for candidate genes using annotations for phenotypes from the systematics, biodiversity, medical, and model organism communities, while also providing entities for logical definitions in the Cell and Gene Ontologies. THE ONTOLOGY RELEASE FILES ASSOCIATED WITH THE ONTOLOGY MERGE DESCRIBED IN THIS MANUSCRIPT ARE AVAILABLE AT: http://purl.obolibrary.org/obo/uberon/releases/2013-02-21/ CURRENT ONTOLOGY RELEASE FILES ARE AVAILABLE ALWAYS AVAILABLE AT: http://purl.obolibrary.org/obo/uberon/releases/

Fifteen years SIB Swiss Institute of Bioinformatics: life science databases, tools and support.

The SIB Swiss Institute of Bioinformatics (www.isb-sib.ch) was created in 1998 as an institution to foster excellence in bioinformatics. It is renowned worldwide for its databases and software tools, such as UniProtKB/Swiss-Prot, PROSITE, SWISS-MODEL, STRING, etc, that are all accessible on ExPASy.org, SIB's Bioinformatics Resource Portal. This article provides an overview of the scientific and training resources SIB has consistently been offering to the life science community for more than 15 years.

Uncovering hidden duplicated content in public transcriptomics data.

As part of the development of the database Bgee (a dataBase for Gene Expression Evolution), we annotate and analyse expression data from different types and different sources, notably Affymetrix data from GEO and ArrayExpress, and RNA-Seq data from SRA. During our quality control procedure, we have identified duplicated content in GEO and ArrayExpress, affecting ∼14% of our data: fully or partially duplicated experiments from independent data submissions, Affymetrix chips reused in several experiments, or reused within an experiment. We present here the procedure that we have established to filter such duplicates from Affymetrix data, and our procedure to identify future potential duplicates in RNA-Seq data.

Recent advances in biocuration: meeting report from the Fifth International Biocuration Conference.

The 5th International Biocuration Conference brought together over 300 scientists to exchange on their work, as well as discuss issues relevant to the International Society for Biocuration's (ISB) mission. Recurring themes this year included the creation and promotion of gold standards, the need for more ontologies, and more formal interactions with journals. The conference is an essential part of the ISB's goal to support exchanges among members of the biocuration community. Next year's conference will be held in Cambridge, UK, from 7 to 10 April 2013. In the meanwhile, the ISB website provides information about the society's activities (http://biocurator.org), as well as related events of interest.

vHOG, a multispecies vertebrate ontology of homologous organs groups.

MOTIVATION: Most anatomical ontologies are species-specific, whereas a framework for comparative studies is needed. We describe the vertebrate Homologous Organs Groups ontology, vHOG, used to compare expression patterns between species. RESULTS: vHOG is a multispecies anatomical ontology for the vertebrate lineage. It is based on the HOGs used in the Bgee database of gene expression evolution. vHOG version 1.4 includes 1184 terms, follows OBO principles and is based on the Common Anatomy Reference Ontology (CARO). vHOG only describes structures with historical homology relations between model vertebrate species. The mapping to species-specific anatomical ontologies is provided as a separate file, so that no homology hypothesis is stated within the ontology itself. Each mapping has been manually reviewed, and we provide support codes and references when available. AVAILABILITY AND IMPLEMENTATION: vHOG is available from the Bgee download site (http://bgee.unil.ch/), as well as from the OBO Foundry and the NCBO Bioportal websites. CONTACT: bgee@isb-sib.ch; frederic.bastian@unil.ch.

Homolonto: generating homology relationships by pairwise alignment of ontologies and application to vertebrate anatomy.

MOTIVATION: The anatomy of model species is described in ontologies, which are used to standardize the annotations of experimental data, such as gene expression patterns. To compare such data between species, we need to establish relations between ontologies describing different species. RESULTS: We present a new algorithm, and its implementation in the software Homolonto, to create new relationships between anatomical ontologies, based on the homology concept. Homolonto uses a supervised ontology alignment approach. Several alignments can be merged, forming homology groups. We also present an algorithm to generate relationships between these homology groups. This has been used to build a multi-species ontology, for the database of gene expression evolution Bgee. AVAILABILITY: download section of the Bgee website http://bgee.unil.ch/