Plant genomic resources at National Genomics Data Center:assisting in data-driven breeding applications

Genomic data serve as an invaluable resource for unraveling the intricacies of the higher plant systems,including the constituent elements within and among species.Through various efforts in genomic data archiving,integrative analysis and value-added curation,the National Genomics Data Center(NGDC),which is a part of the China National Center for Bioinformation(CNCB),has successfully established and currently maintains a vast amount of database resources.This dedicated initiative of the NGDC facilitates a data-rich ecosystem that greatly strengthens and supports genomic research efforts.Here,we present a comprehensive overview of central repositories dedicated to archiving,presenting,and sharing plant omics data,introduce knowledgebases focused on variants or gene-based functional insights,highlight species-specific multiple omics database resources,and briefly review the online application tools.We intend that this review can be used as a guide map for plant researchers wishing to select effective data resources from the NGDC for their specific areas of study.

Spatial search weighting information contained in cell velocity distribution

Cell migration plays a significant role in physiological and pathological processes.Understanding the characteristics of cell movement is crucial for comprehending biological processes such as cell functionality,cell migration,and cell–cell interactions.One of the fundamental characteristics of cell movement is the specific distribution of cell speed,containing valuable information that still requires comprehensive understanding.This article investigates the distribution of mean velocities along cell trajectories,with a focus on optimizing the efficiency of cell food search in the context of the entire colony.We confirm that the specific velocity distribution in the experiments corresponds to an optimal search efficiency when spatial weighting is considered.The simulation results indicate that the distribution of average velocity does not align with the optimal search efficiency when employing average spatial weighting.However,when considering the distribution of central spatial weighting,the specific velocity distribution in the experiment is shown to correspond to the optimal search efficiency.Our simulations reveal that for any given distribution of average velocity,a specific central spatial weighting can be identified among the possible central spatial weighting that aligns with the optimal search strategy.Additionally,our work presents a method for determining the spatial weights embedded in the velocity distribution of cell movement.Our results have provided new avenues for further investigation of significant topics,such as relationship between cell behavior and environmental conditions throughout their evolutionary history,and how cells achieve collective cooperation through cell-cell communication.

Characteristics of cell motility during cell collision

Quantitative examination of cellular motion and intercellullar interactions possesses substantial relevance for both biology and medicine.However,the effects of intercellular interactions during cellular locomotion remain under-explored in experimental research.As such,this study seeks to bridge this research gap,adopting Dictyostelium discoideum(Dicty)cells as a paradigm to investigate variations in cellular motion during reciprocal collisions.We aim to attain a comprehensive understanding of how cell interactions influence cell motion.By observing and processing the motion trajectories of colliding cells under diverse chemical environments,we calculated the diffusion coefficient(D)and the persistence time(τ),using mean square displacement.Our analysis of the relationship dynamics between D andτprior to the collisions reveals intricate and non-monotonic alterations in cell movements during collisions.By quantitatively scrutinizing theτtrend,we were able to categorize the cellular responses to interactions under different conditions.Importantly,we ascertained that the effect of cell interactions during collisions in Dicty cells emulates a classical sigmoid function.This discovery suggests that cellular responses might comply with a pattern akin to the Weber–Fechner law.

Magnetic resonance imaging-based radiomics model for preoperative assessment of risk stratification in endometrial cancer

BACKGROUND Preoperative risk stratification is significant for the management of endometrial cancer(EC)patients.Radiomics based on magnetic resonance imaging(MRI)in combination with clinical features may be useful to predict the risk grade of EC.AIM To construct machine learning models to predict preoperative risk stratification of patients with EC based on radiomics features extracted from MRI.METHODS The study comprised 112 EC patients.The participants were randomly separated into training and validation groups with a 7:3 ratio.Logistic regression analysis was applied to uncover independent clinical predictors.These predictors were then used to create a clinical nomogram.Extracted radiomics features from the T2-weighted imaging and diffusion weighted imaging sequences of MRI images,the Mann-Whitney U test,Pearson test,and least absolute shrinkage and selection operator analysis were employed to evaluate the relevant radiomic features,which were subsequently utilized to generate a radiomic signature.Seven machine learning strategies were used to construct radiomic models that relied on the screening features.The logistic regression method was used to construct a composite nomogram that incorporated both the radiomic signature and clinical independent risk indicators.RESULTS Having an accuracy of 0.82 along with an area under the curve(AUC)of 0.915[95%confidence interval(CI):0.806-0.986],the random forest method trained on radiomics characteristics performed better than expected.The predictive accuracy of radiomics prediction models surpassed that of both the clinical nomogram(AUC:0.75,95%CI:0.611-0.899)and the combined nomogram(AUC:0.869,95%CI:0.702-0.986)that integrated clinical parameters and radiomic signature.CONCLUSION The MRI-based radiomics model may be an effective tool for preoperative risk grade prediction in EC patients.

From BIG Data Center to China National Center for Bioinformation

Background Big data challenges In the late 1980s and early 1990s,three major international biological data centers were created:the DNA Database of Japan(DDBJ)[1],the European Bioinformatics Institute(EMBL-EBI)in the United Kingdom(UK)[2],and the National Center for Biotechnology Information(NCBI)in the United States(US)[3].

Global cold-chain related SARS-CoV-2 transmission identified by pandemic-scale phylogenomics

DEAR EDITOR,The COVID-19 pandemic caused by SARS-CoV-2 continues to pose a tremendous threat to human society. SARS-CoV-2is airborne and transmits primarily through social contact;however, whether cold chain-related transmission has occurred remains highly debated(Han & Liu, 2022;Lewis,2021;Ma et al., 2021;Mallapaty et al., 2021;Pang et al.,2020;Wu et al., 2021). Here, we present a novel method and identify two transmission routes based on lineage-specific reductions in the SARS-CoV-2 evolutionary rate.

Characterization of prognosis and immune infiltration by a novel glutamine metabolism-related model in cutaneous melanoma

Glutamine metabolism(GM)plays an important role in tumor growth and proliferation.Skin cutaneous melanoma(SKCM)is a glutamine-dependent cancer.However,the molecular characteristics and action mechanism of GM on SKCM remain unclear.Therefore,we aimed to explore the effects of GM-related genes on survival,clinicopathological characteristics,and the tumor microenvironment in SKCM.In this study,682 SKCM samples were obtained from the Cancer Genome Atlas(TCGA)and Gene Expression Omnibus(GEO)databases.Consensus clustering was used to classify SKCM samples into distinct subtypes based on 41 GM-related genes.Differences in survival,immune infiltration,clinical characteristics,and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathways as well as differentially expressed genes(DEGs)between subgroups were evaluated.A prognostic model was constructed according to prognostic DEGs.Differential analyses in survival,immune infiltration,tumor microenvironment(TME),tumor mutation burden(TMB),stemness,and drug sensitivity between risk groups were conducted.We identified two distinct GM-related subtypes on SKCM and found that GM-related gene alterations were associated with survival probability,clinical features,biological function,and immune infiltration.Then a risk model based on six DEGs(IL18,SEMA6A,PAEP,TNFRSF17,AIM2,and CXCL10)was constructed and validated for predicting overall survival in SKCM patients.The results showed that the risk score was negatively correlated with CD8+T cells,activated CD4+memory T cells,M1 macrophages,andγδT cells.The group with a low-risk score was accompanied by a better survival rate with higher TME scores and lower stemness index.Moreover,the group with high-and low-risk score had a significant difference with the sensitivity of 75 drugs(p<0.001).Overall,distinct subtypes in SKCM patients based on GM-related genes were identified and the risk model was constructed,which might contribute to prognosis prediction,guide clinical therapy,and develop novel therapeutic strategies.

GenBase: A Nucleotide Sequence Database

The rapid advancement of sequencing technologies poses challenges in managing the large volume and exponential growth of sequence data efficiently and on time.To address this issue,we present GenBase(https://ngdc.cncb.ac.cn/genbase),an open-access data repository that follows the International Nucleotide Sequence Database Collaboration(INSDC)data standards and structures,for efficient nucleotide sequence archiving,searching,and sharing.As a core resource within the National Genomics Data Center(NGDC)of the China National Center for Bioinformation(CNCB;https://ngdc.cncb.ac.cn),GenBase offers bilingual submission pipeline and services,as well as local submission assistance in China.GenBase also provides a unique Excel format for metadata description and feature annotation of nucleotide sequences,along with a real-time data validation system to streamline sequence submissions.As of April 23,2024,GenBase received 68,251 nucleotide sequences and 689,574 annotated protein sequences across 414 species from 2319 submissions.Out of these,63,614(93%)nucleotide sequences and 620,640(90%)annotated protein sequences have been released and are publicly accessible through GenBase’s web search system,File Transfer Protocol(FTP),and Application Programming Interface(API).Additionally,in collaboration with INSDC,GenBase has constructed an effective data exchange mechanism with GenBank and started sharing released nucleotide sequences.Furthermore,GenBase integrates all sequences from GenBank with daily updates,demonstrating its commitment to actively contributing to global sequence data management and sharing.

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/).

RCoV19:A One-stop Hub for SARS-CoV-2 Genome Data Integration,Variant Monitoring,and Risk Pre-warning

The Resource for Coronavirus 2019(RCoV19)is an open-access information resource dedicated to providing valuable data on the genomes,mutations,and variants of the severe acute respiratory syndrome coronavirus 2(SARS-CoV-2).In this updated implementation of RCoV19,we have made significant improvements and advancements over the previous version.Firstly,we have implemented a highly refined genome data curation model.This model now features an automated integration pipeline and optimized curation rules,enabling efficient daily updates of data in RCoV19.Secondly,we have developed a global and regional lineage evolution monitoring platform,alongside an outbreak risk pre-warning system.These additions provide a comprehensive understanding of SARS-CoV-2 evolution and transmission patterns,enabling better preparedness and response strategies.Thirdly,we have developed a powerful interactive mutation spectrum comparison module.This module allows users to compare and analyze mutation patterns,assisting in the detection of potential new lineages.Furthermore,we have incorporated a comprehensive knowledgebase on mutation effects.This knowledgebase serves as a valuable resource for retrieving information on the functional implications of specific mutations.In summary,RCoV19 serves as a vital scientific resource,providing access to valuable data,relevant information,and technical support in the global fight against COVID-19.The complete contents of RCoV19 are available to the public at https://ngdc.cncb.ac.cn/ncov/.

Toward A New Paradigm of Genomics Research——Celebration of the 20th Anniversary of Beijing Institute of Genomics

Twenty years after the completion and forty years after the proposal of the Human Genome Project(HGP),genomics,together with its twin field-bioinformatics,has entered a new paradigm,where its bioscience-related,discipline-centric applications have been creating many new research frontiers.Beijing Institute of Genomics(BIG),now also known as China National Center for Bioinformation(CNCB),will play key roles in supporting and participating in these frontier research activities.On the 20th anniversary of the establishment of BIG,we provide a brief retrospective of its historic events and ascertain strategic research directions with a broader vision for future genomics,where digital genome,digital medicine,and digital health are so structured to meet the needs of human life and healthcare,as well as their related metaverses.

DNA methylation clocks for estimating biological age in Chinese cohorts

Epigenetic clocks are accurate predictors of human chronological age based on the analysis of DNA methylation(DNAm)at specific CpG sites.However,a systematic comparison between DNA methylation data and other omics datasets has not yet been performed.Moreover,available DNAm age predictors are based on datasets with limited ethnic representation.To address these knowledge gaps,we generated and analyzed DNA methylation datasets from two independent Chinese cohorts,revealing age-related DNAm changes.Additionally,a DNA methylation aging clock(iCAS-DNAmAge)and a group of DNAm-based multi-modal clocks for Chinese individuals were developed,with most of them demonstrating strong predictive capabilities for chronological age.The clocks were further employed to predict factors influencing aging rates.The DNAm aging clock,derived from multi-modal aging features(compositeAge-DNAmAge),exhibited a close association with multi-omics changes,lifestyles,and disease status,underscoring its robust potential for precise biological age assessment.Our findings offer novel insights into the regulatory mechanism of age-related DNAm changes and extend the application of the DNAm clock for measuring biological age and aging pace,providing the basis for evaluating aging intervention strategies.

Insights into dammarane-type triterpenoid saponin biosynthesis from the telomere-totelomere genome of Gynostemma pentaphyllum

Dear Editor,Gynostemma pentaphyllum(Thunb.)Makino,a perennial climbing vine in the Cucurbitaceae family,has been widely used in traditional medicine for over 600 years(Blumert and Liu,1999).It serves as a valuable natural source of over 200 dammaranetype saponins with notable bioactive properties,including anti-cancer,cardioprotective,hepatoprotective,neuroprotective,and anti-diabetic activities(Li et al.,2016;Nguyen et al.,2021).

The Genome Sequence Archive Family: Toward Explosive Data Growth and Diverse Data Types

The Genome Sequence Archive(GSA)is a data repository for archiving raw sequence data,which provides data storage and sharing services for worldwide scientific communities.Considering explosive data growth with diverse data types,here we present the GSA family by expanding into a set of resources for raw data archive with different purposes,namely,GSA(https://ngdc.cncb.ac.cn/gsa/),GSA for Human(GSA-Human,https://ngdc.cncb.ac.cn/gsa-human/),and Open Archive for Miscellaneous Data(OMIX,https://ngdc.cncb.ac.cn/omix/).Compared with the 2017 version,GSA has been significantly updated in data model,online functionalities,and web interfaces.GSA-Human,as a new partner of GSA,is a data repository specialized in human genetics-related data with controlled access and security.OMIX,as a critical complement to the two resources mentioned above,is an open archive for miscellaneous data.Together,all these resources form a family of resources dedicated to archiving explosive data with diverse types,accepting data submissions from all over the world,and providing free open access to all publicly available data in support of worldwide research activities.

Genome Warehouse: A Public Repository Housing Genome-scale Data

The Genome Warehouse(GWH)is a public repository housing genome assembly data for a wide range of species and delivering a series of web services for genome data submission,storage,release,and sharing.As one of the core resources in the National Genomics Data Center(NGDC),part of the China National Center for Bioinformation(CNCB;https://ngdc.cncb.ac.cn),GWH accepts both full and partial(chloroplast,mitochondrion,and plasmid)genome sequences with different assembly levels,as well as an update of existing genome assemblies.For each assembly,GWH collects detailed genome-related metadata of biological project,biological sample,and genome assembly,in addition to genome sequence and annotation.To archive high-quality genome sequences and annotations,GWH is equipped with a uniform and standardized procedure for quality control.Besides basic browse and search functionalities,all released genome sequences and annotations can be visualized with JBrowse.By May 21,2021,GWH has received 19,124 direct submissions covering a diversity of 1108 species and has released 8772 of them.Collectively,GWH serves as an important resource for genomescale data management and provides free and publicly accessible data to support research activities throughout the world.GWH is publicly accessible at https://ngdc.cncb.ac.cn/gwh.

IC4R-2.0:Rice Genome Reannotation Using Massive RNA-seq Data

Genome reannotation aims for complete and accurate characterization of gene models and thus is of critical significance for in-depth exploration of gene function.Although the availability of massive RNA-seq data provides great opportunities for gene model refinement,few efforts have been made to adopt these precious data in rice genome reannotation.Here we reannotate the rice(Oryza sativa L.ssp.japonica)genome based on integration of large-scale RNA-seq data and release a new annotation system IC4 R-2.0.In general,IC4 R-2.0 significantly improves the completeness of gene structure,identifies a number of novel genes,and integrates a variety of functional annotations.Furthermore,long non-coding RNAs(lncRNAs)and circular RNAs(circRNAs)are systematically characterized in the rice genome.Performance evaluation shows that compared to previous annotation systems,IC4 R-2.0 achieves higher integrity and quality,primarily attributable to massive RNA-seq data applied in genome annotation.Consequently,we incorporate the improved annotations into the Information Commons for Rice(IC4 R),a database integrating multiple omics data of rice,and accordingly update IC4 R by providing more user-friendly web interfaces and implementing a series of practical online tools.Together,the updated IC4 R,which is equipped with the improved annotations,bears great promise for comparative and functional genomic studies in rice and other monocotyledonous species.The IC4 R-2.0 annotation system and related resources are freely accessible at http://ic4 r.org/.

The Elements of Data Sharing

Data and their tailored characteristics are inheritable and longlived,surpassing their analyzed results and conclusions regardless if they are produced by their generators or users.Aside from designing experiments for the new acquisition,scientific researchers always begin with a thorough synthesis of the existing data,especially those that have been demonstrated authentic and timely.

LSD 4.0:an improved database for comparative studies of leaf senescence

Leaf senescence is the final stage of leaf development and involves the active degradation and dynamic transfer of its cellular components to newly growing and storage tissues,which contributes to plant fitness(Gan and Amasino,1997;Lim et al.,2007).The genetic modification of leaf senescence has emerged as a promising strategy for improving nutritional traits and stress tolerance in plants(Rivero et al.,2007).Efforts to dissect the molecular mechanisms underpinning leaf senescence reveal that it is a highly coordinated process regulated by a large number of senescence-associated genes(SAGs)(Guo and Gan,2005;Lim et al.,2007).Functional studies of these SAG genes through reverse genetics strategies and identification of senescence-altered mutants through forward genetic screening have deepened the understanding of leaf senescence(Guo et al.,2021).

OBIA:An Open Biomedical Imaging Archive

With the development of artificial intelligence(AI)technologies,biomedical imaging data play an important role in scientific research and clinical application,but the available resources are limited.Here we present Open Biomedical Imaging Archive(OBIA),a repository for archiving biomedical imaging and related clinical data.OBIA adopts five data objects(Collection,Individual,Study,Series,and Image)for data organization,and accepts the submission of biomedical images of multiple modalities,organs,and diseases.In order to protect personal privacy,OBIA has formulated a unified de-identification and quality control process.In addition,OBIA provides friendly and intuitive web interfaces for data submission,browsing,and retrieval,as well as image retrieval.As of September 2023,OBIA has housed data for a total of 937 individuals,4136 studies,24,701 series,and 1,938,309 images covering 9 modalities and 30 anatomical sites.Collectively,OBIA provides a reliable platform for biomedical imaging data management and offers free open access to all publicly available data to support research activities throughout the world.OBIA can be accessed at https://ngdc.cncb.ac.cn/obia.

T2T-YAO, T2T-SHUN, and more

The first high-quality reference genome for Chinese populations has just been released[1],T2Y-YAO(尧yao),and it starts a first-of-its-kind series for building virtual ancestor genomes(VAGs)for Chinese population-based studies and healthcare applications[2,3].The reasons are multifold.First,the Han people(汉人han ren)are the world’s largest ethnicity,1.5 billion in total,19%of the global population,and 91%of the Chinese nationals.Second,the Chinese population history—regardless of whether the precise time period of each nation and State has or has not been determined—and complexity are being recorded faithfully in their genome sequences.Third,the Han ethnic groups are still co-habiting with other 55 minor ethnic peoples without geopolitical and geographical barriers.