The European Bioinformatics Institute (EMBL-EBI) in 2021

Osteoporosis and depression in perimenopausal women: From clinical association to genetic causality

BACKGROUND

Osteoporosis and major depressive disorder (MDD) represent two significant health challenges globally, particularly among perimenopausal women. This study utilizes NHANES data and Mendelian randomization (MR) analysis to explore the link between them, aiming to provide a basis for intervention strategies for this group.

METHODS

The study analyzed NHANES 2007-2018 data using weighted logistic regression in R software to evaluate the link between MDD and osteoporosis risk. Then, a two-sample MR analysis with GWAS summary statistics was performed, mainly using the IVW method. Additional validation included MR Egger, Weighted Median, Mode, and MR-PRESSO methods.

RESULTS

The research analysis indicated a significant link between MDD and the risk of osteopenia/osteoporosis. Our analysis revealed a significant positive relationship between MDD and both femoral neck osteoporosis (OR = 6.942 [95 % CI, 1.692-28.485]) and trochanteric osteoporosis (OR = 4.140 [95 % CI, 1.699-10.089]). In analyses related to osteopenia, a significant positive correlation was observed between MDD and both total femoral osteopenia (OR = 3.309 [95 % CI, 1.577-6.942]) and trochanteric osteopenia (OR = 2.467 [95 % CI, 1.004-6.062]). Furthermore, in the MR analysis, genetically predicted MDD was causally associated with an increased risk of osteoporosis via the IVW method (P = 0.013).

LIMITATIONS

Our study was limited by potential selection bias due to excluding subjects with missing data, and its applicability was primarily to European and American populations.

CONCLUSION

Integrating NHANES and MR analyses, a robust correlation between MDD and osteoporosis was identified, emphasizing the significance of addressing this comorbidity within clinical practice and meriting further investigation.

Cholecystitis may decrease the risk of sudden death: A 2-sample Mendelian randomization study

Some observational studies have highlighted a significant association between cholecystitis and factors leading to sudden death; however, the specific relationship between the 2 has not been fully elucidated. The primary objective of this study was to elucidate the causal interplay between cholecystitis and augmented risk of sudden cardiac death. We used large-scale genetic summary data from genome-wide association study, genetic summary statistics were sourced from 3 eminent repositories: the UK Biobank (N = 463,010), the FinnGen consortium (N = 215,027), and the European Bioinformatics Institute (N = 471,251). By employing 2-sample Mendelian randomization (MR) to decipher the causal interplay between cholecystitis and sudden death etiologies, a meta-analytical approach was employed to amalgamate the findings derived from these disparate data sources. The primary MR methodologies used included inverse variance weighting with random effects, inverse variance weighting with fixed effects, maximum likelihood, MR-Egger, and weighted median. Subsequently, we performed heterogeneity testing, polyvalency examination, and sensitivity analysis to bolster the robustness of causal relationship assessments. Meta-analysis and amalgamating variegated data sources revealed a statistically significant inverse correlation between cholecystitis and ventricular arrhythmias (odds ratio, 0.896; 95% confidence interval: 0.826-0.971; P = .008). Similarly, an inverse association was observed between cholecystitis and aortic aneurysm (odds ratio, 0.899; 95% confidence interval: 0.851-0.951, P < .001). This study substantiates the absence of a direct causal link between cholecystitis and cerebrovascular accidents (P = .771), pulmonary embolism (P = .071), and acute myocardial infarction (P = .388). A direct causal correlation existed between cholecystitis and sudden death associated with ventricular arrhythmias and aortic aneurysms. The onset of cholecystitis may mitigate the risk of sudden death due to ventricular arrhythmias and aortic aneurysms.

Screen time in the development of cardiovascular diseases: A two-sample Mendelian randomization study

BACKGROUND AND AIMS

Coronary artery disease (CAD), heart failure (HF), and ischemic heart disease (IHD) are three common cardiovascular diseases that are closely associated with metabolic activity. The global incidence and prevalence of these conditions are on the rise, primarily due to unhealthy lifestyles, aging populations, and the increasing prevalence of obesity and diabetes. Excessive screen time has emerged as a potential risk factor for various adverse health outcomes, although limited research has explored its relationship with cardiovascular disease outcomes.

METHODS AND RESULTS

A Mendelian randomization (MR) study was conducted, employing exposure-associated genetic variants as instrumental variables to explore the causal relationship between screen time use and cardiovascular disease outcomes. Single nucleotide polymorphisms (SNPs) were utilized as pooled data for the genetic variable instrument, investigating the association between screen use duration and three types of cardiovascular diseases: coronary artery disease (CAD), heart failure (HF), and ischemic heart disease (IHD). Through the MR analysis, it was revealed that the use of mobile phones and TV screens exhibited a significant causal association with the occurrence of CAD, heart failure, and IHD. However, no significant association was observed between the use of computers and these three types of cardiovascular diseases.

CONCLUSION

Our study suggests that excessive screen time use is associated with the development of cardiovascular disease. However, it should be noted that the consequences of screen time can vary depending on the reasons and purposes for its use. Implementing reasonable control over screen time, particularly for entertainment purposes, holds promise as a potential approach to mitigating cardiovascular disease.

Mobilisation and analyses of publicly available SARS-CoV-2 data for pandemic responses

The COVID-19 pandemic has seen large-scale pathogen genomic sequencing efforts, becoming part of the toolbox for surveillance and epidemic research. This resulted in an unprecedented level of data sharing to open repositories, which has actively supported the identification of SARS-CoV-2 structure, molecular interactions, mutations and variants, and facilitated vaccine development and drug reuse studies and design. The European COVID-19 Data Platform was launched to support this data sharing, and has resulted in the deposition of several million SARS-CoV-2 raw reads. In this paper we describe (1) open data sharing, (2) tools for submission, analysis, visualisation and data claiming (e.g. ORCiD), (3) the systematic analysis of these datasets, at scale via the SARS-CoV-2 Data Hubs as well as (4) lessons learnt. This paper describes a component of the Platform, the SARS-CoV-2 Data Hubs, which enable the extension and set up of infrastructure that we intend to use more widely in the future for pathogen surveillance and pandemic preparedness.

Corner Engineering: Tailoring Enzymes for Enhanced Resistance and Thermostability in Deep Eutectic Solvents

Deep eutectic solvents (DESs), heralded for their synthesis simplicity, economic viability, and reduced volatility and flammability, have found increasing application in biocatalysis. However, challenges persist due to a frequent diminution in enzyme activity and stability. Herein, we developed a general protein engineering strategy, termed corner engineering, to acquire DES-resistant and thermostable enzymes via precise tailoring of the transition region in enzyme structure. Employing Bacillus subtilis lipase A (BSLA) as a model, we delineated the engineering process, yielding five multi-DESs resistant variants with highly improved thermostability, such as K88E/N89 K exhibited up to a 10.0-fold catalytic efficiency (kcat /KM ) increase in 30 % (v/v) choline chloride (ChCl): acetamide and 4.1-fold in 95 % (v/v) ChCl: ethylene glycol accompanying 6.7-fold thermal resistance improvement than wild type at ≈50 °C. The generality of the optimized approach was validated by two extra industrial enzymes, endo-β-1,4-glucanase PvCel5A (used for biofuel production) and esterase Bs2Est (used for plastics degradation). The molecular investigations revealed that increased water molecules at substrate binding cleft and finetuned helix formation at the corner region are two dominant determinants governing elevated resistance and thermostability. This study, coupling corner engineering with obtained molecular insights, illuminates enzyme-DES interaction patterns and fosters the rational design of more DES-resistant and thermostable enzymes in biocatalysis and biotransformation.

CROST: a comprehensive repository of spatial transcriptomics

The development of spatial transcriptome sequencing technology has revolutionized our comprehension of complex tissues and propelled life and health sciences into an era of spatial omics. However, the current availability of databases for accessing and analyzing spatial transcriptomic data is limited. In response, we have established CROST (https://ngdc.cncb.ac.cn/crost), a comprehensive repository of spatial transcriptomics. CROST encompasses high-quality samples and houses 182 spatial transcriptomic datasets from diverse species, organs, and diseases, comprising 1033 sub-datasets and 48 043 tumor-related spatially variable genes (SVGs). Additionally, it encompasses a standardized spatial transcriptome data processing pipeline, integrates single-cell RNA sequencing deconvolution spatial transcriptomics data, and evaluates correlation, colocalization, intercellular communication, and biological function annotation analyses. Moreover, CROST integrates the transcriptome, epigenome, and genome to explore tumor-associated SVGs and provides a comprehensive understanding of their roles in cancer progression and prognosis. Furthermore, CROST provides two online tools, single-sample gene set enrichment analysis and SpatialAP, for users to annotate and analyze the uploaded spatial transcriptomics data. The user-friendly interface of CROST facilitates browsing, searching, analyzing, visualizing, and downloading desired information. Collectively, CROST offers fresh and comprehensive insights into tissue structure and a foundation for understanding multiple biological mechanisms in diseases, particularly in tumor tissues.

AGIDB: a versatile database for genotype imputation and variant decoding across species

The high cost of large-scale, high-coverage whole-genome sequencing has limited its application in genomics and genetics research. The common approach has been to impute whole-genome sequence variants obtained from a few individuals for a larger population of interest individually genotyped using SNP chip. An alternative involves low-coverage whole-genome sequencing (lcWGS) of all individuals in the larger population, followed by imputation to sequence resolution. To overcome limitations of processing lcWGS data and meeting specific genotype imputation requirements, we developed AGIDB (https://agidb.pro), a website comprising tools and database with an unprecedented sample size and comprehensive variant decoding for animals. AGIDB integrates whole-genome sequencing and chip data from 17 360 and 174 945 individuals, respectively, across 89 species to identify over one billion variants, totaling a massive 688.57 TB of processed data. AGIDB focuses on integrating multiple genotype imputation scenarios. It also provides user-friendly searching and data analysis modules that enable comprehensive annotation of genetic variants for specific populations. To meet a wide range of research requirements, AGIDB offers downloadable reference panels for each species in addition to its extensive dataset, variant decoding and utility tools. We hope that AGIDB will become a key foundational resource in genetics and breeding, providing robust support to researchers.

CGG toolkit: Software components for computational genomics

Public-domain availability for bioinformatics software resources is a key requirement that ensures long-term permanence and methodological reproducibility for research and development across the life sciences. These issues are particularly critical for widely used, efficient, and well-proven methods, especially those developed in research settings that often face funding discontinuities. We re-launch a range of established software components for computational genomics, as legacy version 1.0.1, suitable for sequence matching, masking, searching, clustering and visualization for protein family discovery, annotation and functional characterization on a genome scale. These applications are made available online as open source and include MagicMatch, GeneCAST, support scripts for CoGenT-like sequence collections, GeneRAGE and DifFuse, supported by centrally administered bioinformatics infrastructure funding. The toolkit may also be conceived as a flexible genome comparison software pipeline that supports research in this domain. We illustrate basic use by examples and pictorial representations of the registered tools, which are further described with appropriate documentation files in the corresponding GitHub release.

Database Commons: A Catalog of Worldwide Biological Databases

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/).

COWID: an efficient cloud-based genomics workflow for scalable identification of SARS-COV-2

Implementing a specific cloud resource to analyze extensive genomic data on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) poses a challenge when resources are limited. To overcome this, we repurposed a cloud platform initially designed for use in research on cancer genomics (https://cgc.sbgenomics.com) to enable its use in research on SARS-CoV-2 to build Cloud Workflow for Viral and Variant Identification (COWID). COWID is a workflow based on the Common Workflow Language that realizes the full potential of sequencing technology for use in reliable SARS-CoV-2 identification and leverages cloud computing to achieve efficient parallelization. COWID outperformed other contemporary methods for identification by offering scalable identification and reliable variant findings with no false-positive results. COWID typically processed each sample of raw sequencing data within 5 min at a cost of only US$0.01. The COWID source code is publicly available (https://github.com/hendrick0403/COWID) and can be accessed on any computer with Internet access. COWID is designed to be user-friendly; it can be implemented without prior programming knowledge. Therefore, COWID is a time-efficient tool that can be used during a pandemic.

The Ontology of Biological Attributes (OBA)-computational traits for the life sciences

Existing phenotype ontologies were originally developed to represent phenotypes that manifest as a character state in relation to a wild-type or other reference. However, these do not include the phenotypic trait or attribute categories required for the annotation of genome-wide association studies (GWAS), Quantitative Trait Loci (QTL) mappings or any population-focussed measurable trait data. The integration of trait and biological attribute information with an ever increasing body of chemical, environmental and biological data greatly facilitates computational analyses and it is also highly relevant to biomedical and clinical applications. The Ontology of Biological Attributes (OBA) is a formalised, species-independent collection of interoperable phenotypic trait categories that is intended to fulfil a data integration role. OBA is a standardised representational framework for observable attributes that are characteristics of biological entities, organisms, or parts of organisms. OBA has a modular design which provides several benefits for users and data integrators, including an automated and meaningful classification of trait terms computed on the basis of logical inferences drawn from domain-specific ontologies for cells, anatomical and other relevant entities. The logical axioms in OBA also provide a previously missing bridge that can computationally link Mendelian phenotypes with GWAS and quantitative traits. The term components in OBA provide semantic links and enable knowledge and data integration across specialised research community boundaries, thereby breaking silos.

2022 updates to the Rat Genome Database: a Findable, Accessible, Interoperable, and Reusable (FAIR) resource

The Rat Genome Database (RGD, https://rgd.mcw.edu) has evolved from simply a resource for rat genetic markers, maps, and genes, by adding multiple genomic data types and extensive disease and phenotype annotations and developing tools to effectively mine, analyze, and visualize the available data, to empower investigators in their hypothesis-driven research. Leveraging its robust and flexible infrastructure, RGD has added data for human and eight other model organisms (mouse, 13-lined ground squirrel, chinchilla, naked mole-rat, dog, pig, African green monkey/vervet, and bonobo) besides rat to enhance its translational aspect. This article presents an overview of the database with the most recent additions to RGD's genome, variant, and quantitative phenotype data. We also briefly introduce Virtual Comparative Map (VCMap), an updated tool that explores synteny between species as an improvement to RGD's suite of tools, followed by a discussion regarding the refinements to the existing PhenoMiner tool that assists researchers in finding and comparing quantitative data across rat strains. Collectively, RGD focuses on providing a continuously improving, consistent, and high-quality data resource for researchers while advancing data reproducibility and fulfilling Findable, Accessible, Interoperable, and Reusable (FAIR) data principles.

The Ontology of Biological Attributes (OBA) - Computational Traits for the Life Sciences

Existing phenotype ontologies were originally developed to represent phenotypes that manifest as a character state in relation to a wild-type or other reference. However, these do not include the phenotypic trait or attribute categories required for the annotation of genome-wide association studies (GWAS), Quantitative Trait Loci (QTL) mappings or any population-focused measurable trait data. Moreover, variations in gene expression in response to environmental disturbances even without any genetic alterations can also be associated with particular biological attributes. The integration of trait and biological attribute information with an ever increasing body of chemical, environmental and biological data greatly facilitates computational analyses and it is also highly relevant to biomedical and clinical applications. The Ontology of Biological Attributes (OBA) is a formalised, species-independent collection of interoperable phenotypic trait categories that is intended to fulfil a data integration role. OBA is a standardised representational framework for observable attributes that are characteristics of biological entities, organisms, or parts of organisms. OBA has a modular design which provides several benefits for users and data integrators, including an automated and meaningful classification of trait terms computed on the basis of logical inferences drawn from domain-specific ontologies for cells, anatomical and other relevant entities. The logical axioms in OBA also provide a previously missing bridge that can computationally link Mendelian phenotypes with GWAS and quantitative traits. The term components in OBA provide semantic links and enable knowledge and data integration across specialised research community boundaries, thereby breaking silos.

UniProt: the Universal Protein Knowledgebase in 2023

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 publication we describe enhancements made to our data processing pipeline and to our website to adapt to an ever-increasing information content. The number of sequences in UniProtKB has risen to over 227 million and we are working towards including a reference proteome for each taxonomic group. We continue to extract detailed annotations from the literature to update or create reviewed entries, while unreviewed entries are supplemented with annotations provided by automated systems using a variety of machine-learning techniques. In addition, the scientific community continues their contributions of publications and annotations to UniProt entries of their interest. Finally, we describe our new website (https://www.uniprot.org/), designed to enhance our users' experience and make our data easily accessible to the research community. This interface includes access to AlphaFold structures for more than 85% of all entries as well as improved visualisations for subcellular localisation of proteins.

Toward a data infrastructure for the Plant Cell Atlas

We review how a data infrastructure for the Plant Cell Atlas might be built using existing infrastructure and platforms. The Human Cell Atlas has developed an extensive infrastructure for human and mouse single cell data, while the European Bioinformatics Institute has developed a Single Cell Expression Atlas, that currently houses several plant data sets. We discuss issues related to appropriate ontologies for describing a plant single cell experiment. We imagine how such an infrastructure will enable biologists and data scientists to glean new insights into plant biology in the coming decades, as long as such data are made accessible to the community in an open manner.

Blood cell traits and risk of glaucoma: A two-sample mendelian randomization study

Importance: Glaucoma is the second leading cause of blindness in the world. The causal direction and magnitude of the association between blood cell traits and glaucoma is uncertain because of the susceptibility of observational studies to confounding and reverse causation. Objective: To explore whether there is a causal relationship of blood cell traits including white blood cell (WBC) count (WBCC) and its subtypes [basophil cell count (BASO), monocyte cell count (MONO), lymphocyte cell count (LYMPH), eosinophil cell count (EOS), neutrophil cell count (NEUT)], red blood cell (RBC) count (RBCC), red blood distribution width (RDW), platelet count (PLT), and plateletcrit (PCT) on glaucoma risk. Methods: A two-sample Mendelian randomization (MR) analysis was conducted. Genome-wide significant single nucleotide polymorphisms (SNPs) from published genome-wide association studies (GWAS) on human blood cell traits were utilized as exposure instruments and the dataset for outcome was from the GWAS summary data of glaucoma. In the univariable MR analysis, we examined the association between genetic evidence of blood cell traits and glaucoma. To further investigate the potential causal mechanisms underlying the observed association, we performed multivariable MR analysis with three models, taking into account the mediator effect of inflammation and oxidative stress. According to Bonferroni-corrected for the 10 exposures in 3 methods, the MR study yielded a statistically significant p-value of 0.0017. Results: Genetically BASO, PCT, LYMPH, and PLT were potentially positively associated with glaucoma in the European ancestry [BASO: Odds ratio (OR) = 1.00122, 95% confidence interval (CI), 1.00003-1.00242, p = 0.045; PCT: OR = 1.00078, 95% CI, 1.00012-1.00143, p = 0.019; LYMPH: OR = 1.00076, 95% CI, 1.00002-1.00151, p = 0.045; PLT: OR = 1.00065, 95% CI, 1.00006-1.00123, p = 0.030], There was insufficient evidence to support a causal association of MONO, NEUT, EOS, WBCC, RBCC and RDW (MONO: OR = 1.00050, p = 0.098; NEUT: OR = 1.00028, p = 0.524; EOS: OR = 1.00020, p = 0.562; WBCC: OR = 1.00008, p = 0.830; RBCC: OR = 0.99996, p = 0.920; RDW: OR = 0.99987, p = 0.734) with glaucoma. The multivariable MR with model 1, 2, and 3 demonstrated that BASO, PCT, LYMPH, and PLT were still potentially genetically associated with the risk of glaucoma. Conclusion: Our study reveals a genetic predisposition to higher LYMPH, BASO, PLT, and PCT are associated with a higher risk of glaucoma, whereas WBCC, MONO, EOS, NEUT, RBCC, and RDW are not associated with the occurrence of glaucoma. This finding also supports previous observational studies associating immune components with glaucoma, thus provide guidance on the predication and prevention for glaucoma.

The Plant Phenomics and Genomics Research Data Repository: An On-Premise Approach for FAIR-Compliant Data Acquisition

The FAIR data principle as a commitment to support long-term research data management is widely accepted in the scientific community. However, although many established infrastructures provide comprehensive and long-term stable services and platforms, a large quantity of research data is still hidden. Currently, high-throughput plant genomics and phenomics technologies are producing research data in abundance, the storage of which is not covered by established core databases. This concerns the data volume, for example, time series of images or high-resolution hyperspectral data; the quality of data formatting and annotation, e.g., with regard to structure and annotation specifications of core databases; uncovered data domains; or organizational constraints prohibiting primary data storage outside institutional boundaries. To share these potentially dark data in a FAIR way and master these challenges the ELIXIR Germany/de.NBI service Plant Genomic and Phenomics Research Data Repository (PGP) implements an on-premise approach, which allows research data to be kept in place and wrapped in FAIR-aware software infrastructure. In this chapter, the e!DAL infrastructure software and the PGP repository are presented as best practice on how to easily setup FAIR-compliant and intuitive research data services.

Evolutionary Conserved Short Linear Motifs Provide Insights into the Cellular Response to Stress

Short linear motifs (SLiMs) are evolutionarily conserved functional modules of proteins composed of 3 to 10 residues and involved in multiple cellular functions. Here, we performed a search for SLiMs that exert sequence similarity to two segments of alpha-fetoprotein (AFP), a major mammalian embryonic and cancer-associated protein. Biological activities of the peptides, LDSYQCT (AFP_14-20) and EMTPVNPGV (GIP-9), have been previously confirmed under in vitro and in vivo conditions. In our study, we retrieved a vast array of proteins that contain SLiMs of interest from both prokaryotic and eukaryotic species, including viruses, bacteria, archaea, invertebrates, and vertebrates. Comprehensive Gene Ontology enrichment analysis showed that proteins from multiple functional classes, including enzymes, transcription factors, as well as those involved in signaling, cell cycle, and quality control, and ribosomal proteins were implicated in cellular adaptation to environmental stress conditions. These include response to oxidative and metabolic stress, hypoxia, DNA and RNA damage, protein degradation, as well as antimicrobial, antiviral, and immune response. Thus, our data enabled insights into the common functions of SLiMs evolutionary conserved across all taxonomic categories. These SLiMs can serve as important players in cellular adaptation to stress, which is crucial for cell functioning.

Upregulated ENC1 predicts unfavorable prognosis and correlates with immune infiltration in endometrial cancer

A better knowledge of the molecular process behind uterine corpus endometrial carcinoma (UCEC) is important for prognosis prediction and the development of innovative targeted gene therapies. The purpose of this research is to discover critical genes associated with UCEC. We analyzed the gene expression profiles of TCGA-UCEC and GSE17025, respectively, using Weighted Gene Co-expression Network Analysis (WGCNA) and differential gene expression analysis. From four sets of findings, a total of 95 overlapping genes were retrieved. On the 95 overlapping genes, KEGG pathway and GO enrichment analysis were conducted. Then, we mapped the PPI network of 95 overlapping genes using the STRING database. Twenty hub genes were evaluated using the Cytohubba plugin, including NR3C1, ATF3, KLF15, THRA, NR4A1, FOSB, PER3, HLF, NTRK3, EGR3, MAPK13, ARNTL2, PKM2, SCD, EIF5A, ADHFE1, RERGL, TUB, and ENC1. The expression levels of NR3C1, PKM2, and ENC1 were shown to be adversely linked with the survival time of UCEC patients using univariate Cox regression analysis and Kaplan-Meier survival calculation. ENC1 were also overexpressed in UCEC tumor tissues or cell lines, as shown by quantitative real-time PCR and Western blotting. Then we looked into it further and discovered that ENC1 expression was linked to tumor microenvironment and predicted various immunological checkpoints. In conclusion, our data indicate that ENC1 may be required for the development of UCEC and may serve as a future biomarker for diagnosis and therapy.

IAnimal: a cross-species omics knowledgebase for animals

With the exponential growth of multi-omics data, its integration and utilization have brought unprecedented opportunities for the interpretation of gene regulation mechanisms and the comprehensive analyses of biological systems. IAnimal (https://ianimal.pro/), a cross-species, multi-omics knowledgebase, was developed to improve the utilization of massive public data and simplify the integration of multi-omics information to mine the genetic mechanisms of objective traits. Currently, IAnimal provides 61 191 individual omics data of genome (WGS), transcriptome (RNA-Seq), epigenome (ChIP-Seq, ATAC-Seq) and genome annotation information for 21 species, such as mice, pigs, cattle, chickens, and macaques. The scale of its total clean data has reached 846.46 TB. To better understand the biological significance of omics information, a deep learning model for IAnimal was built based on BioBERT and AutoNER to mine 'gene' and 'trait' entities from 2 794 237 abstracts, which has practical significance for comprehending how each omics layer regulates genes to affect traits. By means of user-friendly web interfaces, flexible data application programming interfaces, and abundant functional modules, IAnimal enables users to easily query, mine, and visualize characteristics in various omics, and to infer how genes play biological roles under the influence of various omics layers.

gutMDisorder v2.0: a comprehensive database for dysbiosis of gut microbiota in phenotypes and interventions

Gut microbiota plays a significant role in maintaining host health, and conversely, disorders potentially lead to dysbiosis, an imbalance in the composition of the gut microbial community. Intervention approaches, such as medications, diets, and several others, also alter the gut microbiota in either a beneficial or harmful direction. In 2020, the gutMDisorder was developed to facilitate researchers in the investigation of dysbiosis of gut microbes as occurs in various disorders as well as with therapeutic interventions. The database has been updated this year, following revision of previous publications and newly published reports to manually integrate confirmed associations under multitudinous conditions. Additionally, the microbial contents of downloaded gut microbial raw sequencing data were annotated, the metadata of the corresponding hosts were manually curated, and the interactive charts were developed to enhance visualization. The improvements have assembled into gutMDisorder v2.0, a more advanced search engine and an upgraded web interface, which can be freely accessed via http://bio-annotation.cn/gutMDisorder/.

TSomVar: a tumor-only somatic and germline variant identification method with random forest

Somatic variants act as critical players during cancer occurrence and development. Thus, an accurate and robust method to identify them is the foundation of cutting-edge cancer genome research. However, due to low accessibility and high individual-/sample-specificity of the somatic variants in tumor samples, the detection is, to date, still crammed with challenges, particularly when lacking paired normal samples as control. To solve this burning issue, we developed a tumor-only somatic and germline variant identification method (TSomVar) using the random forest algorithm established on sample-specific variant datasets derived from genotype imputation, reads-mapping level annotation and functional annotation. We trained TSomVar by using genomic variant datasets of three major cancer types: colorectal cancer, hepatocellular carcinoma and skin cutaneous melanoma. Compared with existing tumor-only somatic variant identification tools, TSomVar shows excellent performances in somatic variant detection with higher accuracy and better capability of recalling for test datasets from colorectal cancer and skin cutaneous melanoma. In addition, TSomVar is equipped with the competence of accurately identifying germline variants in tumor samples. Taken together, TSomVar will undoubtedly facilitate and revolutionize somatic variant explorations in cancer research.

Integrating and formatting biomedical data as pre-calculated knowledge graph embeddings in the Bioteque

Biomedical data is accumulating at a fast pace and integrating it into a unified framework is a major challenge, so that multiple views of a given biological event can be considered simultaneously. Here we present the Bioteque, a resource of unprecedented size and scope that contains pre-calculated biomedical descriptors derived from a gigantic knowledge graph, displaying more than 450 thousand biological entities and 30 million relationships between them. The Bioteque integrates, harmonizes, and formats data collected from over 150 data sources, including 12 biological entities (e.g., genes, diseases, drugs) linked by 67 types of associations (e.g., ‘drug treats disease’, ‘gene interacts with gene’). We show how Bioteque descriptors facilitate the assessment of high-throughput protein-protein interactome data, the prediction of drug response and new repurposing opportunities, and demonstrate that they can be used off-the-shelf in downstream machine learning tasks without loss of performance with respect to using original data. The Bioteque thus offers a thoroughly processed, tractable, and highly optimized assembly of the biomedical knowledge available in the public domain.

Biomedical data is accumulating at a fast pace and integrating it into a unified framework is a major challenge. Here, the authors present a resource that contains pre-calculated biomedical descriptors derived from a very large knowledge graph.

Identification and expression analysis of xyloglucan endotransglucosylase/hydrolase (XTH) family in grapevine (Vitis vinifera L.)

Xyloglucan endotransglucosylases/hydrolases (XTH) are key enzymes in cell wall reformulation. They have the dual functions of catalyzing xyloglucan endotransglucosylase (XET) and xyloglucan endonuclease (XEH) activity and play a crucial role in the responses against abiotic stresses, such as drought, salinity, and freezing. However, a comprehensive analysis of the XTH family and its functions in grapevine (Vitis vinifera L.) has not yet been completed. In this study, 34 XTHs were identified in the whole grapevine genome and then named according to their distribution on chromosomes. Based on a phylogenetic analysis including Arabidopsis XTHs, the VvXTHs were classified into three groups. Cis-element analysis indicated that these family members are related to most abiotic stresses. We further selected 14 VvXTHs from different groups and then examined their transcription levels under drought and salt stress. The results indicated that the transcription levels of selected VvXTHs in the leaves and roots presented the largest changes, suggesting that VvXTHs are likely to take part in the responses to drought and salt stress in grapevines. These results provide useful evidence for the further investigation of VvXTHs function in response to abiotic stresses in grapevine.

The 2022 Nucleic Acids Research database issue and the online molecular biology database collection

The 2022 Nucleic Acids Research Database Issue contains 185 papers, including 87 papers reporting on new databases and 85 updates from resources previously published in the Issue. Thirteen additional manuscripts provide updates on databases most recently published elsewhere. Seven new databases focus specifically on COVID-19 and SARS-CoV-2, including SCoV2-MD, the first of the Issue's Breakthrough Articles. Major nucleic acid databases reporting updates include MODOMICS, JASPAR and miRTarBase. The AlphaFold Protein Structure Database, described in the second Breakthrough Article, is the stand-out in the protein section, where the Human Proteoform Atlas and GproteinDb are other notable new arrivals. Updates from DisProt, FuzDB and ELM comprehensively cover disordered proteins. Under the metabolism and signalling section Reactome, ConsensusPathDB, HMDB and CAZy are major returning resources. In microbial and viral genomes taxonomy and systematics are well covered by LPSN, TYGS and GTDB. Genomics resources include Ensembl, Ensembl Genomes and UCSC Genome Browser. Major returning pharmacology resource names include the IUPHAR/BPS guide and the Therapeutic Target Database. New plant databases include PlantGSAD for gene lists and qPTMplants for post-translational modifications. The entire Database Issue is freely available online on the Nucleic Acids Research website (https://academic.oup.com/nar). Our latest update to the NAR online Molecular Biology Database Collection brings the total number of entries to 1645. Following last year's major cleanup, we have updated 317 entries, listing 89 new resources and trimming 80 discontinued URLs. The current release is available at http://www.oxfordjournals.org/nar/database/c/.