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1. UniProt: the universal protein knowledgebase in 2021

   https://doi.org/10.1093/nar/gkaa1100  

   Bateman, Alex; Martin, Maria-Jesus; Orchard, Sandra; Magrane, Michele; Agivetova, Rahat; Ahmad, Shadab; Alpi, Emanuele; Bowler-Barnett, Emily H; Britto, Ramona; Bursteinas, Borisas; et al (November 2020, Nucleic Acids Research)

   Abstract The aim of the UniProt Knowledgebase is to provide users with a comprehensive, high-quality and freely accessible set of protein sequences annotated with functional information. In this article, we describe significant updates that we have made over the last two years to the resource. The number of sequences in UniProtKB has risen to approximately 190 million, despite continued work to reduce sequence redundancy at the proteome level. We have adopted new methods of assessing proteome completeness and quality. We continue to extract detailed annotations from the literature to add to reviewed entries and supplement these in unreviewed entries with annotations provided by automated systems such as the newly implemented Association-Rule-Based Annotator (ARBA). We have developed a credit-based publication submission interface to allow the community to contribute publications and annotations to UniProt entries. We describe how UniProtKB responded to the COVID-19 pandemic through expert curation of relevant entries that were rapidly made available to the research community through a dedicated portal. UniProt resources are available under a CC-BY (4.0) license via the web at https://www.uniprot.org/. 

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

   Chibucos, Marcus; Nadendla, Suvarna; Munro, James; Mitraka, Elvira; Olley, Dustin; Vasilevsky, Nicole; Brush, Matthew; Giglio, Michelle (November 2016, Proceedings of the Joint International Conference on Biological Ontology and BioCreative (ICBO-BioCreative 2016))

   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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4. 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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5. Improving protein function prediction by learning and integrating representations of protein sequences and function labels

   https://doi.org/10.1093/bioadv/vbae120  

   Boadu, Frimpong; Cheng, Jianlin; Zhu, ed., Shanfeng (August 2024, Bioinformatics Advances)

   Abstract MotivationAs fewer than 1% of proteins have protein function information determined experimentally, computationally predicting the function of proteins is critical for obtaining functional information for most proteins and has been a major challenge in protein bioinformatics. Despite the significant progress made in protein function prediction by the community in the last decade, the general accuracy of protein function prediction is still not high, particularly for rare function terms associated with few proteins in the protein function annotation database such as the UniProt. ResultsWe introduce TransFew, a new transformer model, to learn the representations of both protein sequences and function labels [Gene Ontology (GO) terms] to predict the function of proteins. TransFew leverages a large pre-trained protein language model (ESM2-t48) to learn function-relevant representations of proteins from raw protein sequences and uses a biological natural language model (BioBert) and a graph convolutional neural network-based autoencoder to generate semantic representations of GO terms from their textual definition and hierarchical relationships, which are combined together to predict protein function via the cross-attention. Integrating the protein sequence and label representations not only enhances overall function prediction accuracy, but delivers a robust performance of predicting rare function terms with limited annotations by facilitating annotation transfer between GO terms. Availability and implementationhttps://github.com/BioinfoMachineLearning/TransFew. 

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