Ruikun Xue

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.

The National Genomics Data Center (NGDC), as part of the China National Center for Bioinformation (CNCB), provides a suite of database resources for worldwide researchers. As multi-omics big data and artificial intelligence reshape the paradigm of biology research, CNCB-NGDC continuously updates its database resources to enhance data usability, foster knowledge discovery, and support data-driven innovative research. Over the past year, notable progress has been achieved in expanding the scope of high-quality multi-omics datasets, building new database resources, and optimizing extant core resources. Notably, the launch of BIG Search enables cross-database search services for large-scale biological data platforms, including NGDC, National Center for Biotechnology Information (NCBI), and European Bioinformatics Institute (EBI). Additionally, several new resources have been developed, covering genome and variation (Hiland Resource, TOAnnoPriDB), expression (TEDD), single-cell omics (PreDigs, scMultiModalMap, TE-SCALE), radiomics (TonguExpert), health and disease (CAVDdb, IDP, MTB-KB, ResMicroDb), biodiversity and biosynthesis (SugarcaneOmics), as well as research tools (Dingent, miMatch, OmniExtract, RDBSB, xMarkerFinder). All these resources and services are freely accessible at https://ngdc.cncb.ac.cn.

Pan-genome analysis is a crucial method for studying genomic dynamics. By creating pan-genome maps for prokaryotic organisms, we can gain valuable insights into their genetic diversity and ecological adaptability. However, current analytical methods often struggle to balance accuracy and computational efficiency, and they tend to provide primarily qualitative results. This study introduces PGAP2, an integrated software package that simplifies various processes, including data quality control, pan-genome analysis, and result visualization. PGAP2 facilitates the rapid and accurate identification of orthologous and paralogous genes by employing fine-grained feature analysis within constrained regions. Our systematic evaluation with simulated and gold-standard datasets demonstrates that PGAP2 is more precise, robust, and scalable than state-of-the-art tools for large-scale pan-genome data. Furthermore, PGAP2 introduces four quantitative parameters derived from the distances between or within clusters, enabling detailed characterization of homology clusters. Finally, we validate our quantitative findings by applying PGAP2 to construct a pan-genomic profile of 2794 zoonotic Streptococcus suis strains. This analysis offers new insights into the genetic diversity of S. suis, thereby enhancing our understanding of its genomic structure. PGAP2 is freely available at https://github.com/bucongfan/PGAP2 . Prokaryotic pan-genome analysis is crucial for understanding microbial diversity, however current analytical methods often struggle to balance accuracy and computational efficiency. Here the authors present a more precise, robust and scalable toolkit for large-scale pan genome analysis.