Cardiovascular disease (CVD) is the leading cause of illness and death worldwide. Numerous studies have been conducted into the underlying mechanisms and molecular characteristics of CVD using various omics approaches. However, there is still a need for comprehensive resources on CVD. To fill this gap, we present the CVD Atlas, accessed at https://ngdc.cncb.ac.cn/cvd. This database compiles knowledge and information from manual curation, large-scale data analysis, and existing databases, utilizing multi-omics data to understand CVDs comprehensively. The current version of CVD Atlas contains 215,333 associations gathered from 308 publications, 652 datasets and 7 databases. It covers 190 diseases and 44 traits across multiple omics levels. Additionally, it provides an interactive knowledge graph that integrates disease-gene associations and two types of analysis tools, offering an engaging way to query and display relationships. CVD Atlas also features a user-friendly web interface that allows users to easily browse, search, and download all association information, research metadata, and annotation details. In conclusion, CVD Atlas is a valuable resource that enhances the accessibility and utility of knowledge and information related to CVD, benefiting human health and CVD research communities.

Single-cell transcriptome-wide association studies (scTWAS) is a new method for conducting TWAS analysis at the cellular level to identify gene-trait associations with higher precision. This approach helps overcome the challenge of interpreting cell-type heterogeneity in traditional TWAS results. As the field of scTWAS rapidly advances, there is a growing need for additional database platforms to integrate this wealth of data and knowledge effectively. To address this gap, we present scTWAS Atlas (https://ngdc.cncb.ac.cn/sctwas/), a comprehensive database of scTWAS information integrating literature curation and data analysis. The current version of scTWAS Atlas amasses 2,765,211 associations encompassing 34 traits, 30 cell types, 9 cell conditions and 16,470 genes. The database features visualization tools, including an interactive knowledge graph that integrates single-cell expression quantitative trait loci (sc-eQTL) and scTWAS associations to build a multi-omics level regulatory network at the cellular level. Additionally, scTWAS Atlas facilitates cross-cell-type analysis, highlighting cell-type-specific and shared TWAS genes. The database is designed with user-friendly interfaces and allows for easy browsing, searching, and downloading of relevant information. Overall, scTWAS Atlas is instrumental in exploring the genetic regulatory mechanisms at the cellular level and shedding light on the role of various cell types in biological processes, offering novel insights for human health research.

The respiratory microbiome plays an important role in maintaining human health. Despite the rapid growth of literature and publicly accessible data on the respiratory microbiome, a large-scale, well-curated database is still lacking. Here, we introduced ResMicroDb, a comprehensive database and analysis platform for the human respiratory microbiome. ResMicroDb contains 106 464 samples from 514 projects, spanning 10 sample sites, 72 sample types, and 146 phenotypes. Notably, it includes ~7-fold more respiratory samples than existing multi-body-site resources. To improve the reusability and accessibility of data, a standardized bioinformatics pipeline was employed to generate taxonomic profiles, and 32 metadata fields were manually curated. ResMicroDb also provides 11 908 microbe–disease associations, identified from 132 case–control studies, to deepen the understanding of microbiome–disease relationships. Additionally, ResMicroDb offers three tools for in-depth analysis: “Microbiome Composition” for visualizing taxonomic profiles of user-selected samples; “Sample Similarity Search” for inferring the characteristics of new samples by comparing them to the database based on similarity; and “Cross-study Analysis” for identifying common and specific microbial characteristics across cohorts, phenotypes and sample sites. ResMicroDb serves as a versatile and valuable resource for advancing a broad spectrum of respiratory microbiome research and clinical relevance. ResMicroDb is freely accessed at https://resmicrodb.cncb.ac.cn.

Spatial transcriptomics enables investigation of cell–cell communication (CCC) while preserving spatial context, yet large-scale resources that organize spatial CCC results across studies and make them readily searchable, visualizable, and comparable remain limited. Here, we present CCCView, a knowledge platform integrating 699 spatial transcriptomics samples and 7,473,444 inferred CCC interactions across 24 tissues, 182 annotated cell types, and 27 biological conditions in human and mouse. CCCView uses a unified cell-type annotation workflow and organizes results from three complementary CCC-inference approaches—COMMOT, CellNEST, and SpatialDM—while preserving method-specific outputs. The platform provides interactive search, browsing, visualization, disease-oriented exploration, data download, and online analysis functions. For cross-sample exploration, CCCView also includes MuCIA, a Tweedie regression-based statistical module for comparing inferred interaction strength across user-defined sample groups. A glioblastoma use case illustrates how CCCView can connect spatial cell-type context, inferred communication patterns, and condition-associated signals to generate biologically interpretable hypotheses. CCCView provides an integrated resource for systematic exploration of spatial transcriptomic CCC and for reuse of precomputed communication results across heterogeneous datasets.

Proteome-wide association studies (PWASs) have emerged as a powerful approach for investigating associations between genetically regulated proteins and complex traits, offering valuable insights into the mechanisms linking genetic variation, protein abundance, and phenotypes. Despite the rapid expansion of PWAS research, no resource currently exists to systematically collect, integrate, and organize publicly accessible PWAS findings and related knowledge, limiting efficient use and exploration of these valuable assets. To address this urgent need, we introduce PWAS Atlas (https://ngdc.cncb.ac.cn/pwas/), a comprehensive knowledgebase that consolidates literature-curated information and large-scale, standardized PWAS analyses. The PWAS Atlas encompasses 40 609 associations among 4585 genes/proteins and 763 traits, derived from 118 publications and 841 GWAS datasets. It implements interactive knowledge graphs that reveal associations among genes, proteins, tissues, and traits, as well as online comparative analysis tools that enable systematic comparisons across multiple genes or traits. PWAS Atlas also provides a user-friendly web platform for browsing, searching, exploring, and downloading data. By enabling standardized retrieval, visualization, comparison, and reuse of PWAS information, PWAS Atlas serves as an extensive resource for investigating protein-trait associations and deriving insights into disease biology.

Cerebral small vessel disease (CSVD) is a major contributor to stroke and dementia, and it endangers the health of older individuals (>50 years old). Nevertheless, its clinical diagnosis predominantly depends on radiography. Moreover, omics studies and effective biomarkers for CSVD are still limited, and their pathogenesis has not been comprehensively clarified. To facilitate an in-depth understanding of CSVD at the molecular level and to characterize potential risk factors for this disease, we conducted a series of systematic transcriptome studies using peripheral blood samples from 91 Chinese patients with CSVD. By profiling the transcriptome heterogeneity between patients with CSVD and healthy individuals and conducting quantitative real-time PCR to validate differentially expressed genes, we established a comprehensive transcriptome map of CSVD and identified several statistically significant potential biomarkers for this disease. Our comparative analysis between dementia and non-dementia subgroups in CSVD patients highlights several differentially distributed neuroimaging and clinical features, such as total white matter hyperintensity severity, CSVD burden, neutrophil counts, and triglyceride levels, among others. More importantly, a CSVD-prediction model and a dementia-forecasting model have been constructed through machine learning methods, which achieve the average F1 scores of 0.93 and 0.73, respectively. Consequently, both models are anticipated to provide effective support for clinical predictions and diagnoses. In summary, this study delineates the transcriptome characteristics of CSVD, laying a foundation for research and clinical insights pertaining to CSVD.