Osmanthus fragrans Lour.is a well-known aromatic plant widely used as a food ingredient due to its unique floral fragrance and bioactive compounds.To fully utilize O.fragrans resources,we established an O.fragrans mul...Osmanthus fragrans Lour.is a well-known aromatic plant widely used as a food ingredient due to its unique floral fragrance and bioactive compounds.To fully utilize O.fragrans resources,we established an O.fragrans multi-omics database called the O.fragrans Information Resource(OfIR:http://yanglab.hzau.edu.cn/OfIR/home/).OfIR is a convenient and comprehensive multi-omics database that efficiently integrates phenotype and genetic variation from 127 O.fragrans cultivars,and provides many easy-to-use analysis tools,including primer design,sequence extraction,multi-sequence alignment,GO and KEGG enrichment analysis,variation annotation,and electronic PCR.Two case studies were used to demonstrate its power to mine candidate genetic variation sites or genes associated with specific traits or regulatory networks.In summary,the multi-omics database OfIR provides a convenient and user-friendly platform for researchers in mining functional genes and contributes to the genetic breeding of O.fragrans.展开更多
Gastric cancer(GC), the fifth most common cancer globally, remains the leading cause of cancer deaths worldwide. Inflammation-induced tumorigenesis is the predominant process in GC development;therefore, systematic re...Gastric cancer(GC), the fifth most common cancer globally, remains the leading cause of cancer deaths worldwide. Inflammation-induced tumorigenesis is the predominant process in GC development;therefore, systematic research in this area should improve understanding of the biological mechanisms that initiate GC development and promote cancer hallmarks. Here, we summarize biological knowledge regarding gastric inflammation-induced tumorigenesis, and characterize the multi-omics data and systems biology methods for investigating GC development. Of note, we highlight pioneering studies in multi-omics data and state-of-the-art network-based algorithms used for dissecting the features of gastric inflammation-induced tumorigenesis, and we propose translational applications in early GC warning biomarkers and precise treatment strategies. This review offers integrative insights for GC research, with the goal of paving the way to novel paradigms for GC precision oncology and prevention.展开更多
Metabolic network construction plays a pivotal role in unraveling the regulatory mechanism of biological activities,although it often proves to be challenging and labor-intensive,particularly with non-model organisms....Metabolic network construction plays a pivotal role in unraveling the regulatory mechanism of biological activities,although it often proves to be challenging and labor-intensive,particularly with non-model organisms.In this study,we develop a computational approach that employs reaction models based on the structure-guided chemical modification and related compounds to construct a metabolic network in wheat.This construction results in a comprehensive structure-guided network,including 625 identified metabolites and additional 333 putative reactions compared with the Kyoto Encyclopedia of Genes and Genomes database.Using a combination of gene annotation,reaction classification,structure similarity,and correlations from transcriptome and metabolome analysis,a total of 229 potential genes related to these reactions are identified within this network.To validate the network,the functionality of a hydroxycinnamoyltransferase(TraesCS3D01G314900)for the synthesis of polyphenols and a rhamnosyltransferase(TraesCS2D01G078700)for the modification of flavonoids are verified through in vitro enzymatic studies and wheat mutant tests,respectively.Our research thus supports the utility of structure-guided chemical modification as an effective tool in identifying causal candidate genes for constructing metabolic networks and further in metabolomic genetic studies.展开更多
The rapid growth of biomedical data,particularly multi-omics data including genomes,transcriptomics,proteomics,metabolomics,and epigenomics,medical research and clinical decision-making confront both new opportunities...The rapid growth of biomedical data,particularly multi-omics data including genomes,transcriptomics,proteomics,metabolomics,and epigenomics,medical research and clinical decision-making confront both new opportunities and obstacles.The huge and diversified nature of these datasets cannot always be managed using traditional data analysis methods.As a consequence,deep learning has emerged as a strong tool for analysing numerous omics data due to its ability to handle complex and non-linear relationships.This paper explores the fundamental concepts of deep learning and how they are used in multi-omics medical data mining.We demonstrate how autoencoders,variational autoencoders,multimodal models,attention mechanisms,transformers,and graph neural networks enable pattern analysis and recognition across all omics data.Deep learning has been found to be effective in illness classification,biomarker identification,gene network learning,and therapeutic efficacy prediction.We also consider critical problems like as data quality,model explainability,whether findings can be repeated,and computational power requirements.We now consider future elements of combining omics with clinical and imaging data,explainable AI,federated learning,and real-time diagnostics.Overall,this study emphasises the need of collaborating across disciplines to advance deep learning-based multi-omics research for precision medicine and comprehending complicated disorders.展开更多
High-throughput transcriptomics has evolved from bulk RNA-seq to single-cell and spatial profiling,yet its clinical translation still depends on effective integration across diverse omics and data modalities.Emerging ...High-throughput transcriptomics has evolved from bulk RNA-seq to single-cell and spatial profiling,yet its clinical translation still depends on effective integration across diverse omics and data modalities.Emerging foundation models and multimodal learning frameworks are enabling scalable and transferable representations of cellular states,while advances in interpretability and real-world data integration are bridging the gap between discovery and clinical application.This paper outlines a concise roadmap for AI-driven,transcriptome-centered multi-omics integration in precision medicine(Figure 1).展开更多
Background:Giant cell arteritis(GCA),the most common systemic vasculitis affecting elderly individuals,currently lacks specific therapies.This study aimed to systematically identify therapeutic targets for GCA through...Background:Giant cell arteritis(GCA),the most common systemic vasculitis affecting elderly individuals,currently lacks specific therapies.This study aimed to systematically identify therapeutic targets for GCA through integration of large-scale multi-omics datasets.Methods:We constructed a multi-stage analytical framework encompassing 32 proteomic datasets(covering 2914 unique plasma proteins)and 6 transcriptomic datasets.Multi-omics integration strategies,including two-sample Mendelian randomization,colocalization analysis,and functional enrichment analysis,were employed to identify and validate causal relationships between candidate targets and GCA risk across 4 independent European-ancestry GCA cohorts.Single-cell RNA sequencing analysis of peripheral blood mononuclear cells from untreated GCA patients was performed to characterize hub gene-immune cell relationships.Results:We identified 43 plasma proteins causally associated with GCA[false discovery rate(FDR)<0.05],with 17 representing novel therapeutic targets.Through dual validation using proteome-wide association studies and transcriptome-wide association studies,we identified 13 high-confidence candidate targets with distinct tissue-specific expression patterns.Unc-51 like kinase 3(ULK3)emerged as the strongest protective factor(odds ratio=0.47,95%confidence interval:0.37–0.71)through autophagy regulation,while SLAMF7 represents an immediate drug repositioning opportunity as the target of food and drug administration-approved elotuzumab.Five targets have existing approved drugs(SLAMF7,ICAM1,IL18,IL6ST,CTSS).Single-cell analysis revealed profound disruption of hub gene-immune cell relationships in untreated GCA patients,with cell-type-specific alterations in inflammatory gene expression,and TYMP as the most critical hub gene.Conclusions:This study provides a clinically-actionable atlas of 43 potential therapeutic targets in GCA,identifying novel mechanisms including autophagy modulation and metabolic reprogramming,with immediate drug repositioning opportunities and precision medicine strategies based on tissue-specific and cell-type-specific expression patterns.These findings require experimental validation before clinical translation.展开更多
Cancer deaths and new cases worldwide are projected to rise by 47%by 2040,with transitioning countries experiencing an even higher increase of up to 95%.Tumor severity is profoundly influenced by the timing,accuracy,a...Cancer deaths and new cases worldwide are projected to rise by 47%by 2040,with transitioning countries experiencing an even higher increase of up to 95%.Tumor severity is profoundly influenced by the timing,accuracy,and stage of diagnosis,which directly impacts clinical decision-making.Various biological entities,including genes,proteins,mRNAs,miRNAs,and metabolites,contribute to cancer development.The emergence of multi-omics technologies has transformed cancer research by revealing molecular alterations across multiple biological layers.This integrative approach supports the notion that cancer is fundamentally driven by such alterations,enabling the discovery ofmolecular signatures for precision oncology.This reviewexplores the role of AI-drivenmulti-omics analyses in cancer medicine,emphasizing their potential to identify novel biomarkers and therapeutic targets,enhance understanding of Tumor biology,and address integration challenges in clinical workflows.Network biology analyzes identified ERBB2,KRAS,and TP53 as top hub genes in lung cancer based on Maximal Clique Centrality(MCC)scores.In contrast,TP53,ERBB2,ESR1,MYC,and BRCA1 emerged as central regulators in breast cancer,linked to cell proliferation,hormonal signaling,and genomic stability.The review also discusses how specific Artificial Intelligence(AI)algorithms can streamline the integration of heterogeneous datasets,facilitate the interpretation of the tumor microenvironment,and support data-driven clinical strategies.展开更多
Tumor-specific antigens,also known as neoantigens,have potential utility in anti-cancer immunotherapy,including immune checkpoint blockade(ICB),neoantigen-specific T cell receptor-engineered T(TCR-T),chimeric antigen ...Tumor-specific antigens,also known as neoantigens,have potential utility in anti-cancer immunotherapy,including immune checkpoint blockade(ICB),neoantigen-specific T cell receptor-engineered T(TCR-T),chimeric antigen receptor T(CAR-T),and therapeutic cancer vaccines(TCVs).After recognizing presented neoantigens,the immune system becomes activated and triggers the death of tumor cells.Neoantigens may be derived from multiple origins,including somatic mutations(single nucleotide variants,insertions/deletions,and gene fusions),circular RNAs,alternative splicing,RNA editing,and polymorphic microbiomes.An increasing amount of bioinformatics tools and algorithms are being developed to predict tumor neoantigens derived from different sources,which may require inputs from different multi-omics data.In addition,calculating the peptide-major histocompatibility complex(MHC)affinity can aid in selecting putative neoantigens,as high binding affinities facilitate antigen presentation.Based on these approaches and previous experiments,many resources have been developed to reveal the landscape of tumor neoantigens across multiple cancer types.Herein,we summarize these tools,algorithms,and resources to provide an overview of computational analysis for neoantigen discovery and prioritization,as well as the future development of potential clinical utilities in this field.展开更多
In addition to being a major source of animal protein,pigs are an important model for studying development and diseases in humans.Over the past two decades,thousands of high-throughput sequencing studies in pigs have ...In addition to being a major source of animal protein,pigs are an important model for studying development and diseases in humans.Over the past two decades,thousands of high-throughput sequencing studies in pigs have been performed using a variety of tissues from different breeds and developmental stages.However,multi-omics databases specifically designed for pig functional genomics research are still limited.Here,we present PIGOME,a user-friendly database of pig multi-omes.PIGOME currently contains seven types of pig omics datasets,including whole-genome sequencing(WGS),RNA sequencing(RNA-seq),microRNA sequencing(miRNA-seq),chromatin immunoprecipitation sequenc-ing(ChiP-seq),assay for transposase-accessible chromatin sequencing(ATAC-seq),bisulfite sequencing(BS-seq),and methylated RNA immu-noprecipitation sequencing(MeRiP-seq),from 6901 samples and 392 projects with manually curated metadata,integrated gene annotation,and quantitative trait locus information.Furthermore,various"Explore"and“Browse”functions have been established to provide user-friendly ac-cess to omics information.PIGOME implements several tools to visualize genomic variants,gene expression,and epigenetic signals of a given gene in the pig genome,enabling efficient exploration of spatiotemporal gene expression/epigenetic patterns,functions,regulatory mecha-nisms,and associated economic traits.Collectively,PlGOME provides valuable resources for pig breeding and is helpful for human biomedical research.PIGOMEis availableat https://pigome.com.展开更多
Dear Editor,The breakthrough in single-cell omics sequencing technologies has provided an unprecedented level of detail,allowing biologists to explore the patterns of gene activity,and the dynamics of cellular functio...Dear Editor,The breakthrough in single-cell omics sequencing technologies has provided an unprecedented level of detail,allowing biologists to explore the patterns of gene activity,and the dynamics of cellular function at the resolution of individual cells.At the forefront of this revolution is single-cell RNA sequencing(scRNA-seq),which measures gene expression of individual cells to characterize transcriptional heterogeneity.Additionally,other single-cell assays,such as single-cell assay for transposase-accessible chromatin using sequencing(scATAC-seq),shed light on cellular heterogeneity at the epigenetic level,enhancing our understanding of transcriptional regulation.However,while single-omics sequencing techniques provide valuable insights,they may not capture the intricate relationships between biomolecules in single cells due to their restriction to only one type of omics data.To bridge this gap,recent advancements have led to the development of several joint profiling methods(Cao et al.,2018;Chen et al.,2019;Luecken et al.,2021;Ma et al.,2020),which enable the simultaneous measurement of gene expression and chromatin accessibility,offering a holistic view of the gene regulatory landscape in individual cells.展开更多
Genomic prediction is an effective way to accelerate the rate of agronomic trait improvement in plants.Traditional methods typically use linear regression models with clear assumptions;such methods are unable to captu...Genomic prediction is an effective way to accelerate the rate of agronomic trait improvement in plants.Traditional methods typically use linear regression models with clear assumptions;such methods are unable to capture the complex relationships between genotypes and phenotypes.Non-linear models(e.g.,deep neural networks)have been proposed as a superior alternative to linear models because they can capture complex non-additive effects.Here we introduce a deep learning(DL)method,deep neural network genomic prediction(DNNGP),for integration of multi-omics data in plants.We trained DNNGP on four datasets and compared its performance with methods built with five classic models:genomic best linear unbiased prediction(GBLUP);two methods based on a machine learning(ML)framework,light gradient boosting machine(LightGBM)and support vector regression(SVR);and two methods based on a DL framework,deep learning genomic selection(DeepGS)and deep learning genome-wide association study(DLGWAS).DNNGP is novel in five ways.First,it can be applied to a variety of omics data to predict phenotypes.Second,the multilayered hierarchical structure of DNNGP dynamically learns features from raw data,avoiding overfitting and improving the convergence rate using a batch normalization layer and early stopping and rectified linear activation(rectified linear unit)functions.Third,when small datasets were used,DNNGP produced results that are competitive with results from the other five methods,showing greater prediction accuracy than the other methods when large-scale breeding data were used.Fourth,the computation time required by DNNGP was comparable with that of commonly used methods,up to 10 times faster than DeepGS.Fifth,hyperparameters can easily be batch tuned on a local machine.Compared with GBLUP,LightGBM,SVR,DeepGS and DLGWAS,DNNGP is superior to these existing widely used genomic selection(GS)methods.Moreover,DNNGP can generate robust assessments from diverse datasets,including omics data,and quickly incorporate complex and large datasets into usable models,making it a promising and practical approach for straightforward integration into existing GS platforms.展开更多
Background:Presently,multi-omics data(e.g.,genomics,transcriptomics,proteomics,and metabolomics)are available to improve genomic predictors.Omics data not only offers new data layers for genomic prediction but also pr...Background:Presently,multi-omics data(e.g.,genomics,transcriptomics,proteomics,and metabolomics)are available to improve genomic predictors.Omics data not only offers new data layers for genomic prediction but also provides a bridge between organismal phenotypes and genome variation that cannot be readily captured at the genome sequence level.Therefore,using multi-omics data to select feature markers is a feasible strategy to improve the accuracy of genomic prediction.In this study,simultaneously using whole-genome sequencing(WGS)and gene expression level data,four strategies for single-nucleotide polymorphism(SNP)preselection were investigated for genomic predictions in the Drosophila Genetic Reference Panel.Results:Using genomic best linear unbiased prediction(GBLUP)with complete WGS data,the prediction accuracies were 0.208±0.020(0.181±0.022)for the startle response and 0.272±0.017(0.307±0.015)for starvation resistance in the female(male)lines.Compared with GBLUP using complete WGS data,both GBLUP and the genomic feature BLUP(GFBLUP)did not improve the prediction accuracy using SNPs preselected from complete WGS data based on the results of genome-wide association studies(GWASs)or transcriptome-wide association studies(TWASs).Furthermore,by using SNPs preselected from the WGS data based on the results of the expression quantitative trait locus(eQTL)mapping of all genes,only the startle response had greater accuracy than GBLUP with the complete WGS data.The best accuracy values in the female and male lines were 0.243±0.020 and 0.220±0.022,respectively.Importantly,by using SNPs preselected based on the results of the eQTL mapping of significant genes from TWAS,both GBLUP and GFBLUP resulted in great accuracy and small bias of genomic prediction.Compared with the GBLUP using complete WGS data,the best accuracy values represented increases of 60.66%and 39.09%for the starvation resistance and 27.40%and 35.36%for startle response in the female and male lines,respectively.Conclusions:Overall,multi-omics data can assist genomic feature preselection and improve the performance of genomic prediction.The new knowledge gained from this study will enrich the use of multi-omics in genomic prediction.展开更多
Background:Chromatin-associated RNA(caRNA)acts as a ubiquitous epigenetic layer in eukaryotes,and has been reported to be essential in various biological processes,including gene transcription,chromatin remodeling and...Background:Chromatin-associated RNA(caRNA)acts as a ubiquitous epigenetic layer in eukaryotes,and has been reported to be essential in various biological processes,including gene transcription,chromatin remodeling and cellular differentiation.Recently,numerous experimental techniques have been developed to characterize genome-wide RNA-chromatin interactions to understand their underlying biological functions.However,these experimental methods are generally expensive,time-consuming,and limited in identifying all potential sites,while most of the existing computational methods are restricted to detecting only specific types of RNAs interacting with chromatin.Methods:Here,we propose a highly interpretable computational framework,named DeepRCI,to identify the interactions between various types of RNAs and chromatin.In this framework,we introduce a novel deep learning component called variformer and integrate multi-omics data to capture intrinsic genomic features at both RNA and DNA levels.Results:Extensive experiments demonstrate that DeepRCI can detect RNA-chromatin interactions more accurately when compared to the state-of-the-art baseline prediction methods.Furthermore,the sequence features extracted by DeepRCI can be well matched to known critical gene regulatory components,indicating that our model can provide useful biological insights into understanding the underlying mechanisms of RNA-chromatin interactions.In addition,based on the prediction results,we further delineate the relationships between RNA-chromatin interactions and cellular functions,including gene expression and the modulation of cell states.Conclusions:In summary,DeepRCI can serve as a useful tool for characterizing RNA-chromatin interactions and studying the underlying gene regulatory code.展开更多
The identification of tumor driver genes facilitates accurate cancer diagnosis and treatment,playing a key role in precision oncology,along with gene signaling,regulation,and their interaction with protein complexes.T...The identification of tumor driver genes facilitates accurate cancer diagnosis and treatment,playing a key role in precision oncology,along with gene signaling,regulation,and their interaction with protein complexes.To tackle the challenge of distinguishing driver genes from a large number of genomic data,we construct a feature extraction framework for discovering pan-cancer driver genes based on multi-omics data(mutations,gene expression,copy number variants,and DNA methylation)combined with protein–protein interaction(PPI)networks.Using a network propagation algorithm,we mine functional information among nodes in the PPI network,focusing on genes with weak node information to represent specific cancer information.From these functional features,we extract distribution features of pan-cancer data,pan-cancer TOPSIS features of functional features using the ideal solution method,and SetExpan features of pan-cancer data from the gene functional features,a method to rank pan-cancer data based on the average inverse rank.These features represent the common message of pan-cancer.Finally,we use the lightGBM classification algorithm for gene prediction.Experimental results show that our method outperforms existing methods in terms of the area under the check precision-recall curve(AUPRC)and demonstrates better performance across different PPI networks.This indicates our framework’s effectiveness in predicting potential cancer genes,offering valuable insights for the diagnosis and treatment of tumors.展开更多
Bioinformatic analysis of large and complex omics datasets has become increasingly useful in modern day biology by providing a great depth of information,with its application to neuroscience termed neuroinformatics.Da...Bioinformatic analysis of large and complex omics datasets has become increasingly useful in modern day biology by providing a great depth of information,with its application to neuroscience termed neuroinformatics.Data mining of omics datasets has enabled the generation of new hypotheses based on differentially regulated biological molecules associated with disease mechanisms,which can be tested experimentally for improved diagnostic and therapeutic targeting of neurodegenerative diseases.Importantly,integrating multi-omics data using a systems bioinformatics approach will advance the understanding of the layered and interactive network of biological regulation that exchanges systemic knowledge to facilitate the development of a comprehensive human brain profile.In this review,we first summarize data mining studies utilizing datasets from the individual type of omics analysis,including epigenetics/epigenomics,transcriptomics,proteomics,metabolomics,lipidomics,and spatial omics,pertaining to Alzheimer's disease,Parkinson's disease,and multiple sclerosis.We then discuss multi-omics integration approaches,including independent biological integration and unsupervised integration methods,for more intuitive and informative interpretation of the biological data obtained across different omics layers.We further assess studies that integrate multi-omics in data mining which provide convoluted biological insights and offer proof-of-concept proposition towards systems bioinformatics in the reconstruction of brain networks.Finally,we recommend a combination of high dimensional bioinformatics analysis with experimental validation to achieve translational neuroscience applications including biomarker discovery,therapeutic development,and elucidation of disease mechanisms.We conclude by providing future perspectives and opportunities in applying integrative multi-omics and systems bioinformatics to achieve precision phenotyping of neurodegenerative diseases and towards personalized medicine.展开更多
In the post-genome-wide association study era,multi-omics techniques have shown great power and poten-tial for candidate gene mining and functional genomics research.However,due to the lack of effective data integrati...In the post-genome-wide association study era,multi-omics techniques have shown great power and poten-tial for candidate gene mining and functional genomics research.However,due to the lack of effective data integration and multi-omics analysis platforms,such techniques have not still been applied widely in rape-seed,an important oil crop worldwide.Here,we report a rapeseed multi-omics database(BnlR;http:/l yanglab.hzau.edu.cn/BnlR),which provides datasets of six omics including genomics,transcriptomics,variomics,epigenetics,phenomics,and metabolomics,as well as numerous"variation-gene expression-phenotype"associations by using multiple statistical methods.In addition,a series of multi-omics search and analysis tools are integrated to facilitate the browsing and application of these datasets.BnlR is the most comprehensive multi-omics database for rapeseed so far,and two case studies demonstrated its power to mine candidate genes associated with specific traits and analyze their potential regulatory mechanisms.展开更多
Traditional Chinese medicine(TCM)demonstrates distinctive advantages in disease prevention and treatment.However,analyzing its biological mechanisms through the modern medical research paradigm of“single drug,single ...Traditional Chinese medicine(TCM)demonstrates distinctive advantages in disease prevention and treatment.However,analyzing its biological mechanisms through the modern medical research paradigm of“single drug,single target”presents significant challenges due to its holistic approach.Network pharmacology and its core theory of network targets connect drugs and diseases from a holistic and systematic perspective based on biological networks,overcoming the limitations of reductionist research models and showing considerable value in TCM research.Recent integration of network target computational and experimental methods with artificial intelligence(AI)and multi-modal multi-omics technologies has substantially enhanced network pharmacology methodology.The advancement in computational and experimental techniques provides complementary support for network target theory in decoding TCM principles.This review,centered on network targets,examines the progress of network target methods combined with AI in predicting disease molecular mechanisms and drug-target relationships,alongside the application of multi-modal multi-omics technologies in analyzing TCM formulae,syndromes,and toxicity.Looking forward,network target theory is expected to incorporate emerging technologies while developing novel approaches aligned with its unique characteristics,potentially leading to significant breakthroughs in TCM research and advancing scientific understanding and innovation in TCM.展开更多
Objective:Triple-negative breast cancer(TNBC)is a highly aggressive subtype that lacks targeted therapies,leading to a poorer prognosis.However,some patients achieve long-term recurrence-free survival(RFS),offering va...Objective:Triple-negative breast cancer(TNBC)is a highly aggressive subtype that lacks targeted therapies,leading to a poorer prognosis.However,some patients achieve long-term recurrence-free survival(RFS),offering valuable insights into tumor biology and potential treatment strategies.Methods:We conducted a comprehensive multi-omics analysis of 132 patients with American Joint Committee on Cancer(AJCC)stage III TNBC,comprising 36 long-term survivors(RFS≥8 years),62 moderate-term survivors(RFS:3-8 years),and 34 short-term survivors(RFS<3 years).Analyses investigated clinicopathological factors,whole-exome sequencing,germline mutations,copy number alterations(CNAs),RNA sequences,and metabolomic profiles.Results:Long-term survivors exhibited fewer metastatic regional lymph nodes,along with tumors showing reduced stromal fibrosis and lower Ki67 index.Molecularly,these tumors exhibited multiple alterations in genes related to homologous recombination repair,with higher frequencies of germline mutations and somatic CNAs.Additionally,tumors from long-term survivors demonstrated significant downregulation of the RTK-RAS signaling pathway.Metabolomic profiling revealed decreased levels of lipids and carbohydrate,particularly those involved in glycerophospholipid,fructose,and mannose metabolism,in long-term survival group.Multivariate Cox analysis identified fibrosis[hazard ratio(HR):12.70,95%confidence interval(95%CI):2.19-73.54,P=0.005]and RAC1copy number loss/deletion(HR:0.22,95%CI:0.06-0.83,P=0.026)as independent predictors of RFS.Higher fructose/mannose metabolism was associated with worse overall survival(HR:1.30,95%CI:1.01-1.68,P=0.045).Our findings emphasize the association between biological determinants and prolonged survival in patients with TNBC.Conclusions:Our study systematically identified the key molecular and metabolic features associated with prolonged survival in AJCC stage III TNBC,suggesting potential therapeutic targets to improve patient outcomes.展开更多
Genome-wide association studies(GWASs)have identified over 140 colorectal cancer(CRC)-associated loci;however,target genes at the majority of loci and underlying molecular mechanisms are poorly understood.Here,we util...Genome-wide association studies(GWASs)have identified over 140 colorectal cancer(CRC)-associated loci;however,target genes at the majority of loci and underlying molecular mechanisms are poorly understood.Here,we utilized a Bayesian approach,integrative risk gene selector(iRIGS),to prioritize risk genes at CRC GWAS loci by integrating multi-omics data.As a result,a total of 105 high-confidence risk genes(HRGs)were identified,which exhibited strong gene dependencies for CRC and enrichment in the biological processes implicated in CRC.Among the 105 HRGs,CEBPB,located at the 20q13.13 locus,acted as a transcription factor playing critical roles in cancer.Our subsequent assays indicated the tumor promoter function of CEBPB that facilitated CRC cell proliferation by regulating multiple oncogenic pathways such as MAPK,PI3K-Akt,and Ras signaling.Next,by integrating a fine-mapping analysis and three independent case-control studies in Chinese populations consisting of 8,039 cases and 12,775 controls,we elucidated that rs1810503,a putative functional variant regulating CEBPB,was associated with CRC risk(OR=0.90,95%CI=0.86–0.93,P=1.07×10^(−7)).The association between rs1810503 and CRC risk was further validated in three additional multi-ancestry populations consisting of 24,254 cases and 58,741 controls.Mechanistically,the rs1810503 A to T allele change weakened the enhancer activity in an allele-specific manner to decrease CEBPB expression via longrange promoter-enhancer interactions,mediated by the transcription factor,REST,and thus decreased CRC risk.In summary,our study provides a genetic resource and a generalizable strategy for CRC etiology investigation,and highlights the biological implications of CEBPB in CRC tumorigenesis,shedding new light on the etiology of CRC.展开更多
Childhood asthma is one of the most common respiratory diseases with rising mortality and morbidity.The multi-omics data is providing a new chance to explore collaborative biomarkers and corresponding diagnostic model...Childhood asthma is one of the most common respiratory diseases with rising mortality and morbidity.The multi-omics data is providing a new chance to explore collaborative biomarkers and corresponding diagnostic models of childhood asthma.To capture the nonlinear association of multi-omics data and improve interpretability of diagnostic model,we proposed a novel deep association model(DAM)and corresponding efficient analysis framework.First,the Deep Subspace Reconstruction was used to fuse the omics data and diagnostic information,thereby correcting the distribution of the original omics data and reducing the influence of unnecessary data noises.Second,the Joint Deep Semi-Negative Matrix Factorization was applied to identify different latent sample patterns and extract biomarkers from different omics data levels.Third,our newly proposed Deep Orthogonal Canonical Correlation Analysis can rank features in the collaborative module,which are able to construct the diagnostic model considering nonlinear correlation between different omics data levels.Using DAM,we deeply analyzed the transcriptome and methylation data of childhood asthma.The effectiveness of DAM is verified from the perspectives of algorithm performance and biological significance on the independent test dataset,by ablation experiment and comparison with many baseline methods from clinical and biological studies.The DAM-induced diagnostic model can achieve a prediction AUC of o.912,which is higher than that of many other alternative methods.Meanwhile,relevant pathways and biomarkers of childhood asthma are also recognized to be collectively altered on the gene expression and methylation levels.As an interpretable machine learning approach,DAM simultaneously considers the non-linear associations among samples and those among biological features,which should help explore interpretative biomarker candidates and efficient diagnostic models from multi-omics data analysis for human complexdiseases.展开更多
基金supported by research grants provided by the National Natural Science Foundation of China(Grant Nos.32101581,32271951,and 32372754)the Hubei Provincial Central Leading Local Special Project(Grant No.2022BGE263)+3 种基金the Key Research and Science and Technology Program of Hubei Province(Grant No.2021BBA098)the Hubei Province Natural Science Foundation(Grant Nos.2023AFB1063 and 2024AFB1057)the Innovation Team Project from Hubei University of Science and Technology(Grant No.2022T02)a PhD grant from the Hubei University of Science and Technology(Grant Nos.BK202002and BK202419).
文摘Osmanthus fragrans Lour.is a well-known aromatic plant widely used as a food ingredient due to its unique floral fragrance and bioactive compounds.To fully utilize O.fragrans resources,we established an O.fragrans multi-omics database called the O.fragrans Information Resource(OfIR:http://yanglab.hzau.edu.cn/OfIR/home/).OfIR is a convenient and comprehensive multi-omics database that efficiently integrates phenotype and genetic variation from 127 O.fragrans cultivars,and provides many easy-to-use analysis tools,including primer design,sequence extraction,multi-sequence alignment,GO and KEGG enrichment analysis,variation annotation,and electronic PCR.Two case studies were used to demonstrate its power to mine candidate genetic variation sites or genes associated with specific traits or regulatory networks.In summary,the multi-omics database OfIR provides a convenient and user-friendly platform for researchers in mining functional genes and contributes to the genetic breeding of O.fragrans.
基金supported by funds from the National Natural Science Foundation of China (Grant No. T2341008)。
文摘Gastric cancer(GC), the fifth most common cancer globally, remains the leading cause of cancer deaths worldwide. Inflammation-induced tumorigenesis is the predominant process in GC development;therefore, systematic research in this area should improve understanding of the biological mechanisms that initiate GC development and promote cancer hallmarks. Here, we summarize biological knowledge regarding gastric inflammation-induced tumorigenesis, and characterize the multi-omics data and systems biology methods for investigating GC development. Of note, we highlight pioneering studies in multi-omics data and state-of-the-art network-based algorithms used for dissecting the features of gastric inflammation-induced tumorigenesis, and we propose translational applications in early GC warning biomarkers and precise treatment strategies. This review offers integrative insights for GC research, with the goal of paving the way to novel paradigms for GC precision oncology and prevention.
基金supported by the Young Top-notch Talent Cultivation Program of Hubei Province,the Natural Science Foundation for Distinguished Young Scientists of Hubei Province(2021CFA058)the First-Class Discipline Construction Funds of College of Plant Science and Technology,Huazhong Agricultural University(2023ZKPY005).
文摘Metabolic network construction plays a pivotal role in unraveling the regulatory mechanism of biological activities,although it often proves to be challenging and labor-intensive,particularly with non-model organisms.In this study,we develop a computational approach that employs reaction models based on the structure-guided chemical modification and related compounds to construct a metabolic network in wheat.This construction results in a comprehensive structure-guided network,including 625 identified metabolites and additional 333 putative reactions compared with the Kyoto Encyclopedia of Genes and Genomes database.Using a combination of gene annotation,reaction classification,structure similarity,and correlations from transcriptome and metabolome analysis,a total of 229 potential genes related to these reactions are identified within this network.To validate the network,the functionality of a hydroxycinnamoyltransferase(TraesCS3D01G314900)for the synthesis of polyphenols and a rhamnosyltransferase(TraesCS2D01G078700)for the modification of flavonoids are verified through in vitro enzymatic studies and wheat mutant tests,respectively.Our research thus supports the utility of structure-guided chemical modification as an effective tool in identifying causal candidate genes for constructing metabolic networks and further in metabolomic genetic studies.
文摘The rapid growth of biomedical data,particularly multi-omics data including genomes,transcriptomics,proteomics,metabolomics,and epigenomics,medical research and clinical decision-making confront both new opportunities and obstacles.The huge and diversified nature of these datasets cannot always be managed using traditional data analysis methods.As a consequence,deep learning has emerged as a strong tool for analysing numerous omics data due to its ability to handle complex and non-linear relationships.This paper explores the fundamental concepts of deep learning and how they are used in multi-omics medical data mining.We demonstrate how autoencoders,variational autoencoders,multimodal models,attention mechanisms,transformers,and graph neural networks enable pattern analysis and recognition across all omics data.Deep learning has been found to be effective in illness classification,biomarker identification,gene network learning,and therapeutic efficacy prediction.We also consider critical problems like as data quality,model explainability,whether findings can be repeated,and computational power requirements.We now consider future elements of combining omics with clinical and imaging data,explainable AI,federated learning,and real-time diagnostics.Overall,this study emphasises the need of collaborating across disciplines to advance deep learning-based multi-omics research for precision medicine and comprehending complicated disorders.
文摘High-throughput transcriptomics has evolved from bulk RNA-seq to single-cell and spatial profiling,yet its clinical translation still depends on effective integration across diverse omics and data modalities.Emerging foundation models and multimodal learning frameworks are enabling scalable and transferable representations of cellular states,while advances in interpretability and real-world data integration are bridging the gap between discovery and clinical application.This paper outlines a concise roadmap for AI-driven,transcriptome-centered multi-omics integration in precision medicine(Figure 1).
基金supported by grants from the Fundamental Research Funds for the Central Universities(No.2025ZFJH03)the Central Guidance Fund for Local Science and Technology Development(No.2024ZY01054)the CAMS Innovation Fund for Medical Sciences(No.2019-I2M-5-045).
文摘Background:Giant cell arteritis(GCA),the most common systemic vasculitis affecting elderly individuals,currently lacks specific therapies.This study aimed to systematically identify therapeutic targets for GCA through integration of large-scale multi-omics datasets.Methods:We constructed a multi-stage analytical framework encompassing 32 proteomic datasets(covering 2914 unique plasma proteins)and 6 transcriptomic datasets.Multi-omics integration strategies,including two-sample Mendelian randomization,colocalization analysis,and functional enrichment analysis,were employed to identify and validate causal relationships between candidate targets and GCA risk across 4 independent European-ancestry GCA cohorts.Single-cell RNA sequencing analysis of peripheral blood mononuclear cells from untreated GCA patients was performed to characterize hub gene-immune cell relationships.Results:We identified 43 plasma proteins causally associated with GCA[false discovery rate(FDR)<0.05],with 17 representing novel therapeutic targets.Through dual validation using proteome-wide association studies and transcriptome-wide association studies,we identified 13 high-confidence candidate targets with distinct tissue-specific expression patterns.Unc-51 like kinase 3(ULK3)emerged as the strongest protective factor(odds ratio=0.47,95%confidence interval:0.37–0.71)through autophagy regulation,while SLAMF7 represents an immediate drug repositioning opportunity as the target of food and drug administration-approved elotuzumab.Five targets have existing approved drugs(SLAMF7,ICAM1,IL18,IL6ST,CTSS).Single-cell analysis revealed profound disruption of hub gene-immune cell relationships in untreated GCA patients,with cell-type-specific alterations in inflammatory gene expression,and TYMP as the most critical hub gene.Conclusions:This study provides a clinically-actionable atlas of 43 potential therapeutic targets in GCA,identifying novel mechanisms including autophagy modulation and metabolic reprogramming,with immediate drug repositioning opportunities and precision medicine strategies based on tissue-specific and cell-type-specific expression patterns.These findings require experimental validation before clinical translation.
基金funded by KAU Endowment(WAQF)at King Abdulaziz University,Jeddah,Saudi Arabia.
文摘Cancer deaths and new cases worldwide are projected to rise by 47%by 2040,with transitioning countries experiencing an even higher increase of up to 95%.Tumor severity is profoundly influenced by the timing,accuracy,and stage of diagnosis,which directly impacts clinical decision-making.Various biological entities,including genes,proteins,mRNAs,miRNAs,and metabolites,contribute to cancer development.The emergence of multi-omics technologies has transformed cancer research by revealing molecular alterations across multiple biological layers.This integrative approach supports the notion that cancer is fundamentally driven by such alterations,enabling the discovery ofmolecular signatures for precision oncology.This reviewexplores the role of AI-drivenmulti-omics analyses in cancer medicine,emphasizing their potential to identify novel biomarkers and therapeutic targets,enhance understanding of Tumor biology,and address integration challenges in clinical workflows.Network biology analyzes identified ERBB2,KRAS,and TP53 as top hub genes in lung cancer based on Maximal Clique Centrality(MCC)scores.In contrast,TP53,ERBB2,ESR1,MYC,and BRCA1 emerged as central regulators in breast cancer,linked to cell proliferation,hormonal signaling,and genomic stability.The review also discusses how specific Artificial Intelligence(AI)algorithms can streamline the integration of heterogeneous datasets,facilitate the interpretation of the tumor microenvironment,and support data-driven clinical strategies.
基金supported by the National Natural Science Foundation of China(Grant Nos.32270708 and 82203195)supported by the Medical Science Data Center at Shanghai Medical College of Fudan University,China.
文摘Tumor-specific antigens,also known as neoantigens,have potential utility in anti-cancer immunotherapy,including immune checkpoint blockade(ICB),neoantigen-specific T cell receptor-engineered T(TCR-T),chimeric antigen receptor T(CAR-T),and therapeutic cancer vaccines(TCVs).After recognizing presented neoantigens,the immune system becomes activated and triggers the death of tumor cells.Neoantigens may be derived from multiple origins,including somatic mutations(single nucleotide variants,insertions/deletions,and gene fusions),circular RNAs,alternative splicing,RNA editing,and polymorphic microbiomes.An increasing amount of bioinformatics tools and algorithms are being developed to predict tumor neoantigens derived from different sources,which may require inputs from different multi-omics data.In addition,calculating the peptide-major histocompatibility complex(MHC)affinity can aid in selecting putative neoantigens,as high binding affinities facilitate antigen presentation.Based on these approaches and previous experiments,many resources have been developed to reveal the landscape of tumor neoantigens across multiple cancer types.Herein,we summarize these tools,algorithms,and resources to provide an overview of computational analysis for neoantigen discovery and prioritization,as well as the future development of potential clinical utilities in this field.
基金supported by the National Key Scientific Research Project(Grant No.2023YFF1001100)the Shenzhen Innovation and Entrepreneurship Plan-a major special project of science and technology(Grant No.KJZD20230923115003006)+1 种基金the National Natural Science Foundation of China(Grant Nos.U23A20229 and 32172697)the Agricultural Science and Technology Innovation Program(Grant Nos.CAASZDRW202406 and CAAS-CSAB-202402),China.
文摘In addition to being a major source of animal protein,pigs are an important model for studying development and diseases in humans.Over the past two decades,thousands of high-throughput sequencing studies in pigs have been performed using a variety of tissues from different breeds and developmental stages.However,multi-omics databases specifically designed for pig functional genomics research are still limited.Here,we present PIGOME,a user-friendly database of pig multi-omes.PIGOME currently contains seven types of pig omics datasets,including whole-genome sequencing(WGS),RNA sequencing(RNA-seq),microRNA sequencing(miRNA-seq),chromatin immunoprecipitation sequenc-ing(ChiP-seq),assay for transposase-accessible chromatin sequencing(ATAC-seq),bisulfite sequencing(BS-seq),and methylated RNA immu-noprecipitation sequencing(MeRiP-seq),from 6901 samples and 392 projects with manually curated metadata,integrated gene annotation,and quantitative trait locus information.Furthermore,various"Explore"and“Browse”functions have been established to provide user-friendly ac-cess to omics information.PIGOME implements several tools to visualize genomic variants,gene expression,and epigenetic signals of a given gene in the pig genome,enabling efficient exploration of spatiotemporal gene expression/epigenetic patterns,functions,regulatory mecha-nisms,and associated economic traits.Collectively,PlGOME provides valuable resources for pig breeding and is helpful for human biomedical research.PIGOMEis availableat https://pigome.com.
基金supported by the National Natural Science Foundation of China (Grant Nos. 62203236, 62473212)the Young Elite Scientists Sponsorship Program by CAST (2023QNRC001)+1 种基金the Young Elite Scientists Sponsorship Program by BAST (BYESS2023383)the Emerging Interdisciplinary Platform for Medicine and Engineering in Sports (EIPMES)
文摘Dear Editor,The breakthrough in single-cell omics sequencing technologies has provided an unprecedented level of detail,allowing biologists to explore the patterns of gene activity,and the dynamics of cellular function at the resolution of individual cells.At the forefront of this revolution is single-cell RNA sequencing(scRNA-seq),which measures gene expression of individual cells to characterize transcriptional heterogeneity.Additionally,other single-cell assays,such as single-cell assay for transposase-accessible chromatin using sequencing(scATAC-seq),shed light on cellular heterogeneity at the epigenetic level,enhancing our understanding of transcriptional regulation.However,while single-omics sequencing techniques provide valuable insights,they may not capture the intricate relationships between biomolecules in single cells due to their restriction to only one type of omics data.To bridge this gap,recent advancements have led to the development of several joint profiling methods(Cao et al.,2018;Chen et al.,2019;Luecken et al.,2021;Ma et al.,2020),which enable the simultaneous measurement of gene expression and chromatin accessibility,offering a holistic view of the gene regulatory landscape in individual cells.
基金National Key R&D Program of China(2021YFD1201200)National Science Foundation of China(32022064)+1 种基金Project of Hainan Yazhou Bay Seed Lab(B21HJ0223)Innovation Program of the Chinese Academy of Agricultural Sciences.
文摘Genomic prediction is an effective way to accelerate the rate of agronomic trait improvement in plants.Traditional methods typically use linear regression models with clear assumptions;such methods are unable to capture the complex relationships between genotypes and phenotypes.Non-linear models(e.g.,deep neural networks)have been proposed as a superior alternative to linear models because they can capture complex non-additive effects.Here we introduce a deep learning(DL)method,deep neural network genomic prediction(DNNGP),for integration of multi-omics data in plants.We trained DNNGP on four datasets and compared its performance with methods built with five classic models:genomic best linear unbiased prediction(GBLUP);two methods based on a machine learning(ML)framework,light gradient boosting machine(LightGBM)and support vector regression(SVR);and two methods based on a DL framework,deep learning genomic selection(DeepGS)and deep learning genome-wide association study(DLGWAS).DNNGP is novel in five ways.First,it can be applied to a variety of omics data to predict phenotypes.Second,the multilayered hierarchical structure of DNNGP dynamically learns features from raw data,avoiding overfitting and improving the convergence rate using a batch normalization layer and early stopping and rectified linear activation(rectified linear unit)functions.Third,when small datasets were used,DNNGP produced results that are competitive with results from the other five methods,showing greater prediction accuracy than the other methods when large-scale breeding data were used.Fourth,the computation time required by DNNGP was comparable with that of commonly used methods,up to 10 times faster than DeepGS.Fifth,hyperparameters can easily be batch tuned on a local machine.Compared with GBLUP,LightGBM,SVR,DeepGS and DLGWAS,DNNGP is superior to these existing widely used genomic selection(GS)methods.Moreover,DNNGP can generate robust assessments from diverse datasets,including omics data,and quickly incorporate complex and large datasets into usable models,making it a promising and practical approach for straightforward integration into existing GS platforms.
基金supported by the National Natural Science Foundation of China(31772556)the Local Innovative and Research Teams Project of Guangdong Province(2019BT02N630)+1 种基金the grants from the earmarked fund for China Agriculture Research System(CARS-35)the Science and Technology Innovation Strategy projects of Guangdong Province(Grant No.2018B020203002).
文摘Background:Presently,multi-omics data(e.g.,genomics,transcriptomics,proteomics,and metabolomics)are available to improve genomic predictors.Omics data not only offers new data layers for genomic prediction but also provides a bridge between organismal phenotypes and genome variation that cannot be readily captured at the genome sequence level.Therefore,using multi-omics data to select feature markers is a feasible strategy to improve the accuracy of genomic prediction.In this study,simultaneously using whole-genome sequencing(WGS)and gene expression level data,four strategies for single-nucleotide polymorphism(SNP)preselection were investigated for genomic predictions in the Drosophila Genetic Reference Panel.Results:Using genomic best linear unbiased prediction(GBLUP)with complete WGS data,the prediction accuracies were 0.208±0.020(0.181±0.022)for the startle response and 0.272±0.017(0.307±0.015)for starvation resistance in the female(male)lines.Compared with GBLUP using complete WGS data,both GBLUP and the genomic feature BLUP(GFBLUP)did not improve the prediction accuracy using SNPs preselected from complete WGS data based on the results of genome-wide association studies(GWASs)or transcriptome-wide association studies(TWASs).Furthermore,by using SNPs preselected from the WGS data based on the results of the expression quantitative trait locus(eQTL)mapping of all genes,only the startle response had greater accuracy than GBLUP with the complete WGS data.The best accuracy values in the female and male lines were 0.243±0.020 and 0.220±0.022,respectively.Importantly,by using SNPs preselected based on the results of the eQTL mapping of significant genes from TWAS,both GBLUP and GFBLUP resulted in great accuracy and small bias of genomic prediction.Compared with the GBLUP using complete WGS data,the best accuracy values represented increases of 60.66%and 39.09%for the starvation resistance and 27.40%and 35.36%for startle response in the female and male lines,respectively.Conclusions:Overall,multi-omics data can assist genomic feature preselection and improve the performance of genomic prediction.The new knowledge gained from this study will enrich the use of multi-omics in genomic prediction.
基金supported in part by the National Natural Science Foundation of China(61872216,T2125007 to JZ,31900862 to DZ)the National Key Research and Development Program of China(2018YFC0910404,2021YFF1201300)the Turing AI Institute of Nanjing,the Tsinghua-Toyota Joint Research Fund and the US National Institute of Health grant(1R01NS125018).
文摘Background:Chromatin-associated RNA(caRNA)acts as a ubiquitous epigenetic layer in eukaryotes,and has been reported to be essential in various biological processes,including gene transcription,chromatin remodeling and cellular differentiation.Recently,numerous experimental techniques have been developed to characterize genome-wide RNA-chromatin interactions to understand their underlying biological functions.However,these experimental methods are generally expensive,time-consuming,and limited in identifying all potential sites,while most of the existing computational methods are restricted to detecting only specific types of RNAs interacting with chromatin.Methods:Here,we propose a highly interpretable computational framework,named DeepRCI,to identify the interactions between various types of RNAs and chromatin.In this framework,we introduce a novel deep learning component called variformer and integrate multi-omics data to capture intrinsic genomic features at both RNA and DNA levels.Results:Extensive experiments demonstrate that DeepRCI can detect RNA-chromatin interactions more accurately when compared to the state-of-the-art baseline prediction methods.Furthermore,the sequence features extracted by DeepRCI can be well matched to known critical gene regulatory components,indicating that our model can provide useful biological insights into understanding the underlying mechanisms of RNA-chromatin interactions.In addition,based on the prediction results,we further delineate the relationships between RNA-chromatin interactions and cellular functions,including gene expression and the modulation of cell states.Conclusions:In summary,DeepRCI can serve as a useful tool for characterizing RNA-chromatin interactions and studying the underlying gene regulatory code.
基金National Natural Science Foundation of China,Grant/Award Numbers:61902215,61902216,61972226。
文摘The identification of tumor driver genes facilitates accurate cancer diagnosis and treatment,playing a key role in precision oncology,along with gene signaling,regulation,and their interaction with protein complexes.To tackle the challenge of distinguishing driver genes from a large number of genomic data,we construct a feature extraction framework for discovering pan-cancer driver genes based on multi-omics data(mutations,gene expression,copy number variants,and DNA methylation)combined with protein–protein interaction(PPI)networks.Using a network propagation algorithm,we mine functional information among nodes in the PPI network,focusing on genes with weak node information to represent specific cancer information.From these functional features,we extract distribution features of pan-cancer data,pan-cancer TOPSIS features of functional features using the ideal solution method,and SetExpan features of pan-cancer data from the gene functional features,a method to rank pan-cancer data based on the average inverse rank.These features represent the common message of pan-cancer.Finally,we use the lightGBM classification algorithm for gene prediction.Experimental results show that our method outperforms existing methods in terms of the area under the check precision-recall curve(AUPRC)and demonstrates better performance across different PPI networks.This indicates our framework’s effectiveness in predicting potential cancer genes,offering valuable insights for the diagnosis and treatment of tumors.
基金supported by a Lee Kong Chian School of Medicine Dean’s Postdoctoral Fellowship(021207-00001)from Nanyang Technological University(NTU)Singapore and a Mistletoe Research Fellowship(022522-00001)from the Momental Foundation USA.Jialiu Zeng is supported by a Presidential Postdoctoral Fellowship(021229-00001)from NTU Singapore and an Open Fund Young Investigator Research Grant(OF-YIRG)(MOH-001147)from the National Medical Research Council(NMRC)SingaporeSu Bin Lim is supported by the National Research Foundation(NRF)of Korea(Grant Nos.:2020R1A6A1A03043539,2020M3A9D8037604,2022R1C1C1004756)a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute(KHIDI),funded by the Ministry of Health&Welfare,Republic of Korea(Grant No.:HR22C1734).
文摘Bioinformatic analysis of large and complex omics datasets has become increasingly useful in modern day biology by providing a great depth of information,with its application to neuroscience termed neuroinformatics.Data mining of omics datasets has enabled the generation of new hypotheses based on differentially regulated biological molecules associated with disease mechanisms,which can be tested experimentally for improved diagnostic and therapeutic targeting of neurodegenerative diseases.Importantly,integrating multi-omics data using a systems bioinformatics approach will advance the understanding of the layered and interactive network of biological regulation that exchanges systemic knowledge to facilitate the development of a comprehensive human brain profile.In this review,we first summarize data mining studies utilizing datasets from the individual type of omics analysis,including epigenetics/epigenomics,transcriptomics,proteomics,metabolomics,lipidomics,and spatial omics,pertaining to Alzheimer's disease,Parkinson's disease,and multiple sclerosis.We then discuss multi-omics integration approaches,including independent biological integration and unsupervised integration methods,for more intuitive and informative interpretation of the biological data obtained across different omics layers.We further assess studies that integrate multi-omics in data mining which provide convoluted biological insights and offer proof-of-concept proposition towards systems bioinformatics in the reconstruction of brain networks.Finally,we recommend a combination of high dimensional bioinformatics analysis with experimental validation to achieve translational neuroscience applications including biomarker discovery,therapeutic development,and elucidation of disease mechanisms.We conclude by providing future perspectives and opportunities in applying integrative multi-omics and systems bioinformatics to achieve precision phenotyping of neurodegenerative diseases and towards personalized medicine.
基金supported by the National Natural Science Foundation of China(32070559)the National Key Research and Development Plan of China(2021YFF1000100)+2 种基金the China Postdoctoral Science Foundation(2022M710875)the Hubei Hongshan Laboratory(2021HSZD004)and the Developing Bioinformatics Platform in Hainan Yazhou Bay Seed Lab(no.JBGS-B21HJ0001).
文摘In the post-genome-wide association study era,multi-omics techniques have shown great power and poten-tial for candidate gene mining and functional genomics research.However,due to the lack of effective data integration and multi-omics analysis platforms,such techniques have not still been applied widely in rape-seed,an important oil crop worldwide.Here,we report a rapeseed multi-omics database(BnlR;http:/l yanglab.hzau.edu.cn/BnlR),which provides datasets of six omics including genomics,transcriptomics,variomics,epigenetics,phenomics,and metabolomics,as well as numerous"variation-gene expression-phenotype"associations by using multiple statistical methods.In addition,a series of multi-omics search and analysis tools are integrated to facilitate the browsing and application of these datasets.BnlR is the most comprehensive multi-omics database for rapeseed so far,and two case studies demonstrated its power to mine candidate genes associated with specific traits and analyze their potential regulatory mechanisms.
文摘Traditional Chinese medicine(TCM)demonstrates distinctive advantages in disease prevention and treatment.However,analyzing its biological mechanisms through the modern medical research paradigm of“single drug,single target”presents significant challenges due to its holistic approach.Network pharmacology and its core theory of network targets connect drugs and diseases from a holistic and systematic perspective based on biological networks,overcoming the limitations of reductionist research models and showing considerable value in TCM research.Recent integration of network target computational and experimental methods with artificial intelligence(AI)and multi-modal multi-omics technologies has substantially enhanced network pharmacology methodology.The advancement in computational and experimental techniques provides complementary support for network target theory in decoding TCM principles.This review,centered on network targets,examines the progress of network target methods combined with AI in predicting disease molecular mechanisms and drug-target relationships,alongside the application of multi-modal multi-omics technologies in analyzing TCM formulae,syndromes,and toxicity.Looking forward,network target theory is expected to incorporate emerging technologies while developing novel approaches aligned with its unique characteristics,potentially leading to significant breakthroughs in TCM research and advancing scientific understanding and innovation in TCM.
基金supported by grants from the Medical Engineering Jiont Fund of the Fudan University(No.IDH2310117)。
文摘Objective:Triple-negative breast cancer(TNBC)is a highly aggressive subtype that lacks targeted therapies,leading to a poorer prognosis.However,some patients achieve long-term recurrence-free survival(RFS),offering valuable insights into tumor biology and potential treatment strategies.Methods:We conducted a comprehensive multi-omics analysis of 132 patients with American Joint Committee on Cancer(AJCC)stage III TNBC,comprising 36 long-term survivors(RFS≥8 years),62 moderate-term survivors(RFS:3-8 years),and 34 short-term survivors(RFS<3 years).Analyses investigated clinicopathological factors,whole-exome sequencing,germline mutations,copy number alterations(CNAs),RNA sequences,and metabolomic profiles.Results:Long-term survivors exhibited fewer metastatic regional lymph nodes,along with tumors showing reduced stromal fibrosis and lower Ki67 index.Molecularly,these tumors exhibited multiple alterations in genes related to homologous recombination repair,with higher frequencies of germline mutations and somatic CNAs.Additionally,tumors from long-term survivors demonstrated significant downregulation of the RTK-RAS signaling pathway.Metabolomic profiling revealed decreased levels of lipids and carbohydrate,particularly those involved in glycerophospholipid,fructose,and mannose metabolism,in long-term survival group.Multivariate Cox analysis identified fibrosis[hazard ratio(HR):12.70,95%confidence interval(95%CI):2.19-73.54,P=0.005]and RAC1copy number loss/deletion(HR:0.22,95%CI:0.06-0.83,P=0.026)as independent predictors of RFS.Higher fructose/mannose metabolism was associated with worse overall survival(HR:1.30,95%CI:1.01-1.68,P=0.045).Our findings emphasize the association between biological determinants and prolonged survival in patients with TNBC.Conclusions:Our study systematically identified the key molecular and metabolic features associated with prolonged survival in AJCC stage III TNBC,suggesting potential therapeutic targets to improve patient outcomes.
基金This work was supported by the National Natural Science Foundation of China(82103929,82273713)Young Elite Scientists Sponsorship Program by CAST(2022QNRC001)+7 种基金Fundamental Research Funds for the Central Universities(WHU:2042022kf1205)Knowledge Innovation Program of Wuhan(whkxjsj011)Translational Medicine and Interdisciplinary Research Joint Fund of Zhongnan Hospital of Wuhan University(ZNJC202207)for Jianbo TianDistinguished Young Scholars of China(81925032)Key Program of National Natural Science Foundation of China(82130098)the Leading Talent Program of the Health Commission of Hubei Province,Natural Science Foundation of Hubei Province(2019CFA009)the Fundamental Research Funds for the Central Universities(2042022rc0026,2042023kf1005)for Xiaoping Miaothe National Natural Science Foundation of China(82204128)for Xiaoyang Wang.
文摘Genome-wide association studies(GWASs)have identified over 140 colorectal cancer(CRC)-associated loci;however,target genes at the majority of loci and underlying molecular mechanisms are poorly understood.Here,we utilized a Bayesian approach,integrative risk gene selector(iRIGS),to prioritize risk genes at CRC GWAS loci by integrating multi-omics data.As a result,a total of 105 high-confidence risk genes(HRGs)were identified,which exhibited strong gene dependencies for CRC and enrichment in the biological processes implicated in CRC.Among the 105 HRGs,CEBPB,located at the 20q13.13 locus,acted as a transcription factor playing critical roles in cancer.Our subsequent assays indicated the tumor promoter function of CEBPB that facilitated CRC cell proliferation by regulating multiple oncogenic pathways such as MAPK,PI3K-Akt,and Ras signaling.Next,by integrating a fine-mapping analysis and three independent case-control studies in Chinese populations consisting of 8,039 cases and 12,775 controls,we elucidated that rs1810503,a putative functional variant regulating CEBPB,was associated with CRC risk(OR=0.90,95%CI=0.86–0.93,P=1.07×10^(−7)).The association between rs1810503 and CRC risk was further validated in three additional multi-ancestry populations consisting of 24,254 cases and 58,741 controls.Mechanistically,the rs1810503 A to T allele change weakened the enhancer activity in an allele-specific manner to decrease CEBPB expression via longrange promoter-enhancer interactions,mediated by the transcription factor,REST,and thus decreased CRC risk.In summary,our study provides a genetic resource and a generalizable strategy for CRC etiology investigation,and highlights the biological implications of CEBPB in CRC tumorigenesis,shedding new light on the etiology of CRC.
基金the Self-supporting Program of Guangzhou Laboratory(SRPG22-007)R&D Program of Guangzhou National Laboratory(GZNL2024A01002)+4 种基金National Natural Science Foundation of China(12371485,11871456)II Phase External Project of Guoke Ningbo Life Science and Health Industry Research Institute(2020YJY0217)Science and Technology Project of Yunnan Province(202103AQ100002)National Key R&D Program of China(2022YFF1202100)The Strategic Priority Research Program of the Chinese Academy of Sciences(XDB38050200,XDB38040202,XDA26040304).
文摘Childhood asthma is one of the most common respiratory diseases with rising mortality and morbidity.The multi-omics data is providing a new chance to explore collaborative biomarkers and corresponding diagnostic models of childhood asthma.To capture the nonlinear association of multi-omics data and improve interpretability of diagnostic model,we proposed a novel deep association model(DAM)and corresponding efficient analysis framework.First,the Deep Subspace Reconstruction was used to fuse the omics data and diagnostic information,thereby correcting the distribution of the original omics data and reducing the influence of unnecessary data noises.Second,the Joint Deep Semi-Negative Matrix Factorization was applied to identify different latent sample patterns and extract biomarkers from different omics data levels.Third,our newly proposed Deep Orthogonal Canonical Correlation Analysis can rank features in the collaborative module,which are able to construct the diagnostic model considering nonlinear correlation between different omics data levels.Using DAM,we deeply analyzed the transcriptome and methylation data of childhood asthma.The effectiveness of DAM is verified from the perspectives of algorithm performance and biological significance on the independent test dataset,by ablation experiment and comparison with many baseline methods from clinical and biological studies.The DAM-induced diagnostic model can achieve a prediction AUC of o.912,which is higher than that of many other alternative methods.Meanwhile,relevant pathways and biomarkers of childhood asthma are also recognized to be collectively altered on the gene expression and methylation levels.As an interpretable machine learning approach,DAM simultaneously considers the non-linear associations among samples and those among biological features,which should help explore interpretative biomarker candidates and efficient diagnostic models from multi-omics data analysis for human complexdiseases.
