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Summary: Protein phosphorylation is a crucial post-translational modification implicated in cancer pathogenesis, offering potential diagnostic and therapeutic targets. Here, we developed PhosCancer, a user-friendly database for extracting biologically and clinically relevant insights from phosphoproteomics data. Leveraging data from the CNHPP and CPTAC, PhosCancer encompasses 174,587 phosphosites from 14 datasets spanning 12 cancer types. Through extensive statistical analyses and integration of annotations from external resources, PhosCancer serves as a convenient one-stop platform facilitating the exploration of phosphorylation profiles across different cancer types. Not only does PhosCancer encompass basic information, 3D structure, functional domains, and upstream kinases, but also provides quantitative associations with nine clinical features, and the relevance with hallmarks in both cancer-specific and pan-cancer views. PhosCancer is a valuable resource for cancer researchers and clinicians, promoting the identification of clinically actionable biomarkers and further facilitating the clinical applications of phosphoproteomic data.

Understanding variation of traits within and among species through time and across space is central to many questions in biology. Many resources assemble species-level trait data, but the data and metadata underlying those trait measurements are often not reported. Here, we introduce FuTRES (Functional Trait Resource for Environmental Studies; pronounced few-tress), an online datastore and community resource for individual-level trait reporting that utilizes a semantic framework. FuTRES already stores millions of trait measurements for paleobiological, zooarchaeological, and modern specimens, with a current focus on mammals. We compare dynamically derived extant mammal species' body size measurements in FuTRES with summary values from other compilations, highlighting potential issues with simply reporting a single mean estimate. We then show that individual-level data improve estimates of body mass-including uncertainty-for zooarchaeological specimens. FuTRES facilitates trait data integration and discoverability, accelerating new research agendas, especially scaling from intra- to interspecific trait variability.

Investigator-generated transcriptomic datasets interrogating circulating immune cell (CIC) gene expression in clinical type 1 diabetes (T1D) have underappreciated re-use value. Here, we repurposed these datasets to create an open science environment for the generation of hypotheses around CIC signaling pathways whose gain or loss of function contributes to T1D pathogenesis. We firstly computed sets of genes that were preferentially induced or repressed in T1D CICs and validated these against community benchmarks. We then inferred and validated signaling node networks regulating expression of these gene sets, as well as differentially expressed genes in the original underlying T1D case:control datasets. In a set of three use cases, we demonstrated how informed integration of these networks with complementary digital resources supports substantive, actionable hypotheses around signaling pathway dysfunction in T1D CICs. Finally, we developed a federated, cloud-based web resource that exposes the entire data matrix for unrestricted access and re-use by the research community.

Summary: The human respiratory system is a complex and important system that can suffer a variety of diseases. Single-cell sequencing technologies, applied in many respiratory disease studies, have enhanced our ability in characterizing molecular and phenotypic features at a single-cell resolution. The exponentially increasing data from these studies have consequently led to difficulties in data sharing and analysis. Here, we present scMoresDB, a single-cell multi-omics database platform with extensive omics types tailored for human respiratory diseases. scMoresDB re-analyzes single-cell multi-omics datasets, providing a user-friendly interface with cross-omics search capabilities, interactive visualizations, and analytical tools for comprehensive data sharing and integrative analysis. Our example applications highlight the potential significance of BSG receptor in SARS-CoV-2 infection as well as the involvement of HHIP and TGFB2 in the development and progression of chronic obstructive pulmonary disease. scMoresDB significantly increases accessibility and utility of single-cell data relevant to human respiratory system and associated diseases.

Biological databases serve as a global fundamental infrastructure for the worldwide scientific community, which dramatically aid the transformation of big data into knowledge discovery and drive significant innovations in a wide range of research fields. Given the rapid data production, biological databases continue to increase in size and importance. To build a catalog of worldwide biological databases, we curate a total of 5825 biological databases from 8931 publications, which are geographically distributed in 72 countries/regions and developed by 1975 institutions (as of September 20, 2022). We further devise a z-index, a novel index to characterize the scientific impact of a database, and rank all these biological databases as well as their hosting institutions and countries in terms of citation and z-index. Consequently, we present a series of statistics and trends of worldwide biological databases, yielding a global perspective to better understand their status and impact for life and health sciences. An up-to-date catalog of worldwide biological databases, as well as their curated meta-information and derived statistics, is publicly available at Database Commons (https://ngdc.cncb.ac.cn/databasecommons/).

Summary: The global prevalence and burden of musculoskeletal (MSK) disorders are immense. Advancements in next-generation sequencing (NGS) have generated vast amounts of data, accelerating the research of pathological mechanisms and the development of therapeutic approaches for MSK disorders. However, scattered datasets across various repositories complicate uniform analysis and comparison. Here, we introduce MSdb, a database for visualization and integrated analysis of next-generation sequencing data from human musculoskeletal system, along with manually curated patient phenotype data. MSdb provides various types of analysis, including sample-level browsing of metadata information, gene/miRNA expression, and single-cell RNA-seq dataset. In addition, MSdb also allows integrated analysis for cross-samples and cross-omics analysis, including customized differentially expressed gene/microRNA analysis, microRNA-gene network, scRNA-seq cross-sample/disease integration, and gene regulatory network analysis. Overall, systematic categorizing, standardized processing, and freely accessible knowledge features MSdb a valuable resource for MSK research community.

Summary: AMP-activated protein kinase (AMPK) is a critical cellular energy sensor at the interface of metabolism and cancer. However, the role of AMPK in carcinogenesis remains unclear. Here, through analysis of the TCGA melanoma dataset, we found that PRKAA2 gene that encodes the α2 subunit of AMPK is mutated in ∼9% of cutaneous melanomas, and these mutations tend to co-occur with NF1 mutations. Knockout of AMPKα2 promoted anchorage-independent growth of NF1-mutant melanoma cells, whereas ectopic expression of AMPKα2 inhibited their growth in soft agar assays. Moreover, loss of AMPKα2 accelerated tumor growth of NF1-mutant melanoma and enhanced their brain metastasis in immune-deficient mice. Our findings support that AMPKα2 serves as a tumor suppressor in NF1-mutant melanoma and suggest that AMPK could be a therapeutic target for treating melanoma brain metastasis.

Summary: The public-domain International Tree-Ring Data Bank (ITRDB) is an under-utilized dataset to improve existing estimates of global tree longevity. We used the longest continuous ring-width series of existing ITRDB collections as an index of maximum tree age for that species and site. Using a total of 3,689 collections, we obtained longevity estimates for 237 unique tree species, 157 conifers and 80 angiosperms, distributed all over the world. More than half of the species (167) were represented by no more than 10 collections, and a similar number of species (144) reached longevity greater than 300 years. Maximum tree ages exceeded 1,000 years for several species (22), all of them conifers, whereas angiosperm longevity peaked around 500 years. Given the current emphasis on identifying human-induced impacts on global systems, detailed analyses of ITRDB holdings provide one of the most reliable sources of information for tree longevity as an ecological trait.

Summary: Effective public health measures against SARS-CoV-2 require granular knowledge of population-level immune responses. We developed a Tripartite Automated Blood Immunoassay (TRABI) to assess the IgG response against three SARS-CoV-2 proteins. We used TRABI for continuous seromonitoring of hospital patients and blood donors (n = 72′250) in the canton of Zurich from December 2019 to December 2020 (pre-vaccine period). We found that antibodies waned with a half-life of 75 days, whereas the cumulative incidence rose from 2.3% in June 2020 to 12.2% in mid-December 2020. A follow-up health survey indicated that about 10% of patients infected with wildtype SARS-CoV-2 sustained some symptoms at least twelve months post COVID-19. Crucially, we found no evidence of a difference in long-term complications between those whose infection was symptomatic and those with asymptomatic acute infection. The cohort of asymptomatic SARS-CoV-2-infected subjects represents a resource for the study of chronic and possibly unexpected sequelae.

Protein phosphorylation is a crucial post-translational modification implicated in cancer pathogenesis, offering potential diagnostic and therapeutic targets. Here, we developed PhosCancer, a user-friendly database for extracting biologically and clinically relevant insights from phosphoproteomics data. Leveraging data from the CNHPP and CPTAC, PhosCancer encompasses 174,587 phosphosites from 14 datasets spanning 12 cancer types. Through extensive statistical analyses and integration of annotations from external resources, PhosCancer serves as a convenient one-stop platform facilitating the exploration of phosphorylation profiles across different cancer types. Not only does PhosCancer encompass basic information, 3D structure, functional domains, and upstream kinases, but also provides quantitative associations with nine clinical features, and the relevance with hallmarks in both cancer-specific and pan-cancer views. PhosCancer is a valuable resource for cancer researchers and clinicians, promoting the identification of clinically actionable biomarkers and further facilitating the clinical applications of phosphoproteomic data., Competing Interests: The authors declare no competing interests., (© 2024 The Author(s). Published by Elsevier Inc.)

Summary: Mitochondria are major organelles responsible for cellular energy and metabolism, and their dysfunction is tightly linked to cancer. The mitochondrial ribosome (mitoribosome) is a protein complex consisting of 82 mitoribosomal proteins (MRPs) encoded by nuclear genes and is essential for mitochondrial protein synthesis. However, their roles in tumorigenesis remain poorly understood. We performed pan-cancer analyses of 18,177 tumors representing 28 cancer types to determine somatic alterations of MRP genes as a genetic basis for tumorigenesis. We identified a set of 20 altered MRPs known to be involved in early assembly of the mitoribosome complex. We found that tumors with affected MRPs were associated with impaired mitochondrial functions and TP53 mutations accompanied by increased genomic instability and intra-tumor heterogeneity. MRP deletions were associated with poor survival. Our results reveal a key role for mitochondrial ribosome biogenesis in tumor malignancy across cancer types.

Summary: Understanding variation of traits within and among species through time and across space is central to many questions in biology. Many resources assemble species-level trait data, but the data and metadata underlying those trait measurements are often not reported. Here, we introduce FuTRES (Functional Trait Resource for Environmental Studies; pronounced few-tress), an online datastore and community resource for individual-level trait reporting that utilizes a semantic framework. FuTRES already stores millions of trait measurements for paleobiological, zooarchaeological, and modern specimens, with a current focus on mammals. We compare dynamically derived extant mammal species' body size measurements in FuTRES with summary values from other compilations, highlighting potential issues with simply reporting a single mean estimate. We then show that individual-level data improve estimates of body mass—including uncertainty—for zooarchaeological specimens. FuTRES facilitates trait data integration and discoverability, accelerating new research agendas, especially scaling from intra- to interspecific trait variability.

The rate of biological data generation has increased dramatically in recent years, which has driven the importance of databases as a resource to guide innovation and the generation of biological insights. Given the complexity and scale of these databases, automatic data classification is often required. Biological data sets are often hierarchical in nature, with varying degrees of complexity, imposing different challenges to train, test and validate accurate and generalizable classification models. While some approaches to classify hierarchical data have been proposed, no guidelines regarding their utility, applicability and limitations have been explored or implemented. These include 'Local' approaches considering the hierarchy, building models per level or node, and 'Global' hierarchical classification, using a flat classification approach. To fill this gap, here we have systematically contrasted the performance of 'Local per Level' and 'Local per Node' approaches with a 'Global' approach applied to two different hierarchical datasets: BioLip and CATH. The results show how different components of hierarchical data sets, such as variation coefficient and prediction by depth, can guide the choice of appropriate classification schemes. Finally, we provide guidelines to support this process when embarking on a hierarchical classification task, which will help optimize computational resources and predictive performance. [ABSTRACT FROM AUTHOR]

Summary: Investigator-generated transcriptomic datasets interrogating circulating immune cell (CIC) gene expression in clinical type 1 diabetes (T1D) have underappreciated re-use value. Here, we repurposed these datasets to create an open science environment for the generation of hypotheses around CIC signaling pathways whose gain or loss of function contributes to T1D pathogenesis. We firstly computed sets of genes that were preferentially induced or repressed in T1D CICs and validated these against community benchmarks. We then inferred and validated signaling node networks regulating expression of these gene sets, as well as differentially expressed genes in the original underlying T1D case:control datasets. In a set of three use cases, we demonstrated how informed integration of these networks with complementary digital resources supports substantive, actionable hypotheses around signaling pathway dysfunction in T1D CICs. Finally, we developed a federated, cloud-based web resource that exposes the entire data matrix for unrestricted access and re-use by the research community.

Development of interactive web applications to deposit, visualize and analyze biological datasets is a major subject of bioinformatics. R is a programming language for data science, which is also one of the most popular languages used in biological data analysis and bioinformatics. However, building interactive web applications was a great challenge for R users before the Shiny package was developed by the RStudio company in 2012. By compiling R code into HTML, CSS and JavaScript code, Shiny has made it incredibly easy to build web applications for the large R community in bioinformatics and for even non-programmers. Over 470 biological web applications have been developed with R/Shiny up to now. To further promote the utilization of R/Shiny, we reviewed the development of biological web applications with R/Shiny, including eminent biological web applications built with R/Shiny, basic steps to build an R/Shiny application, commonly used R packages to build the interface and server of R/Shiny applications, deployment of R/Shiny applications in the cloud and online resources for R/Shiny. [ABSTRACT FROM AUTHOR]

Fertility refers to the ability of animals to maintain reproductive function and give birth to offspring, which is an important indicator to measure the productivity of animals. Fertility is affected by many factors, among which environmental factors may also play key roles. During the past years, substantial research studies have been conducted to detect the factors related to fecundity, including genetic factors and environmental factors. However, the identified genes associated with fertility from countless previous studies are randomly dispersed in the literature, whereas some other novel fertility-related genes are needed to detect from omics-based datasets. Here, we constructed a fertility index factor database FifBase based on manually curated published literature and RNA-Seq datasets. During the construction of the literature group, we obtained 3301 articles related to fecundity for 13 species from PubMed, involving 2823 genes, which are related to 75 fecundity indicators or 47 environmental factors. Eventually, 1558 genes associated with fertility were filtered in 10 species, of which 1088 and 470 were from RNA-Seq datasets and text mining data, respectively, involving 2910 fertility-gene pairs and 58 fertility-environmental factors. All these data were cataloged into FifBase (http://www.nwsuaflmz.com/FifBase/), where the fertility-related factor information, including gene annotation and environmental factors, can be browsed, retrieved and downloaded with the user-friendly interface. [ABSTRACT FROM AUTHOR]

Galling insects are specialist herbivorous that have the ability of manipulating plant tissue to form complex biological structures called galls. Even though different organisms have the ability to induce galls in plants, insect galls have the highest degree of structural complexity. The main goal of this study was to obtain a preliminary systematic record of plant gall morphotypes from the Guanacaste Conservation Area in Costa Rica and integrate the information into a biological database. Plant gall morphotypes were recorded, characterized and deposited into a specialized herbarium established as a reference for the inventory. Moreover, organisms associated with gall morphotypes were included in the inventory when it was possible to obtain and identify them. Galls were collected in the rainy season over a period of three years. In total, we recorded forty-four families, seventy genera, and eighty-seven host plant species. One hundred thirty-one morphotypes of plant galls were identified in the Guanacaste Conservation Area. The family with the highest number of gall morphotypes was Fabaceae (8.4%). Leaves were the organ with the largest number of galls (71%), followed by stems (17.6%), and apical buds (6.9%). The predominant gall shape was globular (25.2%), followed by discoid (18.3%). Fifty-nine percent of the galls had a glabrous texture, which was most common on leaves, with 77%. One hundred twenty of our field records (91.6%) of plant galls were new morphotypes not only for Costa Rica but also the world. As a consequence of this research and considering the prospect of future increases in new gall records (and associated organisms), we proposed having the biological entities resulting from the inventory placed in a cecidiarium. This repository represents a standardized and comprehensive way to manage the data and biological materials associated with the plant galls. We also suggest a nomenclature for standardizing gall morphotype registries and identifications. This work is the first and most detailed inventory of plant galls carried out thus far in the Guanacaste Conservation Area. Keywords: Plant gall, morphotype, tropical dry forest, biological database, cecidiarium. [ABSTRACT FROM AUTHOR]

Thoracic malignancies are a common type of cancer and area major global health problem. These complex diseases, including lung cancer, esophageal cancer, and breast cancer, etc. have attracted considerable attention from researchers. Potential gene-cancer associations can be explored by demonstrating the association between clinical data and gene expression data. Emerging evidence suggests that the transcriptome plays a particularly critical role as a diagnostic biomarker in pathology and histology studies. Thus, there is an urgent need to develop a platform that allows users to perform a comprehensive prognostic analysis of thoracic cancers. Here, we developed TTSurv, which aims to correlate coding and noncoding genes with cancers by combining high-throughput data with clinical prognosis. TTSurv focuses on the application of high-throughput data to detect ncRNAs, such as lncRNAs and microRNAs, as novel diagnostic and prognostic biomarkers. For a more comprehensive analysis, a large amount of public expression profile data with clinical follow-up information have been integrated into TTSurv. TTSurv also provides flexible methods such as a minimum p-value algorithm and unsupervised clustering methods that can classify thoracic cancer samples into different risk groups. TTSurv will expand our understanding of ncRNAs in thoracic malignancies and provide new insights into their application as potential prognostic/diagnostic biomarkers.

Thoracic malignancies are a common type of cancer and area major global health problem. These complex diseases, including lung cancer, esophageal cancer, and breast cancer, etc. have attracted considerable attention from researchers. Potential gene-cancer associations can be explored by demonstrating the association between clinical data and gene expression data. Emerging evidence suggests that the transcriptome plays a particularly critical role as a diagnostic biomarker in pathology and histology studies. Thus, there is an urgent need to develop a platform that allows users to perform a comprehensive prognostic analysis of thoracic cancers. Here, we developed TTSurv, which aims to correlate coding and noncoding genes with cancers by combining high-throughput data with clinical prognosis. TTSurv focuses on the application of high-throughput data to detect ncRNAs, such as lncRNAs and microRNAs, as novel diagnostic and prognostic biomarkers. For a more comprehensive analysis, a large amount of public expression profile data with clinical follow-up information have been integrated into TTSurv. TTSurv also provides flexible methods such as a minimum p-value algorithm and unsupervised clustering methods that can classify thoracic cancer samples into different risk groups. TTSurv will expand our understanding of ncRNAs in thoracic malignancies and provide new insights into their application as potential prognostic/diagnostic biomarkers. [ABSTRACT FROM AUTHOR]

The human respiratory system is a complex and important system that can suffer a variety of diseases. Single-cell sequencing technologies, applied in many respiratory disease studies, have enhanced our ability in characterizing molecular and phenotypic features at a single-cell resolution. The exponentially increasing data from these studies have consequently led to difficulties in data sharing and analysis. Here, we present scMoresDB, a single-cell multi-omics database platform with extensive omics types tailored for human respiratory diseases. scMoresDB re-analyzes single-cell multi-omics datasets, providing a user-friendly interface with cross-omics search capabilities, interactive visualizations, and analytical tools for comprehensive data sharing and integrative analysis. Our example applications highlight the potential significance of BSG receptor in SARS-CoV-2 infection as well as the involvement of HHIP and TGFB2 in the development and progression of chronic obstructive pulmonary disease. scMoresDB significantly increases accessibility and utility of single-cell data relevant to human respiratory system and associated diseases., Competing Interests: The authors declare no competing interests., (© 2024 The Author(s).)

Summary: Alzheimer's disease (AD) is a progressive neurodegenerative disorder of the brain and the most common form of dementia among the elderly. The single-cell RNA-sequencing (scRNA-Seq) and single-nucleus RNA-sequencing (snRNA-Seq) techniques are extremely useful for dissecting the function/dysfunction of highly heterogeneous cells in the brain at the single-cell level, and the corresponding data analyses can significantly improve our understanding of why particular cells are vulnerable in AD. We developed an integrated database named scREAD (single-cell RNA-Seq database for Alzheimer's disease), which is as far as we know the first database dedicated to the management of all the existing scRNA-Seq and snRNA-Seq data sets from the human postmortem brain tissue with AD and mouse models with AD pathology. scREAD provides comprehensive analysis results for 73 data sets from 10 brain regions, including control atlas construction, cell-type prediction, identification of differentially expressed genes, and identification of cell-type-specific regulons.

Summary: Since the outbreak in 2019, researchers are trying to find effective drugs against the SARS-CoV-2 virus based on de novo drug design and drug repurposing. The former approach is very time consuming and needs extensive testing in humans, whereas drug repurposing is more promising, as the drugs have already been tested for side effects, etc. At present, there is no treatment for COVID-19 that is clinically effective, but there is a huge amount of data from studies that analyze potential drugs.We developed CORDITE to efficiently combine state-of-the-art knowledge on potential drugs and make it accessible to scientists and clinicians. The web interface also provides access to an easy-to-use API that allows a wide use for other software and applications, e.g., for meta-analysis, design of new clinical studies, or simple literature search. CORDITE is currently empowering many scientists across all continents and accelerates research in the knowledge domains of virology and drug design.

A biological database is a big, structured body of continuous information, generally connected with computerized software intended to update, query, and recover information elements deposited within the framework. A straightforward database could be a single folder comprising several data, each carrying the same number of data. Such famous databases are GenBank from the National Center for Biotechnology Information, SwissProt from the Swiss Institute of Bioinformatics and PIR from the Protein Information Resource. Biological databases are bibliotheques of life science data, gathered from science studies, published literature, high-performance experimental technology, and computational analysis. Here we briefly described some recently published molecular databases.

Abstract Background Germline pathogenic variants in the breast cancer type 1 susceptibility gene BRCA1 are associated with a 60% lifetime risk for breast and ovarian cancer. This overall risk estimate is for all BRCA1 variants; obviously, not all variants confer the same risk of developing a disease. In cancer patients, loss of BRCA1 function in tumor tissue has been associated with an increased sensitivity to platinum agents and to poly-(ADP-ribose) polymerase (PARP) inhibitors. For clinical management of both at-risk individuals and cancer patients, it would be important that each identified genetic variant be associated with clinical significance. Unfortunately for the vast majority of variants, the clinical impact is unknown. The availability of results from studies assessing the impact of variants on protein function may provide insight of crucial importance. Results and conclusion We have collected, curated, and structured the molecular and cellular phenotypic impact of 3654 distinct BRCA1 variants. The data was modeled in triple format, using the variant as a subject, the studied function as the object, and a predicate describing the relation between the two. Each annotation is supported by a fully traceable evidence. The data was captured using standard ontologies to ensure consistency, and enhance searchability and interoperability. We have assessed the extent to which functional defects at the molecular and cellular levels correlate with the clinical interpretation of variants by ClinVar submitters. Approximately 30% of the ClinVar BRCA1 missense variants have some molecular or cellular assay available in the literature. Pathogenic variants (as assigned by ClinVar) have at least some significant functional defect in 94% of testable cases. For benign variants, 77% of ClinVar benign variants, for which neXtProt Cancer variant portal has data, shows either no or mild experimental functional defects. While this does not provide evidence for clinical interpretation of variants, it may provide some guidance for variants of unknown significance, in the absence of more reliable data. The neXtProt Cancer variant portal (https://www.nextprot.org/portals/breast-cancer) contains over 6300 observations at the molecular and/or cellular level for BRCA1 variants.

CytoMegaloVirus (CMV) is known to cause infection in humans and may remain dormant throughout the life span of an individual. CMV infection has been reported to be fatal in patients with weak immunity. It is transmitted through blood, saliva, urine, semen and breast milk. Although medications are available to treat the infected patients, there is no cure for CMV. This concern prompted us to construct a comprehensive database having exhaustive information regarding CMV, its infections and therapies to be available on a single platform. Thus, we propose a newly designed database that includes all the information from various public resources such as biological databases, virus taxonomy databanks, viral databases, and drug bank, integrated into this database, named as cytomegalovirus database (CMVdb). It features all the relevant data regarding the strains of CMV, genes, expressed proteins, the genomic sequence of CMV and drugs used in the treatment of cytomegalovirus infection. CMVdb has a unique feature of in-house data analysis, so all the data obtained from various resources are processed within the system. The user interface is more responsive because of the integrated platform that will highly facilitate the researchers. Based on CMVdb functionality and quality of the data, it will accelerate the research and development in the field of infectious diseases and immunology with a special focus on CMV. The obtained data would be useful in designing better therapeutic strategies and agents for the treatment of CMV infections. The proposed database (CMVdb) is freely accessible at http://shaktisahislab.com/include/CMV/ or http://weislab.com/WeiDOCK/include/content/CMV/. [ABSTRACT FROM AUTHOR]

Phage lytic proteins are a clinically advanced class of novel enzyme-based antibiotics, so-called enzybiotics. A growing community of researchers develops phage lytic proteins with the perspective of their use as enzybiotics. A successful translation of enzybiotics to the market requires well-considered selections of phage lytic proteins in early research stages. Here, we introduce PhaLP, a database of phage lytic proteins, which serves as an open portal to facilitate the development of phage lytic proteins. PhaLP is a comprehensive, easily accessible and automatically updated database (currently 16,095 entries). Capitalizing on the rich content of PhaLP, we have mapped the high diversity of natural phage lytic proteins and conducted analyses at three levels to gain insight in their host-specific evolution. First, we provide an overview of the modular diversity. Secondly, datamining and interpretable machine learning approaches were adopted to reveal host-specific design rules for domain architectures in endolysins. Lastly, the evolution of phage lytic proteins on the protein sequence level was explored, revealing host-specific clusters. In sum, PhaLP can act as a starting point for the broad community of enzybiotic researchers, while the steadily improving evolutionary insights will serve as a natural inspiration for protein engineers.

Abstract Background Biological databases store data about laboratory experiments, together with semantic annotations, in order to support data aggregation and retrieval. The exact meaning of such annotations in the context of a database record is often ambiguous. We address this problem by grounding implicit and explicit database content in a formal-ontological framework. Methods By using a typical extract from the databases UniProt and Ensembl, annotated with content from GO, PR, ChEBI and NCBI Taxonomy, we created four ontological models (in OWL), which generate explicit, distinct interpretations under the BioTopLite2 (BTL2) upper-level ontology. The first three models interpret database entries as individuals (IND), defined classes (SUBC), and classes with dispositions (DISP), respectively; the fourth model (HYBR) is a combination of SUBC and DISP. For the evaluation of these four models, we consider (i) database content retrieval, using ontologies as query vocabulary; (ii) information completeness; and, (iii) DL complexity and decidability. The models were tested under these criteria against four competency questions (CQs). Results IND does not raise any ontological claim, besides asserting the existence of sample individuals and relations among them. Modelling patterns have to be created for each type of annotation referent. SUBC is interpreted regarding maximally fine-grained defined subclasses under the classes referred to by the data. DISP attempts to extract truly ontological statements from the database records, claiming the existence of dispositions. HYBR is a hybrid of SUBC and DISP and is more parsimonious regarding expressiveness and query answering complexity. For each of the four models, the four CQs were submitted as DL queries. This shows the ability to retrieve individuals with IND, and classes in SUBC and HYBR. DISP does not retrieve anything because the axioms with disposition are embedded in General Class Inclusion (GCI) statements. Conclusion Ambiguity of biological database content is addressed by a method that identifies implicit knowledge behind semantic annotations in biological databases and grounds it in an expressive upper-level ontology. The result is a seamless representation of database structure, content and annotations as OWL models.

Rice is one of the most important agricultural crop in the world and widely studied model plant. The completion of whole genome sequence of rice (Oryza sativa) and high-throughput experimental platforms have led to the generation of the tremendous amount of data, and development of the specialized databases and bioinformatics tools for data processing, efficient organization, analysis, and visualization. In this article, we discuss a collection of biological databases that host genomics data on sequence, gene expression, genetic variation, gene-interactomes, and pathways, and facilitate data analysis and visualization.

Non-coding RNAs (ncRNAs) are pivotal in gene regulation during development and disease. MicroRNAs have been extensively studied in neurogenesis. However, limited knowledge exists about the developmental signatures of other ncRNA species in sensory neuron differentiation, and human dorsal root ganglia (DRG) ncRNA expression remains undocumented. To address this gap, we generated a comprehensive atlas of small ncRNA species during iPSC-derived sensory neuron differentiation. Utilizing iPSC-derived sensory neurons and human DRG RNA sequencing, we unveiled signatures describing developmental processes. Our analysis identified ncRNAs associated with various sensory neuron stages. Striking similarities in ncRNA expression signatures between human DRG and iPSC-derived neurons support the latter as a model to bridge the translational gap between preclinical findings and human disorders. In summary, our research sheds light on the role of ncRNA species in human nociceptors, and NOCICEPTRA2.0 offers a comprehensive ncRNA database for sensory neurons that researchers can use to explore ncRNA regulators in nociceptors thoroughly., Competing Interests: The authors declare no competing interests., (© 2023 The Authors.)

Biological databases serve as a global fundamental infrastructure for the worldwide scientific community, which dramatically aid the transformation of big data into knowledge discovery and drive significant innovations in a wide range of research fields. Given the rapid data production, biological databases continue to increase in size and importance. To build a catalog of worldwide biological databases, we curate a total of 5825 biological databases from 8931 publications, which are geographically distributed in 72 countries/regions and developed by 1975 institutions (as of September 20, 2022). We further devise a z-index, a novel index to characterize the scientific impact of a database, and rank all these biological databases as well as their hosting institutions and countries in terms of citation and z-index. Consequently, we present a series of statistics and trends of worldwide biological databases, yielding a global perspective to better understand their status and impact for life and health sciences. An up-to-date catalog of worldwide biological databases, as well as their curated meta-information and derived statistics, is publicly available at Database Commons (https://ngdc.cncb.ac.cn/databasecommons/)., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)