EPPO ontology: a semantic-driven approach for plant and pest codes representation.

The agricultural industry and regulatory organizations define strategies and build tools and products for plant protection against pests. To identify different plants and their related pests and avoid inconsistencies between such organizations, an agreed and shared classification is necessary. In this regard, the European and Mediterranean Plant Protection Organization (EPPO) has been working on defining and maintaining a harmonized coding system (EPPO codes). EPPO codes are an easy way of referring to a specific organism by means of short 5 or 6 letter codes instead of long scientific names or ambiguous common names. EPPO codes are freely available in different formats through the EPPO Global Database platform and are implemented as a worldwide standard and used among scientists and experts in both industry and regulatory organizations. One of the large companies that adopted such codes is BASF, which uses them mainly in research and development to build their crop protection and seeds products. However, extracting the information is limited by fixed API calls or files that require additional processing steps. Facing these issues makes it difficult to use the available information flexibly, infer new data connections, or enrich it with external data sources. To overcome such limitations, BASF has developed an internal EPPO ontology to represent the list of codes provided by the EPPO Global Database as well as the regulatory categorization and relationship among them. This paper presents the development process of this ontology along with its enrichment process, which allows the reuse of relevant information available in an external knowledge source such as the NCBI Taxon. In addition, this paper describes the use and adoption of the EPPO ontology within the BASF's Agricultural Solutions division and the lessons learned during this work.

Development of a fixed module repertoire for the analysis and interpretation of blood transcriptome data.

As the capacity for generating large-scale molecular profiling data continues to grow, the ability to extract meaningful biological knowledge from it remains a limitation. Here, we describe the development of a new fixed repertoire of transcriptional modules, BloodGen3, that is designed to serve as a stable reusable framework for the analysis and interpretation of blood transcriptome data. The construction of this repertoire is based on co-clustering patterns observed across sixteen immunological and physiological states encompassing 985 blood transcriptome profiles. Interpretation is supported by customized resources, including module-level analysis workflows, fingerprint grid plot visualizations, interactive web applications and an extensive annotation framework comprising functional profiling reports and reference transcriptional profiles. Taken together, this well-characterized and well-supported transcriptional module repertoire can be employed for the interpretation and benchmarking of blood transcriptome profiles within and across patient cohorts. Blood transcriptome fingerprints for the 16 reference cohorts can be accessed interactively via:  https://drinchai.shinyapps.io/BloodGen3Module/ .

GSAn: an alternative to enrichment analysis for annotating gene sets.

The revolution in new sequencing technologies is greatly leading to new understandings of the relations between genotype and phenotype. To interpret and analyze data that are grouped according to a phenotype of interest, methods based on statistical enrichment became a standard in biology. However, these methods synthesize the biological information by a priori selecting the over-represented terms and may suffer from focusing on the most studied genes that represent a limited coverage of annotated genes within a gene set. Semantic similarity measures have shown great results within the pairwise gene comparison by making advantage of the underlying structure of the Gene Ontology. We developed GSAn, a novel gene set annotation method that uses semantic similarity measures to synthesize a priori Gene Ontology annotation terms. The originality of our approach is to identify the best compromise between the number of retained annotation terms that has to be drastically reduced and the number of related genes that has to be as large as possible. Moreover, GSAn offers interactive visualization facilities dedicated to the multi-scale analysis of gene set annotations. Compared to enrichment analysis tools, GSAn has shown excellent results in terms of maximizing the gene coverage while minimizing the number of terms.

A new method for evaluating the impacts of semantic similarity measures on the annotation of gene sets.

MOTIVATION: The recent revolution in new sequencing technologies, as a part of the continuous process of adopting new innovative protocols has strongly impacted the interpretation of relations between phenotype and genotype. Thus, understanding the resulting gene sets has become a bottleneck that needs to be addressed. Automatic methods have been proposed to facilitate the interpretation of gene sets. While statistical functional enrichment analyses are currently well known, they tend to focus on well-known genes and to ignore new information from less-studied genes. To address such issues, applying semantic similarity measures is logical if the knowledge source used to annotate the gene sets is hierarchically structured. In this work, we propose a new method for analyzing the impact of different semantic similarity measures on gene set annotations. RESULTS: We evaluated the impact of each measure by taking into consideration the two following features that correspond to relevant criteria for a "good" synthetic gene set annotation: (i) the number of annotation terms has to be drastically reduced and the representative terms must be retained while annotating the gene set, and (ii) the number of genes described by the selected terms should be as large as possible. Thus, we analyzed nine semantic similarity measures to identify the best possible compromise between both features while maintaining a sufficient level of details. Using Gene Ontology to annotate the gene sets, we obtained better results with node-based measures that use the terms' characteristics than with measures based on edges that link the terms. The annotation of the gene sets achieved with the node-based measures did not exhibit major differences regardless of the characteristics of terms used.