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

{"title":"PeptiHub: a curated repository of precisely annotated cancer-related peptides with advanced utilities for peptide exploration and discovery.","authors":"Sara Zareei, Babak Khorsand, Alireza Dantism, Neda Zareei, Fereshteh Asgharzadeh, Shadi Shams Zahraee, Samane Mashreghi Kashan, Shirin Hekmatirad, Shila Amini, Fatemeh Ghasemi, Maryam Moradnia, Atena Vaghf, Anahid Hemmatpour, Hamdam Hourfar, Soudabeh Niknia, Ali Johari, Fatemeh Salimi, Neda Fariborzi, Zohreh Shojaei, Elaheh Asiaei, Hossein Shabani","doi":"10.1093/database/baae092","DOIUrl":"https://doi.org/10.1093/database/baae092","url":null,"abstract":"<p><p>Peptihub (https://bioinformaticscollege.ir/peptihub/) is a meticulously curated repository of cancer-related peptides (CRPs) that have been documented in scientific literature. A diverse collection of CRPs is included in the PeptiHub, showcasing a spectrum of effects and activities. While some peptides demonstrated significant anticancer efficacy, others exhibited no discernible impact, and some even possessed alternative non-drug functionalities, including drug carrier or carcinogenic attributes. Presently, Peptihub houses 874 CRPs, subjected to evaluation across 10 distinct organism categories, 26 organs, and 438 cell lines. Each entry in the database is accompanied by easily accessible 3D conformations, obtained either experimentally or through predictive methodology. Users are provided with three search frameworks offering basic, advanced, and BLAST sequence search options. Furthermore, precise annotations of peptides enable users to explore CRPs based on their specific activities (anticancer, no effect, insignificant effect, carcinogen, and others) and their effectiveness (rate and IC50) under cancer conditions, specifically within individual organs. This unique property facilitates the construction of robust training and testing datasets. Additionally, PeptiHub offers 1141 features with the convenience of selecting the most pertinent features to address their specific research questions. Features include aaindex1 (in six main subcategories: alpha propensities, beta propensity, composition indices, hydrophobicity, physicochemical properties, and other properties), amino acid composition (Amino acid Composition and Dipeptide Composition), and Grouped Amino Acid Composition (Grouped amino acid composition, Grouped dipeptide composition, and Conjoint triad) categories. These utilities not only speed up machine learning-based peptide design but also facilitate peptide classification. Database URL: https://bioinformaticscollege.ir/peptihub/.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142281705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Autophagy3D: a comprehensive autophagy structure database.","authors":"Neha, Jesu Castin, Saman Fatihi, Deepanshi Gahlot, Akanksha Arun, Lipi Thukral","doi":"10.1093/database/baae088","DOIUrl":"https://doi.org/10.1093/database/baae088","url":null,"abstract":"<p><p>Autophagy pathway plays a central role in cellular degradation. The proteins involved in the core autophagy process are mostly localised on membranes or interact indirectly with lipid-associated proteins. Therefore, progress in structure determination of 'core autophagy proteins' remained relatively limited. Recent paradigm shift in structural biology that includes cutting-edge cryo-EM technology and robust AI-based Alphafold2 predicted models has significantly increased data points in biology. Here, we developed Autophagy3D, a web-based resource that provides an efficient way to access data associated with 40 core human autophagic proteins (80322 structures), their protein-protein interactors and ortholog structures from various species. Autophagy3D also offers detailed visualizations of protein structures, and, hence deriving direct biological insights. The database significantly enhances access to information as full datasets are available for download. The Autophagy3D can be publicly accessed via https://autophagy3d.igib.res.in. Database URL: https://autophagy3d.igib.res.in.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11412239/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142281702","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":"Influenza sequence validation and annotation using VADR.","authors":"Vincent C Calhoun, Eneida L Hatcher, Linda Yankie, Eric P Nawrocki","doi":"10.1093/database/baae091","DOIUrl":"10.1093/database/baae091","url":null,"abstract":"<p><p>Tens of thousands of influenza sequences are deposited into the GenBank database each year. The software tool FLu ANnotation tool (FLAN) has been used by GenBank since 2007 to validate and annotate incoming influenza sequence submissions and has been publicly available as a webserver but not as a standalone tool. Viral Annotation DefineR (VADR) is a general sequence validation and annotation software package used by GenBank for norovirus, dengue virus and SARS-CoV-2 virus sequence processing that is available as a standalone tool. We have created VADR influenza models based on the FLAN reference sequences and adapted VADR to accurately annotate influenza sequences. VADR and FLAN show consistent results on the vast majority of influenza sequences, and when they disagree, VADR is usually correct. VADR can also accurately process influenza D sequences as well as influenza A H17, H18, H19, N10 and N11 subtype sequences, which FLAN cannot. VADR 1.6.3 and the associated influenza models are now freely available for users to download and use. Database URL: https://bitbucket.org/nawrockie/vadr-models-flu.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11411204/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142281704","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":"collectNET: a web server for integrated inference of cell-cell communication network.","authors":"Yan Pan, Zijing Gao, Xuejian Cui, Zhen Li, Rui Jiang","doi":"10.1093/database/baae098","DOIUrl":"https://doi.org/10.1093/database/baae098","url":null,"abstract":"<p><p>Cell-cell communication (CCC) through ligand-receptor (L-R) pairs forms the cornerstone for complex functionalities in multicellular organisms. Deciphering such intercellular signaling can contribute to unraveling disease mechanisms and enable targeted therapy. Nonetheless, notable biases and inconsistencies are evident among the inferential outcomes generated by current methods for inferring CCC network. To fill this gap, we developed collectNET (http://health.tsinghua.edu.cn/collectnet) as a comprehensive web platform for analyzing CCC network, with efficient calculation, hierarchical browsing, comprehensive statistics, advanced searching, and intuitive visualization. collectNET provides a reliable online inference service with prior knowledge of three public L-R databases and systematic integration of three mainstream inference methods. Additionally, collectNET has assembled a human CCC atlas, including 126 785 significant communication pairs based on 343 023 cells. We anticipate that collectNET will benefit researchers in gaining a more holistic understanding of cell development and differentiation mechanisms. Database URL: http://health.tsinghua.edu.cn/collectnet.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11403813/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142281703","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":"An analysis of FRE @ BC8 SympTEMIST track: named entity recognition.","authors":"Ander Martinez, Nuria García-Santa","doi":"10.1093/database/baae101","DOIUrl":"https://doi.org/10.1093/database/baae101","url":null,"abstract":"<p><p>This paper is a more in-depth analysis of the approaches used in our submission (Martínez A, García-Santa N. (2023) FRE @ BC8 SympTEMIST track: Named Entity Recognition Zenodo.) to the 'SympTEMIST' Named Entity Recognition (NER) shared subtask at 'BioCreative 2023'. We participated on the challenge submitting two systems based on a RoBERTa architecture LLM trained on Spanish-language clinical data available at 'HuggingFace' model repository. Before choosing the systems that would be submitted, we tried different combinations of the techniques described here: Conditional Random Fields and Byte-Pair Encoding dropout. In the second system we also included Sub-Subword feature based embeddings (SSW). The test set used in the challenge has now been released (López SL, Sánchez LG, Farré E et al. (2024) SympTEMIST Corpus: Gold Standard annotations for clinical symptoms, signs and findings information extraction. Zenodo), allowing us to analyze more in depth our methods, as well as measuring the impact of introducing data from CARMEN-I (Lima-López S, Farré-Maduell E, Krallinger M. (2023) CARMEN-I: Clinical Entities Annotation Guidelines in Spanish. Zenodo) corpus. Our experiments show the moderate effect of using the Sub-Subword feature based embeddings and the impact of including the symptom NER data from the CARMEN-I dataset. Database URL: https://physionet.org/content/carmen-i/1.0/.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11403810/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142281701","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":"RegulaTome: a corpus of typed, directed, and signed relations between biomedical entities in the scientific literature.","authors":"Katerina Nastou, Farrokh Mehryary, Tomoko Ohta, Jouni Luoma, Sampo Pyysalo, Lars Juhl Jensen","doi":"10.1093/database/baae095","DOIUrl":"10.1093/database/baae095","url":null,"abstract":"<p><p>In the field of biomedical text mining, the ability to extract relations from the literature is crucial for advancing both theoretical research and practical applications. There is a notable shortage of corpora designed to enhance the extraction of multiple types of relations, particularly focusing on proteins and protein-containing entities such as complexes and families, as well as chemicals. In this work, we present RegulaTome, a corpus that overcomes the limitations of several existing biomedical relation extraction (RE) corpora, many of which concentrate on single-type relations at the sentence level. RegulaTome stands out by offering 16 961 relations annotated in >2500 documents, making it the most extensive dataset of its kind to date. This corpus is specifically designed to cover a broader spectrum of >40 relation types beyond those traditionally explored, setting a new benchmark in the complexity and depth of biomedical RE tasks. Our corpus both broadens the scope of detected relations and allows for achieving noteworthy accuracy in RE. A transformer-based model trained on this corpus has demonstrated a promising F1-score (66.6%) for a task of this complexity, underscoring the effectiveness of our approach in accurately identifying and categorizing a wide array of biological relations. This achievement highlights RegulaTome's potential to significantly contribute to the development of more sophisticated, efficient, and accurate RE systems to tackle biomedical tasks. Finally, a run of the trained RE system on all PubMed abstracts and PMC Open Access full-text documents resulted in >18 million relations, extracted from the entire biomedical literature.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11394941/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142281706","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":"Transformer-based approach for symptom recognition and multilingual linking","authors":"Sylvia Vassileva, Georgi Grazhdanski, Ivan Koychev, Svetla Boytcheva","doi":"10.1093/database/baae090","DOIUrl":"https://doi.org/10.1093/database/baae090","url":null,"abstract":"This paper presents a transformer-based approach for symptom Named Entity Recognition (NER) in Spanish clinical texts and multilingual entity linking on the SympTEMIST dataset. For Spanish NER, we fine tune a RoBERTa-based token-level classifier with Bidirectional Long Short-Term Memory and conditional random field layers on an augmented train set, achieving an F1 score of 0.73. Entity linking is performed via a hybrid approach with dictionaries, generating candidates from a knowledge base containing Unified Medical Language System aliases using the cross-lingual SapBERT and reranking the top candidates using GPT-3.5. The entity linking approach shows consistent results for multiple languages of 0.73 accuracy on the SympTEMIST multilingual dataset and also achieves an accuracy of 0.6123 on the Spanish entity linking task surpassing the current top score for this subtask. Database URL: https://github.com/svassileva/symptemist-multilingual-linking","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":null,"pages":null},"PeriodicalIF":5.8,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142204871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interactive tools for functional annotation of bacterial genomes.","authors":"Morgan N Price, Adam P Arkin","doi":"10.1093/database/baae089","DOIUrl":"10.1093/database/baae089","url":null,"abstract":"<p><p>Automated annotations of protein functions are error-prone because of our lack of knowledge of protein functions. For example, it is often impossible to predict the correct substrate for an enzyme or a transporter. Furthermore, much of the knowledge that we do have about the functions of proteins is missing from the underlying databases. We discuss how to use interactive tools to quickly find different kinds of information relevant to a protein's function. Many of these tools are available via PaperBLAST (http://papers.genomics.lbl.gov). Combining these tools often allows us to infer a protein's function. Ideally, accurate annotations would allow us to predict a bacterium's capabilities from its genome sequence, but in practice, this remains challenging. We describe interactive tools that infer potential capabilities from a genome sequence or that search a genome to find proteins that might perform a specific function of interest. Database URL: http://papers.genomics.lbl.gov.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11378808/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142143039","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":"Global Globin Network and adopting genomic variant database requirements for thalassemia.","authors":"Hashim Halim-Fikri, Ninie Nadia Zulkipli, Hafiza Alauddin, Celeste Bento, Carsten W Lederer, Petros Kountouris, Marina Kleanthous, Yetti Hernaningsih, Meow-Keong Thong, Muhammad Hamdi Mahmood, Norafiza Mohd Yasin, Ezalia Esa, Jacques Elion, Domenico Coviello, Raja-Zahratul-Azma Raja-Sabudin, Ghada El-Kamah, John Burn, Narazah Mohd Yusoff, Raj Ramesar, Bin Alwi Zilfalil","doi":"10.1093/database/baae080","DOIUrl":"10.1093/database/baae080","url":null,"abstract":"<p><p>Thalassemia is one of the most prevalent monogenic disorders in low- and middle-income countries (LMICs). There are an estimated 270 million carriers of hemoglobinopathies (abnormal hemoglobins and/or thalassemia) worldwide, necessitating global methods and solutions for effective and optimal therapy. LMICs are disproportionately impacted by thalassemia, and due to disparities in genomics awareness and diagnostic resources, certain LMICs lag behind high-income countries (HICs). This spurred the establishment of the Global Globin Network (GGN) in 2015 at UNESCO, Paris, as a project-wide endeavor within the Human Variome Project (HVP). Primarily aimed at enhancing thalassemia clinical services, research, and genomic diagnostic capabilities with a focus on LMIC needs, GGN aims to foster data collection in a shared database by all affected nations, thus improving data sharing and thalassemia management. In this paper, we propose a minimum requirement for establishing a genomic database in thalassemia based on the HVP database guidelines. We suggest using an existing platform recommended by HVP, the Leiden Open Variation Database (LOVD) (https://www.lovd.nl/). Adoption of our proposed criteria will assist in improving or supplementing the existing databases, allowing for better-quality services for individuals with thalassemia. Database URL: https://www.lovd.nl/.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11373567/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142132087","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":"CuPCA: a web server for pan-cancer association analysis of large-scale cuproptosis-related genes.","authors":"Yishu Xu, Zhenshu Ma, Yajie Wang, Long Zhang, Jiaming Ye, Yuan Chen, Zhengrong Yuan","doi":"10.1093/database/baae075","DOIUrl":"10.1093/database/baae075","url":null,"abstract":"<p><p>Copper-induced cell death is a novel mechanism of cell death, which is defined as cuproptosis. The increasing level of copper can produce toxicity in cells and may cause the occurrence of cell death. Several previous studies have proved that cuproptosis has a tight association with various cancers. Thus, the discovery of relationships between cuproptosis-related genes (CRGs) and human cancers is of great importance. Pan-cancer analysis can efficiently help researchers find out the relationship between multiple cancers and target genes precisely and make various prognostic analyses on cancers and cancer patients. Pan-cancer web servers can provide researchers with direct results of pan-cancer prognostic analyses, which can greatly improve the efficiency of their work. However, to date, no web server provides pan-cancer analysis about CRGs. Therefore, we introduce the cuproptosis pan-cancer analysis database (CuPCA), the first database for various analysis results of CRGs through 33 cancer types. CuPCA is a user-friendly resource for cancer researchers to gain various prognostic analyses between cuproptosis and cancers. It provides single CRG pan-cancer analysis, multi-CRGs pan-cancer analysis, multi-CRlncRNA pan-cancer analysis, and mRNA-circRNA-lncRNA conjoint analysis. These analysis results can not only indicate the relationship between cancers and cuproptosis at both gene level and protein level, but also predict the conditions of different cancer patients, which include their clinical condition, survival condition, and their immunological condition. CuPCA procures the delivery of analyzed data to end users, which improves the efficiency of wide research as well as releases the value of data resources. Database URL: http://cupca.cn/.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11373563/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142132086","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}