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1. Supporting database annotations and beyond with the Evidence & Conclusion Ontology (ECO)

   Chibucos, M.C. ; Nadendla, S. ; Munro, J.B. ; Mitraka, E. ; Olley, D. ; Vasilevsky, N.A. ; Brush, M.H. ; Giglio, M. ( November 2016 , CEUR workshop proceedings)

   The Evidence & Conclusion Ontology (ECO) is a community standard for summarizing evidence in scientific research in a controlled, structured way. Annotations at the world's most frequented biological databases (e.g. model organisms, UniProt, Gene Ontology) are supported using ECO terms. ECO describes evidence derived from experimental and computational methods, author statements curated from the literature, inferences drawn by curators, and other types of evidence. Here, we describe recent ECO developments and collaborations, most notably: (i) a new ECO website containing user documentation, up-to-date news, and visualization tools; (ii) improvements to the ontology structure; (iii) implementing logic via an ongoing collaboration with the Ontology for Biomedical Investigations (OBI); (iv) addition of numerous experimental evidence types; and (v) addition of new evidence classes describing computationally derived evidence. Due to its utility, popularity, and simplicity, ECO is now expanding into realms beyond the protein annotation community, for example the biodiversity and phenotype communities. As ECO continues to grow as a resource, we are seeking new users and new use cases, with the hope that ECO will continue to be a broadly used and easy-to-implement community standard for representing evidence in diverse biological applications. Feel free to visit two ECO-sponsored workshops at ICBO 2016 to learn more: 1. “An introduction to the Evidence and Conclusion Ontology and representing evidence in scientific research” and 2. “OBI-ECO Interactions & Evidence”. 

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2. Supporting database annotations and beyond with the Evidence & Conclusion Ontology (ECO)

   Chibucos, M.C. ; Nadendla, S. ; Munro, J.B. ; Mitraka, E. ; Olley, D. ; Vasilevsky, N.A. ; Brush, M.H. ; Giglio, M. ( November 2016 , CEUR workshop proceedings)

   The Evidence & Conclusion Ontology (ECO) is a community standard for summarizing evidence in scientific research in a controlled, structured way. Annotations at the world's most frequented biological databases (e.g. model organisms, UniProt, Gene Ontology) are supported using ECO terms. ECO describes evidence derived from experimental and computational methods, author statements curated from the literature, inferences drawn by curators, and other types of evidence. Here, we describe recent ECO developments and collaborations, most notably: (i) a new ECO website containing user documentation, up-to-date news, and visualization tools; (ii) improvements to the ontology structure; (iii) implementing logic via an ongoing collaboration with the Ontology for Biomedical Investigations (OBI); (iv) addition of numerous experimental evidence types; and (v) addition of new evidence classes describing computationally derived evidence. Due to its utility, popularity, and simplicity, ECO is now expanding into realms beyond the protein annotation community, for example the biodiversity and phenotype communities. As ECO continues to grow as a resource, we are seeking new users and new use cases, with the hope that ECO will continue to be a broadly used and easy-to-implement community standard for representing evidence in diverse biological applications. Feel free to visit two ECO-sponsored workshops at ICBO 2016 to learn more: 1. “An introduction to the Evidence and Conclusion Ontology and representing evidence in scientific research” and 2. “OBI-ECO Interactions & Evidence”. 

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3. The Evidence and Conclusion Ontology (ECO): Supporting GO Annotations. In Dessimoz, C. and Škunca, N (eds.), "The Gene Ontology Handbook".

   Chibucos, M.C. ; Siegele, D.A. ; Hu, J.C. ; Giglio, M. ( November 2016 , Methods in molecular biology)

   The Evidence and Conclusion Ontology (ECO) is a community resource for describing the various types of evidence that are generated during the course of a scientific study and which are typically used to support assertions made by researchers. ECO describes multiple evidence types, including evidence resulting from experimental (i.e., wet lab) techniques, evidence arising from computational methods, statements made by authors (whether or not supported by evidence), and inferences drawn by researchers curating the literature. In addition to summarizing the evidence that supports a particular assertion, ECO also offers a means to document whether a computer or a human performed the process of making the annotation. Incorporating ECO into an annotation system makes it possible to leverage the structure of the ontology such that associated data can be grouped hierarchically, users can select data associated with particular evidence types, and quality control pipelines can be optimized. Today, over 30 resources, including the Gene Ontology, use the Evidence and Conclusion Ontology to represent both evidence and how annotations are made. 

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4. Wanted: Standards for FAIR taxonomic concept representations and relationships 

   https://doi.org/10.3897/biss.5.75587  

   Sterner, Beckett ; Upham, Nathan ; Gupta, Prashant ; Powell, Caleb ; Franz, Nico ( September 2021 , Biodiversity Information Science and Standards)

   Making the most of biodiversity data requires linking observations of biological species from multiple sources both efficiently and accurately (Bisby 2000, Franz et al. 2016). Aggregating occurrence records using taxonomic names and synonyms is computationally efficient but known to experience significant limitations on accuracy when the assumption of one-to-one relationships between names and biological entities breaks down (Remsen 2016, Franz and Sterner 2018). Taxonomic treatments and checklists provide authoritative information about the correct usage of names for species, including operational representations of the meanings of those names in the form of range maps, reference genetic sequences, or diagnostic traits. They increasingly provide taxonomic intelligence in the form of precise description of the semantic relationships between different published names in the literature. Making this authoritative information Findable, Accessible, Interoperable, and Reusable (FAIR; Wilkinson et al. 2016) would be a transformative advance for biodiversity data sharing and help drive adoption and novel extensions of existing standards such as the Taxonomic Concept Schema and the OpenBiodiv Ontology (Kennedy et al. 2006, Senderov et al. 2018). We call for the greater, global Biodiversity Information Standards (TDWG) and taxonomy community to commit to extending and expanding on how FAIR applies to biodiversity data and include practical targets and criteria for the publication and digitization of taxonomic concept representations and alignments in taxonomic treatments, checklists, and backbones. As a motivating case, consider the abundantly sampled North American deer mouse— Peromyscus maniculatus (Wagner 1845)—which was recently split from one continental species into five more narrowly defined forms, so that the name P. maniculatus is now only applied east of the Mississippi River (Bradley et al. 2019, Greenbaum et al. 2019). That single change instantly rendered ambiguous ~7% of North American mammal records in the Global Biodiversity Information Facility (n=242,663, downloaded 2021-06-04; GBIF.org 2021) and ⅓ of all National Ecological Observatory Network (NEON) small mammal samples (n=10,256, downloaded 2021-06-27). While this type of ambiguity is common in name-based databases when species are split, the example of P. maniculatus is particularly striking for its impact upon biological questions ranging from hantavirus surveillance in North America to studies of climate change impacts upon rodent life-history traits. Of special relevance to NEON sampling is recent evidence suggesting deer mice potentially transmit SARS-CoV-2 (Griffin et al. 2021). Automating the updating of occurrence records in such cases and others will require operational representations of taxonomic concepts—e.g., range maps, reference sequences, and diagnostic traits—that are FAIR in addition to taxonomic concept alignment information (Franz and Peet 2009). Despite steady progress, it remains difficult to find, access, and reuse authoritative information about how to apply taxonomic names even when it is already digitized. It can also be difficult to tell without manual inspection whether similar types of concept representations derived from multiple sources, such as range maps or reference sequences selected from different research articles or checklists, are in fact interoperable for a particular application. The issue is therefore different from important ongoing efforts to digitize trait information in species circumscriptions, for example, and focuses on how already digitized knowledge can best be packaged to inform human experts and artifical intelligence applications (Sterner and Franz 2017). We therefore propose developing community guidelines and criteria for FAIR taxonomic concept representations as "semantic artefacts" of general relevance to linked open data and life sciences research (Le Franc et al. 2020). 

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5. tRFtarget: a database for transfer RNA-derived fragment targets

   https://doi.org/10.1093/nar/gkaa831  

   Li, Ningshan ; Shan, Nayang ; Lu, Lingeng ; Wang, Zuoheng ( October 2020 , Nucleic Acids Research)

   null (Ed.)

   Abstract Transfer RNA-derived fragments (tRFs) are a new class of small non-coding RNAs and play important roles in biological and physiological processes. Prediction of tRF target genes and binding sites is crucial in understanding the biological functions of tRFs in the molecular mechanisms of human diseases. We developed a publicly accessible web-based database, tRFtarget (http://trftarget.net), for tRF target prediction. It contains the computationally predicted interactions between tRFs and mRNA transcripts using the two state-of-the-art prediction tools RNAhybrid and IntaRNA, including location of the binding sites on the target, the binding region, and free energy of the binding stability with graphic illustration. tRFtarget covers 936 tRFs and 135 thousand predicted targets in eight species. It allows researchers to search either target genes by tRF IDs or tRFs by gene symbols/transcript names. We also integrated the manually curated experimental evidence of the predicted interactions into the database. Furthermore, we provided a convenient link to the DAVID® web server to perform downstream functional pathway analysis and gene ontology annotation on the predicted target genes. This database provides useful information for the scientific community to experimentally validate tRF target genes and facilitate the investigation of the molecular functions and mechanisms of tRFs. 

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