WikiGenomes: an open web application for community consumption and curation of gene annotation data in Wikidata

Paying it forward: Crowdsourcing the harmonisation and linking of taxon names and biodiversity identifiers

Linking records for the same taxa between different databases is an essential step when working with biodiversity data. However, name-matching alone is error-prone, because of issues such as homonyms (unrelated taxa with the same name) and synonyms (same taxon under different names). Therefore, most projects will require some curation to ensure that taxon identifiers are correctly linked. Unfortunately, formal guidance on such curation is uncommon and these steps are often ad hoc and poorly documented, which hinders transparency and reproducibility, yet the task requires specialist knowledge and cannot be easily automated without careful validation. Here, we present a case study on linking identifiers between the GBIF and NCBI taxonomies for a species checklist. This represents a common scenario: finding published sequence data (from NCBI) for species chosen by occurrence or geographical distribution (from GBIF). Wikidata, a publicly editable knowledge base of structured data, can serve as an additional information source for identifier linking. We suggest a software toolkit for taxon name-matching and data-cleaning, describe common issues encountered during curation and propose concrete steps to address them. For example, about 2.8% of the taxa in our dataset had wrong identifiers linked on Wikidata because of errors in name-matching caused by homonyms. By correcting such errors during data-cleaning, either directly (through editing Wikidata) or indirectly (by reporting errors in GBIF or NCBI), we crowdsource the curation and contribute to community resources, thereby improving the quality of downstream analyses.

Investigating the potential of the semantic web for education: Exploring Wikidata as a learning platform

Wikidata is a free, multilingual, open knowledge-base that stores structured, linked data. It has grown rapidly and as of December 2022 contains over 100 million items and millions of statements, making it the largest semantic knowledge-base in existence. Changing the interaction between people and knowledge, Wikidata offers various learning opportunities, leading to new applications in sciences, technology and cultures. These learning opportunities stem in part from the ability to query this data and ask questions that were difficult to answer in the past. They also stem from the ability to visualize query results, for example on a timeline or a map, which, in turn, helps users make sense of the data and draw additional insights from it. Research on the semantic web as learning platform and on Wikidata in the context of education is almost non-existent, and we are just beginning to understand how to utilize it for educational purposes. This research investigates the Semantic Web as a learning platform, focusing on Wikidata as a prime example. To that end, a methodology of multiple case studies was adopted, demonstrating Wikidata uses by early adopters. Seven semi-structured, in-depth interviews were conducted, out of which 10 distinct projects were extracted. A thematic analysis approach was deployed, revealing eight main uses, as well as benefits and challenges to engaging with the platform. The results shed light on Wikidata's potential as a lifelong learning process, enabling opportunities for improved Data Literacy and a worldwide social impact.

Inserción de metadatos de las bibliotecas españolas en Wikidata: un modelo de datos abiertos enlazados

El término datos abiertos enlazados se ha hecho muy habitual en el ámbito de la biblioteconomía en los últimos años. Esto es debido al uso que se puede hacer de los metadatos con los que cuentan las bibliotecas. Normalmente se habla de liberar esta información que poseen, pero no sobre dónde se encuentran físicamente estas instituciones u otros datos de la propia institución, para que también puedan utilizarse por cualquier usuario. Esta información podría ser útil para realizar búsquedas de las bibliotecas más cercanas, acceder a los medios de comunicación con los que cuentan o consultar los identificadores que tienen en otras bases de datos. El objetivo de este artículo es el de mostrar el procedimiento utilizado para inserción de todas las bibliotecas de España en una base de datos libre, con la intención de que sean accesibles por todo el mundo. Utilizando diversas herramientas libres se limpiaron los datos y se insertaron 7861 bibliotecas nuevas en Wikidata, para después corregir duplicaciones existentes e insertar nuevos campos. Por último, se presentan diferentes posibilidades de reutilización de los datos en Wikipedia, demostrando que esos datos pueden ser útiles para los usuarios que quieran utilizarlos en el futuro.

TAGOPSIN: collating taxa-specific gene and protein functional and structural information

Background

The wealth of biological information available nowadays in public databases has triggered an unprecedented rise in multi-database search and data retrieval for obtaining detailed information about key functional and structural entities. This concerns investigations ranging from gene or genome analysis to protein structural analysis. However, the retrieval of interconnected data from a number of different databases is very often done repeatedly in an unsystematic way.

Results

Here, we present TAxonomy, Gene, Ontology, Protein, Structure INtegrated (TAGOPSIN), a command line program written in Java for rapid and systematic retrieval of select data from seven of the most popular public biological databases relevant to comparative genomics and protein structure studies. The program allows a user to retrieve organism-centred data and assemble them in a single data warehouse which constitutes a useful resource for several biological applications. TAGOPSIN was tested with a number of organisms encompassing eukaryotes, prokaryotes and viruses. For example, it successfully integrated data for about 17,000 UniProt entries of Homo sapiens and 21 UniProt entries of human coronavirus.

Conclusion

TAGOPSIN demonstrates efficient data integration whereby manipulation of interconnected data is more convenient than doing multi-database queries. The program facilitates for instance interspecific comparative analyses of protein-coding genes in a molecular evolutionary study, or identification of taxa-specific protein domains and three-dimensional structures. TAGOPSIN is available as a JAR file at https://github.com/ebundhoo/TAGOPSIN and is released under the GNU General Public License.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04429-5.

Intestinal microbiota alterations by dietary exposure to chemicals from food cooking and processing. Application of data science for risk prediction

Diet is one of the main sources of exposure to toxic chemicals with carcinogenic potential, some of which are generated during food processing, depending on the type of food (primarily meat, fish, bread and potatoes), cooking methods and temperature. Although demonstrated in animal models at high doses, an unequivocal link between dietary exposure to these compounds with disease has not been proven in humans. A major difficulty in assessing the actual intake of these toxic compounds is the lack of standardised and harmonised protocols for collecting and analysing dietary information. The intestinal microbiota (IM) has a great influence on health and is altered in some diseases such as colorectal cancer (CRC). Diet influences the composition and activity of the IM, and the net exposure to genotoxicity of potential dietary carcinogens in the gut depends on the interaction among these compounds, IM and diet. This review analyses critically the difficulties and challenges in the study of interactions among these three actors on the onset of CRC. Machine Learning (ML) of data obtained in subclinical and precancerous stages would help to establish risk thresholds for the intake of toxic compounds generated during food processing as related to diet and IM profiles, whereas Semantic Web could improve data accessibility and usability from different studies, as well as helping to elucidate novel interactions among those chemicals, IM and diet.

Wikidata as a knowledge graph for the life sciences

Wikidata is a community-maintained knowledge base that has been assembled from repositories in the fields of genomics, proteomics, genetic variants, pathways, chemical compounds, and diseases, and that adheres to the FAIR principles of findability, accessibility, interoperability and reusability. Here we describe the breadth and depth of the biomedical knowledge contained within Wikidata, and discuss the open-source tools we have built to add information to Wikidata and to synchronize it with source databases. We also demonstrate several use cases for Wikidata, including the crowdsourced curation of biomedical ontologies, phenotype-based diagnosis of disease, and drug repurposing.

GeneDB and Wikidata

Publishing authoritative genomic annotation data, keeping it up to date, linking it to related information, and allowing community annotation is difficult and hard to support with limited resources.

Here, we show how importing GeneDB annotation data into Wikidata allows for leveraging existing resources, integrating volunteer and scientific communities, and enriching the original information.

WikiPathways: a multifaceted pathway database bridging metabolomics to other omics research

WikiPathways (wikipathways.org) captures the collective knowledge represented in biological pathways. By providing a database in a curated, machine readable way, omics data analysis and visualization is enabled. WikiPathways and other pathway databases are used to analyze experimental data by research groups in many fields. Due to the open and collaborative nature of the WikiPathways platform, our content keeps growing and is getting more accurate, making WikiPathways a reliable and rich pathway database. Previously, however, the focus was primarily on genes and proteins, leaving many metabolites with only limited annotation. Recent curation efforts focused on improving the annotation of metabolism and metabolic pathways by associating unmapped metabolites with database identifiers and providing more detailed interaction knowledge. Here, we report the outcomes of the continued growth and curation efforts, such as a doubling of the number of annotated metabolite nodes in WikiPathways. Furthermore, we introduce an OpenAPI documentation of our web services and the FAIR (Findable, Accessible, Interoperable and Reusable) annotation of resources to increase the interoperability of the knowledge encoded in these pathways and experimental omics data. New search options, monthly downloads, more links to metabolite databases, and new portals make pathway knowledge more effortlessly accessible to individual researchers and research communities.

ChlamBase: a curated model organism database for the Chlamydia research community

The accelerating growth of genomic and proteomic information for Chlamydia species, coupled with unique biological aspects of these pathogens, necessitates bioinformatic tools and features that are not provided by major public databases. To meet these growing needs, we developed ChlamBase, a model organism database for Chlamydia that is built upon the WikiGenomes application framework, and Wikidata, a community-curated database. ChlamBase was designed to serve as a central access point for genomic and proteomic information for the Chlamydia research community. ChlamBase integrates information from numerous external databases, as well as important data extracted from the literature that are otherwise not available in structured formats that are easy to use. In addition, a key feature of ChlamBase is that it empowers users in the field to contribute new annotations and data as the field advances with continued discoveries. ChlamBase is freely and publicly available at chlambase.org.

Semalytics: a semantic analytics platform for the exploration of distributed and heterogeneous cancer data in translational research

Each cancer is a complex system with unique molecular features determining its dynamics, such as its prognosis and response to therapies. Understanding the role of these biological traits is fundamental in order to personalize cancer clinical care according to the characteristics of each patient’s disease. To achieve this, translational researchers propagate patients’ samples through in vivo and in vitro cultures to test different therapies on the same tumor and to compare their outcomes with the molecular profile of the disease. This in turn generates information that can be subsequently translated into the development of predictive biomarkers for clinical use. These large-scale experiments generate huge collections of hierarchical data (i.e. experimental trees) with relative annotations that are extremely difficult to analyze. To address such issues in data analyses, we came up with the Semalytics data framework, the core of an analytical platform that processes experimental information through Semantic Web technologies. Semalytics allows (i) the efficient exploration of experimental trees with irregular structures together with their annotations. Moreover, (ii) the platform links its data to a wider open knowledge base (i.e. Wikidata) to add an extended knowledge layer without the need to manage and curate those data locally. Altogether, Semalytics provides augmented perspectives on experimental data, allowing the generation of new hypotheses, which were not anticipated by the user a priori.

In this work, we present the data core we created for Semalytics, focusing on its semantic nucleus and on how it exploits semantic reasoning and data integration to tackle issues of this kind of analyses. Finally, we describe a proof-of-concept study based on the examination of several dozen cases of metastatic colorectal cancer in order to illustrate how Semalytics can help researchers generate hypotheses about the role of genes alterations in causing resistance or sensitivity of cancer cells to specific drugs.

Combinatorial Interactions of Biotic and Abiotic Stresses in Plants and Their Molecular Mechanisms: Systems Biology Approach

Plants are continually facing biotic and abiotic stresses, and hence, they need to respond and adapt to survive. Plant response during multiple and combined biotic and abiotic stresses is highly complex and varied than the individual stress. These stresses resulted alteration of plant behavior through regulating the levels of microRNA, heat shock proteins, epigenetic variations. These variations can cause many adverse effects on the growth and development of the plant. Further, in natural conditions, several abiotic stresses causing factors make the plant more susceptible to pathogens infections and vice-versa. A very intricate and multifaceted interactions of various biomolecules are involved in metabolic pathways that can direct towards a cross-tolerance and improvement of plant's defence system. Systems biology approach plays a significant role in the investigation of these molecular interactions. The valuable information obtained by systems biology will help to develop stress-resistant plant varieties against multiple stresses. Thus, this review aims to decipher various multilevel interactions at the molecular level under combinatorial biotic and abiotic stresses and the role of systems biology to understand these molecular interactions.

Improved strategy for the curation and classification of kinases, with broad applicability to other eukaryotic protein groups

Despite the substantial amount of genomic and transcriptomic data available for a wide range of eukaryotic organisms, most genomes are still in a draft state and can have inaccurate gene predictions. To gain a sound understanding of the biology of an organism, it is crucial that inferred protein sequences are accurately identified and annotated. However, this can be challenging to achieve, particularly for organisms such as parasitic worms (helminths), as most gene prediction approaches do not account for substantial phylogenetic divergence from model organisms, such as Caenorhabditis elegans and Drosophila melanogaster, whose genomes are well-curated. In this paper, we describe a bioinformatic strategy for the curation of gene families and subsequent annotation of encoded proteins. This strategy relies on pairwise gene curation between at least two closely related species using genomic and transcriptomic data sets, and is built on recent work on kinase complements of parasitic worms. Here, we discuss salient technical aspects of this strategy and its implications for the curation of protein families more generally.