Database: The Journal of Biological Databases and Curation最新文献

{"title":"Autoinhibited Protein Database: a curated database of autoinhibitory domains and their autoinhibition mechanisms.","authors":"Daeahn Cho, Hyang-Mi Lee, Ji Ah Kim, Jae Gwang Song, Su-Hee Hwang, Bomi Lee, Jinsil Park, Kha Mong Tran, Jiwon Kim, Phuong Ngoc Lam Vo, Jooeun Bae, Teerapat Pimt, Kangseok Lee, Jörg Gsponer, Hyung Wook Kim, Dokyun Na","doi":"10.1093/database/baae085","DOIUrl":"10.1093/database/baae085","url":null,"abstract":"<p><p>Autoinhibition, a crucial allosteric self-regulation mechanism in cell signaling, ensures signal propagation exclusively in the presence of specific molecular inputs. The heightened focus on autoinhibited proteins stems from their implication in human diseases, positioning them as potential causal factors or therapeutic targets. However, the absence of a comprehensive knowledgebase impedes a thorough understanding of their roles and applications in drug discovery. Addressing this gap, we introduce Autoinhibited Protein Database (AiPD), a curated database standardizing information on autoinhibited proteins. AiPD encompasses details on autoinhibitory domains (AIDs), their targets, regulatory mechanisms, experimental validation methods, and implications in diseases, including associated mutations and post-translational modifications. AiPD comprises 698 AIDs from 532 experimentally characterized autoinhibited proteins and 2695 AIDs from their 2096 homologs, which were retrieved from 864 published articles. AiPD also includes 42 520 AIDs of computationally predicted autoinhibited proteins. In addition, AiPD facilitates users in investigating potential AIDs within a query sequence through comparisons with documented autoinhibited proteins. As the inaugural autoinhibited protein repository, AiPD significantly aids researchers studying autoinhibition mechanisms and their alterations in human diseases. It is equally valuable for developing computational models, analyzing allosteric protein regulation, predicting new drug targets, and understanding intervention mechanisms AiPD serves as a valuable resource for diverse researchers, contributing to the understanding and manipulation of autoinhibition in cellular processes. Database URL: http://ssbio.cau.ac.kr/databases/AiPD.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":"2024 ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11349611/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142079544","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Australian Biosecurity Genomic Database: a new resource for high-throughput sequencing analysis based on the National Notifiable Disease List of Terrestrial Animals.","authors":"Jana Batovska, Natasha D Brohier, Peter T Mee, Fiona E Constable, Brendan C Rodoni, Stacey E Lynch","doi":"10.1093/database/baae084","DOIUrl":"10.1093/database/baae084","url":null,"abstract":"<p><p>The Australian Biosecurity Genomic Database (ABGD) is a curated collection of reference viral genome sequences based on the Australian National Notifiable Disease List of Terrestrial Animals. It was created to facilitate the screening of high-throughput sequencing (HTS) data for the potential presence of viruses associated with notifiable disease. The database includes a single verified sequence (the exemplar species sequence, where relevant) for each of the 60 virus species across 21 viral families that are associated with or cause these notifiable diseases, as recognized by the World Organisation for Animal Health. The open-source ABGD on GitHub provides usage guidance documents and is intended to support building a culture in Australian HTS communities that promotes the use of quality-assured, standardized, and verified databases for Australia's national biosecurity interests. Future expansion of the database will include the addition of more strains or subtypes for highly variable viruses, viruses causing diseases of aquatic animals, and genomes of other types of pathogens associated with notifiable diseases, such as bacteria. Database URL: https://github.com/ausbiopathgenDB/AustralianBiosecurityGenomicDatabase.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":"2024 ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11352597/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142085939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MACSFeD-a database of mosquito acoustic communication and swarming features.","authors":"YuMin M Loh, Matthew P Su, Kayla G Haruni, Azusa Kamikouchi","doi":"10.1093/database/baae086","DOIUrl":"10.1093/database/baae086","url":null,"abstract":"<p><p>Acoustic communication plays an important role during the courtship of many mosquito species. Male mosquitoes show strong attraction to female wing beat frequencies, mediated via spectral matching between female wing beat frequency and male ear mechanical tuning frequency. Such acoustic communication typically occurs within swarms, male-dominated aggregations with species-specific properties. Despite hundreds of relevant publications being available, the lack of a central platform hosting all associated data hinders research efforts and limits cross-species comparisons. Here, we introduce MACSFeD (Mosquito Acoustic Communication and Swarming Features Database), an interactive platform for the exploration of our comprehensive database containing 251 unique reports focusing on different aspects of mosquito acoustic communication, including hearing function, wing beat frequency and phonotaxis, as well as male swarming parameters. MACSFeD serves as an easily accessible, efficient, and robust data visualization tool for mosquito acoustic communication research. We envision that further in-depth studies could arise following the use of this new platform. Database URL: https://minmatt.shinyapps.io/MACSFeD/.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":"2024 ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11352598/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142085937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Recognition and normalization of multilingual symptom entities using in-domain-adapted BERT models and classification layers.","authors":"Fernando Gallego, Francisco J Veredas","doi":"10.1093/database/baae087","DOIUrl":"10.1093/database/baae087","url":null,"abstract":"<p><p>Due to the scarcity of available annotations in the biomedical domain, clinical natural language processing poses a substantial challenge, especially when applied to low-resource languages. This paper presents our contributions for the detection and normalization of clinical entities corresponding to symptoms, signs, and findings present in multilingual clinical texts. For this purpose, the three subtasks proposed in the SympTEMIST shared task of the Biocreative VIII conference have been addressed. For Subtask 1-named entity recognition in a Spanish corpus-an approach focused on BERT-based model assemblies pretrained on a proprietary oncology corpus was followed. Subtasks 2 and 3 of SympTEMIST address named entity linking (NEL) in Spanish and multilingual corpora, respectively. Our approach to these subtasks followed a classification strategy that starts from a bi-encoder trained by contrastive learning, for which several SapBERT-like models are explored. To apply this NEL approach to different languages, we have trained these models by leveraging the knowledge base of domain-specific medical concepts in Spanish supplied by the organizers, which we have translated into the other languages of interest by using machine translation tools. The results obtained in the three subtasks establish a new state of the art. Thus, for Subtask 1 we obtain precision results of 0.804, F1-score of 0.748, and recall of 0.699. For Subtask 2, we obtain performance gains of up to 5.5% in top-1 accuracy when the trained bi-encoder is followed by a WNT-softmax classification layer that is initialized with the mean of the embeddings of a subset of SNOMED-CT terms. For Subtask 3, the differences are even more pronounced, and our multilingual bi-encoder outperforms the other models analyzed in all languages except Swedish when combined with a WNT-softmax classification layer. Thus, the improvements in top-1 accuracy over the best bi-encoder model alone are 13% for Portuguese and 13.26% for Swedish. Database URL: https://doi.org/10.1093/database/baae087.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":"2024 ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11352596/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142085938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating deep learning architectures for enhanced biomedical relation extraction: a pipeline approach.","authors":"M Janina Sarol, Gibong Hong, Evan Guerra, Halil Kilicoglu","doi":"10.1093/database/baae079","DOIUrl":"10.1093/database/baae079","url":null,"abstract":"<p><p>Biomedical relation extraction from scientific publications is a key task in biomedical natural language processing (NLP) and can facilitate the creation of large knowledge bases, enable more efficient knowledge discovery, and accelerate evidence synthesis. In this paper, building upon our previous effort in the BioCreative VIII BioRED Track, we propose an enhanced end-to-end pipeline approach for biomedical relation extraction (RE) and novelty detection (ND) that effectively leverages existing datasets and integrates state-of-the-art deep learning methods. Our pipeline consists of four tasks performed sequentially: named entity recognition (NER), entity linking (EL), RE, and ND. We trained models using the BioRED benchmark corpus that was the basis of the shared task. We explored several methods for each task and combinations thereof: for NER, we compared a BERT-based sequence labeling model that uses the BIO scheme with a span classification model. For EL, we trained a convolutional neural network model for diseases and chemicals and used an existing tool, PubTator 3.0, for mapping other entity types. For RE and ND, we adapted the BERT-based, sentence-bound PURE model to bidirectional and document-level extraction. We also performed extensive hyperparameter tuning to improve model performance. We obtained our best performance using BERT-based models for NER, RE, and ND, and the hybrid approach for EL. Our enhanced and optimized pipeline showed substantial improvement compared to our shared task submission, NER: 93.53 (+3.09), EL: 83.87 (+9.73), RE: 46.18 (+15.67), and ND: 38.86 (+14.9). While the performances of the NER and EL models are reasonably high, RE and ND tasks remain challenging at the document level. Further enhancements to the dataset could enable more accurate and useful models for practical use. We provide our models and code at https://github.com/janinaj/e2eBioMedRE/. Database URL: https://github.com/janinaj/e2eBioMedRE/.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":"2024 ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11352595/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142085936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RiceMetaSys: Drought-miR, a one-stop solution for drought responsive miRNAs-mRNA module in rice.","authors":"Deepesh Kumar, SureshKumar Venkadesan, Ratna Prabha, Shbana Begam, Bipratip Dutta, Dwijesh C Mishra, K K Chaturvedi, Girish Kumar Jha, Amolkumar U Solanke, Amitha Mithra Sevanthi","doi":"10.1093/database/baae076","DOIUrl":"10.1093/database/baae076","url":null,"abstract":"<p><p>MicroRNAs are key players involved in stress responses in plants and reports are available on the role of miRNAs in drought stress response in rice. This work reports the development of a database, RiceMetaSys: Drought-miR, based on the meta-analysis of publicly available sRNA datasets. From 28 drought stress-specific sRNA datasets, we identified 216 drought-responsive miRNAs (DRMs). The major features of the database include genotype-, tissue- and miRNA ID-specific search options and comparison of genotypes to identify common miRNAs. Co-localization of the DRMs with the known quantitative trait loci (QTLs), i.e., meta-QTL regions governing drought tolerance in rice pertaining to different drought adaptive traits, narrowed down this to 37 promising DRMs. To identify the high confidence target genes of DRMs under drought stress, degradome datasets and web resource on drought-responsive genes (RiceMetaSys: DRG) were used. Out of the 216 unique DRMs, only 193 had targets with high stringent parameters. Out of the 1081 target genes identified by Degradome datasets, 730 showed differential expression under drought stress in at least one accession. To retrieve complete information on the target genes, the database has been linked with RiceMetaSys: DRG. Further, we updated the RiceMetaSys: DRGv1 developed earlier with the addition of DRGs identified from RNA-seq datasets from five rice genotypes. We also identified 759 putative novel miRNAs and their target genes employing stringent criteria. Novel miRNA search has all the search options of known miRNAs and additionally, it gives information on their in silico validation features. Simple sequence repeat markers for both the miRNAs and their target genes have also been designed and made available in the database. Network analysis of the target genes identified 60 hub genes which primarily act through abscisic acid pathway and jasmonic acid pathway. Co-localization of the hub genes with the meta-QTL regions governing drought tolerance narrowed down this to 16 most promising DRGs. Database URL: http://14.139.229.201/RiceMetaSys_miRNA Updated database of RiceMetaSys URL: http://14.139.229.201/RiceMetaSysA/Drought/.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":"2024 ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11338179/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142016614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancing microbiome research through standardized data and metadata collection: introducing the Microbiome Research Data Toolkit.","authors":"Lyndon Zass, Lamech M Mwapagha, Adetola F Louis-Jacques, Imane Allali, Julius Mulindwa, Anmol Kiran, Mariem Hanachi, Oussama Souiai, Nicola Mulder, Ovokeraye H Oduaran","doi":"10.1093/database/baae062","DOIUrl":"10.1093/database/baae062","url":null,"abstract":"<p><p>Microbiome research has made significant gains with the evolution of sequencing technologies. Ensuring comparability between studies and enhancing the findability, accessibility, interoperability and reproducibility of microbiome data are crucial for maximizing the value of this growing body of research. Addressing the challenges of standardized metadata reporting, collection and curation, the Microbiome Working Group of the Human Hereditary and Health in Africa (H3Africa) consortium aimed to develop a comprehensive solution. In this paper, we present the Microbiome Research Data Toolkit, a versatile tool designed to standardize microbiome research metadata, facilitate MIxS-MIMS and PhenX reporting, standardize prospective collection of participant biological and lifestyle data, and retrospectively harmonize such data. This toolkit enables past, present and future microbiome research endeavors to collaborate effectively, fostering novel collaborations and accelerating knowledge discovery in the field. Database URL: https://doi.org/10.25375/uct.24218999.v2.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":"2024 ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11338178/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142016613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GMMID: genetically modified mice information database.","authors":"Menglin Xu, Minghui Fang, Qiyang Chen, Wenjun Xiao, Zhixuan Xu, Bao Cai, Zhenyang Zhao, Tao Wang, Zhu Zhu, Yingshan Chen, Yue Zhu, Mingzhou Dai, Tiancheng Jiang, Xinyi Li, Siuwing Chun, Runhua Zhou, Yafei Li, Yueyue Gou, Jingjing He, Lin Luo, Linlin You, Xuan Jiang","doi":"10.1093/database/baae078","DOIUrl":"10.1093/database/baae078","url":null,"abstract":"<p><p>Genetically engineered mouse models (GEMMs) are vital for elucidating gene function and disease mechanisms. An overwhelming number of GEMM lines have been generated, but endeavors to collect and organize the information of these GEMMs are seriously lagging behind. Only a few databases are developed for the information of current GEMMs, and these databases lack biological descriptions of allele compositions, which poses a challenge for nonexperts in mouse genetics to interpret the genetic information of these mice. Moreover, these databases usually do not provide information on human diseases related to the GEMM, which hinders the dissemination of the insights the GEMM provides as a human disease model. To address these issues, we developed an algorithm to annotate all the allele compositions that have been reported with Python programming and have developed the genetically modified mice information database (GMMID; http://www.gmmid.cn), a user-friendly database that integrates information on GEMMs and related diseases from various databases, including National Center for Biotechnology Information, Mouse Genome Informatics, Online Mendelian Inheritance in Man, International Mouse Phenotyping Consortium, and Jax lab. GMMID provides comprehensive genetic information on >70 055 alleles, 65 520 allele compositions, and ∼4000 diseases, along with biologically meaningful descriptions of alleles and allele combinations. Furthermore, it provides spatiotemporal visualization of anatomical tissues mentioned in these descriptions, shown alongside the allele compositions. Compared to existing mouse databases, GMMID considers the needs of researchers across different disciplines and presents obscure genetic information in an intuitive and easy-to-understand format. It facilitates users in obtaining complete genetic information more efficiently, making it an essential resource for cross-disciplinary researchers. Database URL: http://www.gmmid.cn.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":"2024 ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11334936/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142008463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ncStem: a comprehensive resource of curated and predicted ncRNAs in cancer stemness.","authors":"Hui Liu, Nan Zhang, Yijie Jia, Jun Wang, Aokun Ye, Siru Yang, Honghan Zhou, Yingli Lv, Chaohan Xu, Shuyuan Wang","doi":"10.1093/database/baae081","DOIUrl":"10.1093/database/baae081","url":null,"abstract":"<p><p>Cancer stemness plays an important role in cancer initiation and progression, and is the major cause of tumor invasion, metastasis, recurrence, and poor prognosis. Non-coding RNAs (ncRNAs) are a class of RNA transcripts that generally cannot encode proteins and have been demonstrated to play a critical role in regulating cancer stemness. Here, we developed the ncStem database to record manually curated and predicted ncRNAs associated with cancer stemness. In total, ncStem contains 645 experimentally verified entries, including 159 long non-coding RNAs (lncRNAs), 254 microRNAs (miRNAs), 39 circular RNAs (circRNAs), and 5 other ncRNAs. The detailed information of each entry includes the ncRNA name, ncRNA identifier, disease, reference, expression direction, tissue, species, and so on. In addition, ncStem also provides computationally predicted cancer stemness-associated ncRNAs for 33 TCGA cancers, which were prioritized using the random walk with restart (RWR) algorithm based on regulatory and co-expression networks. The total predicted cancer stemness-associated ncRNAs included 11 132 lncRNAs and 972 miRNAs. Moreover, ncStem provides tools for functional enrichment analysis, survival analysis, and cell location interrogation for cancer stemness-associated ncRNAs. In summary, ncStem provides a platform to retrieve cancer stemness-associated ncRNAs, which may facilitate research on cancer stemness and offer potential targets for cancer treatment. Database URL: http://www.nidmarker-db.cn/ncStem/index.html.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":"2024 ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11321241/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141975336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Functional implications of glycans and their curation: insights from the workshop held at the 16th Annual International Biocuration Conference in Padua, Italy.","authors":"Karina Martinez, Jon Agirre, Yukie Akune, Kiyoko F Aoki-Kinoshita, Cecilia Arighi, Kristian B Axelsen, Evan Bolton, Emily Bordeleau, Nathan J Edwards, Elisa Fadda, Ten Feizi, Catherine Hayes, Callum M Ives, Hiren J Joshi, Khakurel Krishna Prasad, Sofia Kossida, Frederique Lisacek, Yan Liu, Thomas Lütteke, Junfeng Ma, Adnan Malik, Maria Martin, Akul Y Mehta, Sriram Neelamegham, Kalpana Panneerselvam, René Ranzinger, Sylvie Ricard-Blum, Gaoussou Sanou, Vijay Shanker, Paul D Thomas, Michael Tiemeyer, James Urban, Randi Vita, Jeet Vora, Yasunori Yamamoto, Raja Mazumder","doi":"10.1093/database/baae073","DOIUrl":"10.1093/database/baae073","url":null,"abstract":"<p><p>Dynamic changes in protein glycosylation impact human health and disease progression. However, current resources that capture disease and phenotype information focus primarily on the macromolecules within the central dogma of molecular biology (DNA, RNA, proteins). To gain a better understanding of organisms, there is a need to capture the functional impact of glycans and glycosylation on biological processes. A workshop titled \"Functional impact of glycans and their curation\" was held in conjunction with the 16th Annual International Biocuration Conference to discuss ongoing worldwide activities related to glycan function curation. This workshop brought together subject matter experts, tool developers, and biocurators from over 20 projects and bioinformatics resources. Participants discussed four key topics for each of their resources: (i) how they curate glycan function-related data from publications and other sources, (ii) what type of data they would like to acquire, (iii) what data they currently have, and (iv) what standards they use. Their answers contributed input that provided a comprehensive overview of state-of-the-art glycan function curation and annotations. This report summarizes the outcome of discussions, including potential solutions and areas where curators, data wranglers, and text mining experts can collaborate to address current gaps in glycan and glycosylation annotations, leveraging each other's work to improve their respective resources and encourage impactful data sharing among resources. Database URL: https://wiki.glygen.org/Glycan_Function_Workshop_2023.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":"2024 ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11321244/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141975335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}