Amos Bairoch

Background: In recent decades, several life science resources have structured data using the same framework and made these accessible using the same query language to facilitate interoperability. Knowledge graphs have seen increased adoption in bioinformatics due to their advantages for representing data in a generic graph format. For example, yummydata.org catalogs more than 60 knowledge graphs accessible through SPARQL, a technical query language. Although SPARQL allows powerful, expressive queries, even across physically distributed knowledge graphs, formulating such queries is a challenge for most users. Therefore, to guide users in retrieving the relevant data, many of these resources provide representative examples. These examples can also be an important source of information for machine learning (for example, machine-learning algorithms for translating natural language questions to SPARQL), if a sufficiently large number of examples are provided and published in a common, machine-readable, and standardized format across different resources. Findings: We introduce a large collection of human-written natural language questions and their corresponding SPARQL queries over federated bioinformatics knowledge graphs (KGs) collected for several years across different research groups at the SIB Swiss Institute of Bioinformatics. The collection comprises more than 1,000 example questions and queries, including almost 100 federated queries. We propose a methodology to uniformly represent the examples with minimal metadata, based on existing standards. Furthermore, we introduce an extensive set of open-source applications, including query graph visualizations and smart query editors, easily reusable by KG maintainers who adopt the proposed methodology. Conclusions: We encourage the community to adopt and extend the proposed methodology, towards richer KG metadata and improved Semantic Web services. URL: https://github.com/sib-swiss/sparql-examples.

The SIB Swiss Institute of Bioinformatics (https://www.sib.swiss/) is a federation of bioinformatics research and service groups. The international life science community in academia and industry has been accessing the freely available databases provided by SIB since its inception in 1998. In this paper we present the 11 databases which currently offer semantically enriched data in accordance with the FAIR principles (Findable, Accessible, Interoperable, Reusable), as well as the Swiss Personalized Health Network initiative (SPHN) which also employs this enrichment. The semantic enrichment facilitates the manipulation of large data sets from public databases and private data sets. Examples are provided to illustrate that the data from the SIB databases can not only be queried using precise criteria individually, but also across multiple databases, including a variety of non-SIB databases. Data manipulation, be it exploration, extraction, annotation, combination, and publication, is possible using the SPARQL query language. Providing documentation, tutorials and sample queries makes it easier to navigate this web of semantic data. Through this paper, the reader will discover how the existing SIB knowledge graphs can be leveraged to tackle the complex biological or clinical questions that are being addressed today.

Summary: The Feature-Viewer is a lightweight library for the visualization of biological data mapped to a protein or nucleotide sequence. It is designed for ease of use while allowing for a full customization. The library is already used by several biological data resources and allows intuitive visual mapping of a full spectra of sequence features for different usages. Availability and implementation: The Feature-Viewer is open source, compatible with state-of-the-art development technologies and responsive, also for mobile viewing. Documentation and usage examples are available online.

The neXtProt knowledgebase (https://www.nextprot.org) is an integrative resource providing both data on human protein and the tools to explore these. In order to provide comprehensive and up-to-date data, we evaluate and add new data sets. We describe the incorporation of three new data sets that provide expression, function, protein-protein binary interaction, post-translational modifications (PTM) and variant information. New SPARQL query examples illustrating uses of the new data were added. neXtProt has continued to develop tools for proteomics. We have improved the peptide uniqueness checker and have implemented a new protein digestion tool. Together, these tools make it possible to determine which proteases can be used to identify trypsin-resistant proteins by mass spectrometry. In terms of usability, we have finished revamping our web interface and completely rewritten our API. Our SPARQL endpoint now supports federated queries. All the neXtProt data are available via our user interface, API, SPARQL endpoint and FTP site, including the new PEFF 1.0 format files. Finally, the data on our FTP site is now CC BY 4.0 to promote its reuse.

Despite an increased awareness of the problematic of cell line cross‐contamination and misidentification, it remains nowadays a major source of erroneous experimental results in biomedical research. To prevent it, researchers are expected to frequently test the authenticity of the cell lines they are working on. STR profiling was selected as the international reference method to perform cell line authentication. While the experimental protocols and manipulations for generating a STR profile are well described, the available tools and workflows to analyze such data are lacking. The Cellosaurus knowledge resource aimed to improve the situation by compiling all the publicly available STR profiles from the literature and other databases. As a result, it grew to become the largest database in terms of human STR profiles, with 6,474 distinct cell lines having an associated STR profile (release July 31, 2019). Here we present CLASTR, the Cellosaurus STR similarity search tool enabling users to compare one or more STR profiles with those available in the Cellosaurus cell line knowledge resource. It aims to help researchers in the process of cell line authentication by providing numerous functionalities. The tool is publicly accessible on the SIB ExPASy server (https://web.expasy.org/cellosaurus-str-search) and its source code is available on GitHub under the GPL‐3.0 license.

The ABCD (for AntiBodies Chemically Defined) database is a repository of sequenced antibodies, integrating curated information about the antibody and its antigen with cross-links to standardized databases of chemical and protein entities. It is freely available to the academic community, accessible through the ExPASy server (https://web.expasy.org/abcd/). The ABCD database aims at helping to improve reproducibility in academic research by providing a unique, unambiguous identifier associated to each antibody sequence. It also allows to determine rapidly if a sequenced antibody is available for a given antigen.

This micro-review is intended to provide an up to date list of the cell lines that have flown in space, starting with HeLa in 1960. We coin a new term, cellonaut for cell lines that have been sent in earth orbit. As of June 2019, we were able to identify 52 different celllonauts. Cell lines that are cellonauts are annotated as such in the Cellosaurus.

Research in toxicology relies on in vitro models such as cell lines. These living models are prone to change and may be described in publications with insufficient information or quality control testing. This article sets out recommendations to improve the reliability of cell-based research.

The use of misidentified and contaminated cell lines continues to be a problem in biomedical research. Research Resource Identifiers (RRIDs) should reduce the prevalence of misidentified and contaminated cell lines in the literature by alerting researchers to cell lines that are on the list of problematic cell lines, which is maintained by the International Cell Line Authentication Committee (ICLAC) and the Cellosaurus database. To test this assertion, we text-mined the methods sections of about two million papers in PubMed Central, identifying 305,161 unique cell-line names in 150,459 articles. We estimate that 8.6% of these cell lines were on the list of problematic cell lines, whereas only 3.3% of the cell lines in the 634 papers that included RRIDs were on the problematic list. This suggests that the use of RRIDs is associated with a lower reported use of problematic cell lines.