The plastid genome(plastome)represents an indispensable molecular resource for studying plant phylogeny and evolution.Although plastome size is much smaller than that of nuclear genomes,accurately and efficientlyannot...The plastid genome(plastome)represents an indispensable molecular resource for studying plant phylogeny and evolution.Although plastome size is much smaller than that of nuclear genomes,accurately and efficientlyannotating and utilizing plastome sequences remain challenging.Therefore,a streamlined phylogenomic pipeline spanning plastome annotation,phylogenetic reconstruction and comparative genomics would greatly facilitate research utilizing this important organellar genome.Here,we develop PlastidHub,a novel web application employing innovative tools to analyze plastome sequences.In comparison with existing tools,key novel functionalities in PlastidHub include:(1)standardization of quadripartite structure;(2)improvement of annotation flexibility and consistency;(3)quantitative assessment of annotation completeness;(4)diverse extraction modes for canonical and specialized sequences;(5)intelligent screening of molecular markers for biodiversity studies;(6)genelevel visual comparison of structural variations and annotation completeness.PlastidHub features cloud-based web applications that do not require users to install,update,or maintain tools;detailed help documents including user guides,test examples,a static pop-up prompt box,and dynamic pop-up warning prompts when entering unreasonable parameter values;batch processing capabilities for all tools;intermediate results for secondary use;and easy-to-operate task flows between fileupload and download.A key feature of PlastidHub is its interrelated task-based user interface design.Give that PlastidHub is easy to use without specialized computational skills or resources,this new platform should be widely used among botanists and evolutionary biologists,improving and expediting research employing the plastome.PlastidHub is available at https://www.plastidhub.cn.展开更多
Artificial intelligence(AI)is significantly advancing precision medicine,particularly in the fields of immunogenomics,radiomics,and pathomics.In immunogenomics,AI can process vast amounts of genomic and multi-omic dat...Artificial intelligence(AI)is significantly advancing precision medicine,particularly in the fields of immunogenomics,radiomics,and pathomics.In immunogenomics,AI can process vast amounts of genomic and multi-omic data to identify biomarkers associated with immunotherapy responses and disease prognosis,thus providing strong support for personalized treatments.In radiomics,AI can analyze high-dimensional features from computed tomography(CT),magnetic resonance imaging(MRI),and positron emission tomography/computed tomography(PET/CT)images to discover imaging biomarkers associated with tumor heterogeneity,treatment response,and disease progression,thereby enabling non-invasive,real-time assessments for personalized therapy.Pathomics leverages AI for deep analysis of digital pathology images,and can uncover subtle changes in tissue microenvironments,cellular characteristics,and morphological features,and offer unique insights into immunotherapy response prediction and biomarker discovery.These AI-driven technologies not only enhance the speed,accuracy,and robustness of biomarker discovery but also significantly improve the precision,personalization,and effectiveness of clinical treatments,and are driving a shift from empirical to precision medicine.Despite challenges such as data quality,model interpretability,integration of multi-modal data,and privacy protection,the ongoing advancements in AI,coupled with interdisciplinary collaboration,are poised to further enhance AI’s roles in biomarker discovery and immunotherapy response prediction.These improvements are expected to lead to more accurate,personalized treatment strategies and ultimately better patient outcomes,marking a significant step forward in the evolution of precision medicine.展开更多
As a high-value eudicot family,many famous horticultural crop genomes have been deciphered in Oleaceae.However,there are currently no bioinformatics platforms focused on empowering genome research in Oleaceae.Herein,w...As a high-value eudicot family,many famous horticultural crop genomes have been deciphered in Oleaceae.However,there are currently no bioinformatics platforms focused on empowering genome research in Oleaceae.Herein,we developed the first comprehensive Oleaceae Genome Research Platform(OGRP,https://oleaceae.cgrpoee.top/).In OGRP,70 genomes of 10 Oleaceae species and 46 eudicots and 366 transcriptomes involving 18 Oleaceae plant tissues can be obtained.We built 34 window-operated bioinformatics tools,collected 38 professional practical software programs,and proposed 3 new pipelines,namely ancient polyploidization identification,ancestral karyotype reconstruction,and gene family evolution.Employing these pipelines to reanalyze the Oleaceae genomes,we clarified the polyploidization,reconstructed the ancestral karyotypes,and explored the effects of paleogenome evolution on genes with specific biological regulatory roles.Significantly,we generated a series of comparative genomic resources focusing on the Oleaceae,comprising 108 genomic synteny dot plots,1952225 collinear gene pairs,multiple genome alignments,and imprints of paleochromosome rearrangements.Moreover,in Oleaceae genomes,researchers can efficiently search for 1785987 functional annotations,22584 orthogroups,29582 important trait genes from 74 gene families,12664 transcription factor-related genes,9178872 transposable elements,and all involved regulatory pathways.In addition,we provided downloads and usage instructions for the tools,a species encyclopedia,ecological resources,relevant literatures,and external database links.In short,ORGP integrates rich data resources and powerful analytical tools with the characteristic of continuous updating,which can efficiently empower genome research and agricultural breeding in Oleaceae and other plants.展开更多
Background:Neoadjuvant chemotherapy(NAC)significantly enhances clinical outcomes in patients with triple-negative breast cancer(TNBC);however,chemoresistance frequently results in treatment failure.Consequently,unders...Background:Neoadjuvant chemotherapy(NAC)significantly enhances clinical outcomes in patients with triple-negative breast cancer(TNBC);however,chemoresistance frequently results in treatment failure.Consequently,understanding the mechanisms underlying resistance and accurately predicting this phenomenon are crucial for improving treatment efficacy.Methods:Ultrasound images from 62 patients,taken before and after neoadjuvant therapy,were collected.Mitochondrial-related genes were extracted from a public database.Ultrasound features associated with NAC resistance were identified and correlated with significant mitochondrial-related genes.Subsequently,a prognostic model was developed and evaluated using the GSE58812 dataset.We also assessed this model alongside clinical factors and its ability to predict immunotherapy response.Results:A total of 32 significant differentially expressed genes in TNBC across three groups indicated a strong correlation with ultrasound features.Univariate and multivariate Cox regression analyses identified six genes as independent risk factors for TNBC prognosis.Based on these six mitochondrial-related genes,we constructed a TNBC prognostic model.The model’s risk scores indicated that high-risk patients generally have a poorer prognosis compared to low-risk patients,with the model demonstrating high predictive performance(p=0.002,AUC=0.745).This conclusion was further supported in the test set(p=0.026,AUC=0.718).Additionally,we found that high-risk patients exhibited more advanced tumor characteristics,while low-risk patients were more sensitive to common chemotherapy drugs and immunotherapy.The signature-related genes also predicted immunotherapy response with a high accuracy of 0.765.Conclusion:We identified resistance-related features from ultrasound images and integrated them with genomic data,enabling effective risk stratification of patients and prediction of the efficacy of neoadjuvant chemotherapy and immunotherapy in patients with TNBC.展开更多
With the rapid development of high-throughput sequencing technologies and the accumulation of large-scale multi-omics data,deep learning(DL)has emerged as a powerful tool to solve complex biological problems,with part...With the rapid development of high-throughput sequencing technologies and the accumulation of large-scale multi-omics data,deep learning(DL)has emerged as a powerful tool to solve complex biological problems,with particular promise in plant genomics.This review systematically examines the progress of DL applications in DNA,RNA,and protein sequence analysis,covering key tasks such as gene regulatory element identification,gene function annotation,and protein structure prediction,and highlighting how these DL applications illuminate research of plants,including horticultural plants.We evaluate the advantages of different neural network architectures and their applications in different biology studies,as well as the development of large language models(LLMs)in genomic modelling,such as the plantspecific models PDLLMs and AgroNT.We also briefly introduce the general workflow of the basic DL model for plant genomics study.While DL has significantly improved prediction accuracy in plant genomics,its broader application remains constrained by several challenges,including the limited availability of well-annotated data,computational capacity,innovative model architectures adapted to plant genomes,and model interpretability.Future advances will require interdisciplinary collaborations to develop DL applications for intelligent plant genomic research frameworks with broader applicability.展开更多
Rice cultivation contributes up to 12%of global anthropogenic methane(CH4)emissions,making it a significant climate concern.With rice demand projected to double by 2050,achieving the required 2.4%annual genetic gain m...Rice cultivation contributes up to 12%of global anthropogenic methane(CH4)emissions,making it a significant climate concern.With rice demand projected to double by 2050,achieving the required 2.4%annual genetic gain must be balanced with emission reduction.This review synthesizes recent progress in three key areas:(1)mitigation strategies such as alternate wetting and drying and direct-seeded rice,which can reduce CH4 emissions by 30%-40%;(2)identification of physiological and molecular traits,such as short duration,high harvest index,improved nitrogen use efficiency,optimized root architecture,and stress tolerance with reduced greenhouse gas(GHG)footprints;and(3)the potential of genomics-assisted breeding and high-throughput phenotyping to accelerate the development of climate-resilient rice varieties with lower CH4 emissions.Specifically,we highlight how the synergistic integration of high-throughput phenotyping,genomic selection,and marker-assisted breeding can substantially improve the efficiency and precision of breeding programs targeting the development of climate-resilient rice varieties with reduced CH4 emissions.This is exemplified through successful case studies utilizing multi-omics approaches,including the development of Green Super Rice varieties(GSR 2 and GSR 8),which have demonstrated up to a 37%reduction in GHG emissions.Crucially,we propose a stratified trait profile for low-GHG rice development and provide guidelines and metrics for integrating these traits into mainstream breeding pipelines.We conclude by proposing a strategic framework integrating carbon-efficient breeding,climate-adapted agronomy,and policy support,which is essential for scaling low-GHG rice systems globally.展开更多
The low egg production of goose greatly limits the development of the industry.China possesses the most abundant goose breeds resources.In this study,genome resequencing data of swan goose(Anser cygnoides)and domestic...The low egg production of goose greatly limits the development of the industry.China possesses the most abundant goose breeds resources.In this study,genome resequencing data of swan goose(Anser cygnoides)and domesticated high and low laying goose breeds(Anser cygnoides domestiation)were used to identify key genes related to egg laying ability in geese and verify their functions.Selective sweep analyses revealed 416 genes that were specifically selected during the domestication process from swan geese to high laying geese.Furthermore,SNPs and Indels markers were used in GWAS analyses between high and low laying breed geese.The results showed that RTCB,BPIFC,SYN3,SYNE1,VIP,and ESR1 may be related to the differences in laying ability of geese.Notably,only ESR1 was identified simultaneously by GWAS and selective sweep analysis.The genotype of Indelchr3:54429172,located downstream of ESR1,was confirmed to affect the expression of ESR1 in the ovarian stroma and showed significant correlation with body weight at first egg and laying frequency of geese.CCK-8,EdU,and flow cytometry confirmed that ESR1 can promote the apoptosis of goose pre-hierarchical follicles ganulosa cells(phGCs)and inhibit their proliferation.Combined with transcriptome data,it was found ESR1 involved in the function of goose phGCs may be related to MAPK and TGF-beta signaling pathways.Overall,our study used genomic information from different goose breeds to identify an indel located in the downstream of ESR1 associated with goose laying ability.The main pathways and biological processes of ESR1 involved in the regulation of goose laying ability were identified by cell biology and transcriptomics methods.These results are helpful to further understand the laying ability characteristics of goose and improve the egg production of geese.展开更多
The Beijing Institute of Genomics(BIG)of the Chinese Academy of Sciences,as the leading Institute in Genomics,has walked through 20 year’s journey since being founded in November 2003.From participating in the Human ...The Beijing Institute of Genomics(BIG)of the Chinese Academy of Sciences,as the leading Institute in Genomics,has walked through 20 year’s journey since being founded in November 2003.From participating in the Human Genome Project(HGP)in completing the“1%task”to independently accomplishing the super-hybrid rice genome and other several national and international genome projects,BIG has made tremendous contributions in genomics research and development in China.In 2024,bearing great ambition and responsibility,BIG is transformed to the China National Center for Bioinformation(CNCB),aiming to become a global hub in bioinformatics big data services,innovation,and entrepreneurship.With the completion of its new infrastructure in 2027,CNCB is looking into a brighter future.展开更多
Sorghum,renowned for its substantial biomass production and remarkable tolerance to various stresses,possesses extensive gene resources and phenotypic variations.A comprehensive understanding of the genetic basis unde...Sorghum,renowned for its substantial biomass production and remarkable tolerance to various stresses,possesses extensive gene resources and phenotypic variations.A comprehensive understanding of the genetic basis underlying complex agronomic traits is essential for unlocking the potential of sorghum in addressing food and feed security and utilizing marginal lands.In this context,we provide an overview of the major trends in genomic resource studies focusing on key agronomic traits over the past decade,accompanied by a summary of functional genomic platforms.We also delve into the molecular functions and regulatory networks of impactful genes for important agricultural traits.Lastly,we discuss and synthesize the current challenges and prospects for advancing molecular design breeding by gene-editing and polymerization of the excellent alleles,with the aim of accelerating the development of desired sorghum varieties.展开更多
Plant germplasm underpins much of crop genetic improvement. Millions of germplasm accessions have been collected and conserved ex situ and/or in situ, and the major challenge is now how to exploit and utilize this abu...Plant germplasm underpins much of crop genetic improvement. Millions of germplasm accessions have been collected and conserved ex situ and/or in situ, and the major challenge is now how to exploit and utilize this abundant resource. Genomics-based plant germplasm research （GPGR） or ＂Cenoplasmics＂ is a novel cross-disciplinary research field that seeks to apply the principles and techniques of genomics to germplasm research. We describe in this paper the concept, strategy, and approach behind GPGR, and summarize current progress in the areas of the definition and construction of core collections, enhancement of germplasm with core collections, and gene discovery from core collections. GPGR is opening a new era in germplasm research. The contribution, progress and achievements of GPGR in the future are predicted.展开更多
Horseshoe bats(genus Rhinolophus,family Rhinolophidae)represent an important group within chiropteran phylogeny due to their distinctive traits,including constant high-frequency echolocation,rapid karyotype evolution,...Horseshoe bats(genus Rhinolophus,family Rhinolophidae)represent an important group within chiropteran phylogeny due to their distinctive traits,including constant high-frequency echolocation,rapid karyotype evolution,and unique immune system.Advances in evolutionary biology,supported by high-quality reference genomes and comprehensive whole-genome data,have significantly enhanced our understanding of species origins,speciation mechanisms,adaptive evolutionary processes,and phenotypic diversity.However,genomic research and understanding of the evolutionary patterns of Rhinolophus are severely constrained by limited data,with only a single published genome of R.ferrumequinum currently available.In this study,we constructed a high-quality chromosome-level reference genome for the intermediate horseshoe bat(R.affinis).Comparative genomic analyses revealed potential genetic characteristics associated with virus tolerance in Rhinolophidae.Notably,we observed expansions in several immune-related gene families and identified various genes functionally associated with the SARS-CoV-2 signaling pathway,DNA repair,and apoptosis,which displayed signs of rapid evolution.In addition,we observed an expansion of the major histocompatibility complex class II(MHC-II)region and a higher copy number of the HLA-DQB2 gene in horseshoe bats compared to other chiropteran species.Based on whole-genome resequencing and population genomic analyses,we identified multiple candidate loci(e.g.,GLI3)associated with variations in echolocation call frequency across R.affinis subspecies.This research not only expands our understanding of the genetic characteristics of the Rhinolophus genus but also establishes a valuable foundation for future research.展开更多
This article reviews basic concepts, general applications, and the potential impact of next-generation sequencing （NGS） technologies on genomics, with particular reference to currently available and possible future ...This article reviews basic concepts, general applications, and the potential impact of next-generation sequencing （NGS） technologies on genomics, with particular reference to currently available and possible future platforms and bioinformatics. NGS technologies have demon- strated the capacity to sequence DNA at unprecedented speed, thereby enabling previously unimaginable scientific achievements and novel biological applications. But, the massive data produced by NGS also presents a significant challenge for data storage, analyses, and management solutions. Advanced bioinformatic tools are essential for the successful application of NGS technology. As evidenced throughout this review, NGS technologies will have a striking impact on genomic research and the entire biological field. With its ability to tackle the unsolved challenges unconquered by previous genomic technologies, NGS is likely to unravel the complexity of the human genome in terms of genetic variations, some of which may be confined to susceptible loci for some common human conditions. The impact of NGS technologies on genomics will be far reaching and likely change the field for years to come.展开更多
The study of gene function in filamentous fungi is a field of research that has made great advances in very recent years. A number of transformation and gene manipulation strategies have been developed and applied to ...The study of gene function in filamentous fungi is a field of research that has made great advances in very recent years. A number of transformation and gene manipulation strategies have been developed and applied to a diverse and rapidly expanding list of economically important filamentous fungi and oomycetes. With the significant number of fungal genomes now sequenced or being sequenced, functional genomics promises to uncover a great deal of new information in coming years. This review discusses recent advances that have been made in examining gene function in filamentous fungi and describes the advantages and limitations of the different approaches.展开更多
Enterovirus 71(EV71) is one of the main pathogens that causes hand-foot-and-mouth disease(HFMD). HFMD caused by EV71 infection is mostly self-limited; however, some infections can cause severe neurological diseases, s...Enterovirus 71(EV71) is one of the main pathogens that causes hand-foot-and-mouth disease(HFMD). HFMD caused by EV71 infection is mostly self-limited; however, some infections can cause severe neurological diseases, such as aseptic meningitis, brain stem encephalitis, and even death. There are still no effective clinical drugs used for the prevention and treatment of HFMD. Studying EV71 protein function is essential for elucidating the EV71 replication process and developing anti-EV71 drugs and vaccines. In this review, we summarized the recent progress in the studies of EV71 noncoding regions(50 UTR and 30 UTR) and all structural and nonstructural proteins, especially the key motifs involving in viral infection, replication, and immune regulation. This review will promote our understanding of EV71 virus replication and pathogenesis, and will facilitate the development of novel drugs or vaccines to treat EV71.展开更多
Alfalfa(M. sativa L.) is a highly valuable forage crop, providing >58 Mt of hay, silage, and pasture each year in the United States. As alfalfa is an outcrossing autotetraploid crop,however, breeding for enhanced a...Alfalfa(M. sativa L.) is a highly valuable forage crop, providing >58 Mt of hay, silage, and pasture each year in the United States. As alfalfa is an outcrossing autotetraploid crop,however, breeding for enhanced agronomic traits is challenging and progress has historically not been rapid. Methods that make use of genotypic information and statistical models to generate a genomic estimated breeding value(GEBV) for each plant at a young age hold a great deal of promise to accelerate breeding gains. An emerging genomic breeding pipeline employs SNP chips or genotyping-by-sequencing(GBS) to identify SNP markers in a training population, followed by the use of a statistical model to find associations between the discovered SNPs and traits of interest, followed by genomic selection(GS), a breeding program utilizing the trained model to predict breeding values and making selections based on the estimated breeding value(EBV). Much work has been done in recent years in all of these areas, to generate marker sets and discover SNPs associated with desirable traits, and the application of these technologies in alfalfa breeding programs is under way. However, GBS/GWAS/GS is still a new breeding paradigm,and work is ongoing to evaluate different models, software, and methods for use in such programs. In this review, we look at the progress of alfalfa genomics over the past halfdecade, and review work comparing models and methods relevant to this new type of breeding strategy.展开更多
Proso millet (Panicummiliaceum) has highwater use efficiency (WUE), a short growing-season, and is highly adapted to a semi-arid climate. Genomic resources for proso millet are very limited. Large numbers of DNA marke...Proso millet (Panicummiliaceum) has highwater use efficiency (WUE), a short growing-season, and is highly adapted to a semi-arid climate. Genomic resources for proso millet are very limited. Large numbers of DNA markers and other genomic tools in proso millet can readily be developed by using genomic resources in related grasses. The objectives of the present report were to 1) test and characterize switchgrass SSR markers for use in proso millet, and 2) elucidate repeat-motifs in proso millet based on new SSR marker analysis. A total of 548 SSR markers were tested on 8 proso millet genotypes. Out of these, 339 amplified SSR markers in proso millet. This showed that 62% of the switchgrass SSR markers were transferable to proso millet. Of these 339 markers, 254 were highly polymorphic among the 8 proso genotypes. The resolving power of these 254 polymorphic SSR markers ranged from 0.25-14.75 with an average of 2.71. The 254 polymorphic SSR markers amplified 984 alleles in the ranges of 50 bp to 1300 bp. The majority of the SSR markers (221 of 254) amplified dinucleotide repeats. Based on SSR marker analysis, AG/GA was the most abundant repeat-motifs in proso millet. Switchgrass genomic information seems to be the most useful for developing DNA markers in proso millet. Markers developed in this study will be helpful for linkage map construction, mapping agronomic traits and future molecular breeding efforts in proso millet.展开更多
In the last decade,the focus of computational pathology research community has shifted from replicating the pathological examination for diagnosis done by pathologists to unlocking and discovering"sub-visual"...In the last decade,the focus of computational pathology research community has shifted from replicating the pathological examination for diagnosis done by pathologists to unlocking and discovering"sub-visual"prognostic image cues from the histopathological image.While we are getting more knowledge and experience in digital pathology,the emerging goal is to integrate other-omics or modalities that will contribute for building a better prognostic assay.In this paper,we provide a brief review of representative works that focus on integrating pathomics with radiomics and genomics for cancer prognosis.It includes:correlation of pathomics and genomics;fusion of pathomics and genomics;fusion of pathomics and radiomics.We also present challenges,potential opportunities,and avenues for future work.展开更多
基金the Natural Science Foundation of Shandong Province(ZR2020QC022)the Science and Technology Basic Resources Investigation Program of China(No.2019FY100900)+2 种基金the Major Program for Basic Research Project of Yunnan Province(202401BC070001)Yunnan Revitalization Talent Support Program:Yunling Scholar Project to Tingshuang Yithe open research project of“Cross Cooperative Team”of the Germplasm Bank of Wild Species,Kunming Institute of Botany,Chinese Academy of Sciences.
文摘The plastid genome(plastome)represents an indispensable molecular resource for studying plant phylogeny and evolution.Although plastome size is much smaller than that of nuclear genomes,accurately and efficientlyannotating and utilizing plastome sequences remain challenging.Therefore,a streamlined phylogenomic pipeline spanning plastome annotation,phylogenetic reconstruction and comparative genomics would greatly facilitate research utilizing this important organellar genome.Here,we develop PlastidHub,a novel web application employing innovative tools to analyze plastome sequences.In comparison with existing tools,key novel functionalities in PlastidHub include:(1)standardization of quadripartite structure;(2)improvement of annotation flexibility and consistency;(3)quantitative assessment of annotation completeness;(4)diverse extraction modes for canonical and specialized sequences;(5)intelligent screening of molecular markers for biodiversity studies;(6)genelevel visual comparison of structural variations and annotation completeness.PlastidHub features cloud-based web applications that do not require users to install,update,or maintain tools;detailed help documents including user guides,test examples,a static pop-up prompt box,and dynamic pop-up warning prompts when entering unreasonable parameter values;batch processing capabilities for all tools;intermediate results for secondary use;and easy-to-operate task flows between fileupload and download.A key feature of PlastidHub is its interrelated task-based user interface design.Give that PlastidHub is easy to use without specialized computational skills or resources,this new platform should be widely used among botanists and evolutionary biologists,improving and expediting research employing the plastome.PlastidHub is available at https://www.plastidhub.cn.
基金supported by grants from the National Natural Science Foundation of China(Grant No.82272008)The Science&Technology Development Fund of Tianjin Education Commission for Higher Education(Grant No.2021KJ194)Tianjin Key Medical Discipline(Specialty)Construction Project(Grant No.TJYXZDXK-009A).
文摘Artificial intelligence(AI)is significantly advancing precision medicine,particularly in the fields of immunogenomics,radiomics,and pathomics.In immunogenomics,AI can process vast amounts of genomic and multi-omic data to identify biomarkers associated with immunotherapy responses and disease prognosis,thus providing strong support for personalized treatments.In radiomics,AI can analyze high-dimensional features from computed tomography(CT),magnetic resonance imaging(MRI),and positron emission tomography/computed tomography(PET/CT)images to discover imaging biomarkers associated with tumor heterogeneity,treatment response,and disease progression,thereby enabling non-invasive,real-time assessments for personalized therapy.Pathomics leverages AI for deep analysis of digital pathology images,and can uncover subtle changes in tissue microenvironments,cellular characteristics,and morphological features,and offer unique insights into immunotherapy response prediction and biomarker discovery.These AI-driven technologies not only enhance the speed,accuracy,and robustness of biomarker discovery but also significantly improve the precision,personalization,and effectiveness of clinical treatments,and are driving a shift from empirical to precision medicine.Despite challenges such as data quality,model interpretability,integration of multi-modal data,and privacy protection,the ongoing advancements in AI,coupled with interdisciplinary collaboration,are poised to further enhance AI’s roles in biomarker discovery and immunotherapy response prediction.These improvements are expected to lead to more accurate,personalized treatment strategies and ultimately better patient outcomes,marking a significant step forward in the evolution of precision medicine.
基金supported by the National Natural Science Foundation of China(32470676 and 32170236)Central Guidance on Local Science and Technology Development Fund of Hebei Province(246Z2508G)+2 种基金Hebei Natural Science Foundation(C2020209064)Tangshan Science and Technology Program Project(21130217C)Key research project of North China University of Science and Technology(ZD-YG-202313-23).
文摘As a high-value eudicot family,many famous horticultural crop genomes have been deciphered in Oleaceae.However,there are currently no bioinformatics platforms focused on empowering genome research in Oleaceae.Herein,we developed the first comprehensive Oleaceae Genome Research Platform(OGRP,https://oleaceae.cgrpoee.top/).In OGRP,70 genomes of 10 Oleaceae species and 46 eudicots and 366 transcriptomes involving 18 Oleaceae plant tissues can be obtained.We built 34 window-operated bioinformatics tools,collected 38 professional practical software programs,and proposed 3 new pipelines,namely ancient polyploidization identification,ancestral karyotype reconstruction,and gene family evolution.Employing these pipelines to reanalyze the Oleaceae genomes,we clarified the polyploidization,reconstructed the ancestral karyotypes,and explored the effects of paleogenome evolution on genes with specific biological regulatory roles.Significantly,we generated a series of comparative genomic resources focusing on the Oleaceae,comprising 108 genomic synteny dot plots,1952225 collinear gene pairs,multiple genome alignments,and imprints of paleochromosome rearrangements.Moreover,in Oleaceae genomes,researchers can efficiently search for 1785987 functional annotations,22584 orthogroups,29582 important trait genes from 74 gene families,12664 transcription factor-related genes,9178872 transposable elements,and all involved regulatory pathways.In addition,we provided downloads and usage instructions for the tools,a species encyclopedia,ecological resources,relevant literatures,and external database links.In short,ORGP integrates rich data resources and powerful analytical tools with the characteristic of continuous updating,which can efficiently empower genome research and agricultural breeding in Oleaceae and other plants.
基金supported by Wu Jieping Medical Foundation(320.6750.2022-19-40 and 320.6750.2024-18-41)Guangxi University Young and Middle-Aged Teachers Research Basic Ability Improvement Project(2024KY0510)Guangxi Health Commission Self-Funded Research Project(Z-C20231002).
文摘Background:Neoadjuvant chemotherapy(NAC)significantly enhances clinical outcomes in patients with triple-negative breast cancer(TNBC);however,chemoresistance frequently results in treatment failure.Consequently,understanding the mechanisms underlying resistance and accurately predicting this phenomenon are crucial for improving treatment efficacy.Methods:Ultrasound images from 62 patients,taken before and after neoadjuvant therapy,were collected.Mitochondrial-related genes were extracted from a public database.Ultrasound features associated with NAC resistance were identified and correlated with significant mitochondrial-related genes.Subsequently,a prognostic model was developed and evaluated using the GSE58812 dataset.We also assessed this model alongside clinical factors and its ability to predict immunotherapy response.Results:A total of 32 significant differentially expressed genes in TNBC across three groups indicated a strong correlation with ultrasound features.Univariate and multivariate Cox regression analyses identified six genes as independent risk factors for TNBC prognosis.Based on these six mitochondrial-related genes,we constructed a TNBC prognostic model.The model’s risk scores indicated that high-risk patients generally have a poorer prognosis compared to low-risk patients,with the model demonstrating high predictive performance(p=0.002,AUC=0.745).This conclusion was further supported in the test set(p=0.026,AUC=0.718).Additionally,we found that high-risk patients exhibited more advanced tumor characteristics,while low-risk patients were more sensitive to common chemotherapy drugs and immunotherapy.The signature-related genes also predicted immunotherapy response with a high accuracy of 0.765.Conclusion:We identified resistance-related features from ultrasound images and integrated them with genomic data,enabling effective risk stratification of patients and prediction of the efficacy of neoadjuvant chemotherapy and immunotherapy in patients with TNBC.
基金supported by the National Natural Science Foundation of China(Grant Nos.U23A20210,31722048,and 32102382)Central Public-interest Scientific Institution Basal Research Fund,The Agricultural Science and Technology Innovation Program of the Chinese Academy of Agricultural SciencesKey Laboratory of Biology and Genetic Improvement of Horticultural Crops,Ministry of Agriculture and Rural Affairs,China.
文摘With the rapid development of high-throughput sequencing technologies and the accumulation of large-scale multi-omics data,deep learning(DL)has emerged as a powerful tool to solve complex biological problems,with particular promise in plant genomics.This review systematically examines the progress of DL applications in DNA,RNA,and protein sequence analysis,covering key tasks such as gene regulatory element identification,gene function annotation,and protein structure prediction,and highlighting how these DL applications illuminate research of plants,including horticultural plants.We evaluate the advantages of different neural network architectures and their applications in different biology studies,as well as the development of large language models(LLMs)in genomic modelling,such as the plantspecific models PDLLMs and AgroNT.We also briefly introduce the general workflow of the basic DL model for plant genomics study.While DL has significantly improved prediction accuracy in plant genomics,its broader application remains constrained by several challenges,including the limited availability of well-annotated data,computational capacity,innovative model architectures adapted to plant genomes,and model interpretability.Future advances will require interdisciplinary collaborations to develop DL applications for intelligent plant genomic research frameworks with broader applicability.
基金supported by the Consultative Group of International Agricultural Research,Science Programme-Breeding for Tomorrow,India(Grant No.SP01/B4T/AoW02).
文摘Rice cultivation contributes up to 12%of global anthropogenic methane(CH4)emissions,making it a significant climate concern.With rice demand projected to double by 2050,achieving the required 2.4%annual genetic gain must be balanced with emission reduction.This review synthesizes recent progress in three key areas:(1)mitigation strategies such as alternate wetting and drying and direct-seeded rice,which can reduce CH4 emissions by 30%-40%;(2)identification of physiological and molecular traits,such as short duration,high harvest index,improved nitrogen use efficiency,optimized root architecture,and stress tolerance with reduced greenhouse gas(GHG)footprints;and(3)the potential of genomics-assisted breeding and high-throughput phenotyping to accelerate the development of climate-resilient rice varieties with lower CH4 emissions.Specifically,we highlight how the synergistic integration of high-throughput phenotyping,genomic selection,and marker-assisted breeding can substantially improve the efficiency and precision of breeding programs targeting the development of climate-resilient rice varieties with reduced CH4 emissions.This is exemplified through successful case studies utilizing multi-omics approaches,including the development of Green Super Rice varieties(GSR 2 and GSR 8),which have demonstrated up to a 37%reduction in GHG emissions.Crucially,we propose a stratified trait profile for low-GHG rice development and provide guidelines and metrics for integrating these traits into mainstream breeding pipelines.We conclude by proposing a strategic framework integrating carbon-efficient breeding,climate-adapted agronomy,and policy support,which is essential for scaling low-GHG rice systems globally.
基金supported by the China Agriculture Research System of MOF and MARA(CARS-42-4)School Cooperation Project of Ya’an(21SXHZ0028)the Key Technology Support Program of Sichuan Province,China(2021YFYZ0014),for the financial support。
文摘The low egg production of goose greatly limits the development of the industry.China possesses the most abundant goose breeds resources.In this study,genome resequencing data of swan goose(Anser cygnoides)and domesticated high and low laying goose breeds(Anser cygnoides domestiation)were used to identify key genes related to egg laying ability in geese and verify their functions.Selective sweep analyses revealed 416 genes that were specifically selected during the domestication process from swan geese to high laying geese.Furthermore,SNPs and Indels markers were used in GWAS analyses between high and low laying breed geese.The results showed that RTCB,BPIFC,SYN3,SYNE1,VIP,and ESR1 may be related to the differences in laying ability of geese.Notably,only ESR1 was identified simultaneously by GWAS and selective sweep analysis.The genotype of Indelchr3:54429172,located downstream of ESR1,was confirmed to affect the expression of ESR1 in the ovarian stroma and showed significant correlation with body weight at first egg and laying frequency of geese.CCK-8,EdU,and flow cytometry confirmed that ESR1 can promote the apoptosis of goose pre-hierarchical follicles ganulosa cells(phGCs)and inhibit their proliferation.Combined with transcriptome data,it was found ESR1 involved in the function of goose phGCs may be related to MAPK and TGF-beta signaling pathways.Overall,our study used genomic information from different goose breeds to identify an indel located in the downstream of ESR1 associated with goose laying ability.The main pathways and biological processes of ESR1 involved in the regulation of goose laying ability were identified by cell biology and transcriptomics methods.These results are helpful to further understand the laying ability characteristics of goose and improve the egg production of geese.
文摘The Beijing Institute of Genomics(BIG)of the Chinese Academy of Sciences,as the leading Institute in Genomics,has walked through 20 year’s journey since being founded in November 2003.From participating in the Human Genome Project(HGP)in completing the“1%task”to independently accomplishing the super-hybrid rice genome and other several national and international genome projects,BIG has made tremendous contributions in genomics research and development in China.In 2024,bearing great ambition and responsibility,BIG is transformed to the China National Center for Bioinformation(CNCB),aiming to become a global hub in bioinformatics big data services,innovation,and entrepreneurship.With the completion of its new infrastructure in 2027,CNCB is looking into a brighter future.
基金the National Science Foundation for Young Scientists of China(32201780)the Fundamental Research Fund for the Central Universities(77000–12240011)+2 种基金Shenzhen Postdoctoral Funding Project(szbo202410)the National Natural Science Foundation of China(32241045 and 32241038)the National Key Research and Development Program of China(2022YFD1500503,2023YFD1200700,and 2023YFD1200704).
文摘Sorghum,renowned for its substantial biomass production and remarkable tolerance to various stresses,possesses extensive gene resources and phenotypic variations.A comprehensive understanding of the genetic basis underlying complex agronomic traits is essential for unlocking the potential of sorghum in addressing food and feed security and utilizing marginal lands.In this context,we provide an overview of the major trends in genomic resource studies focusing on key agronomic traits over the past decade,accompanied by a summary of functional genomic platforms.We also delve into the molecular functions and regulatory networks of impactful genes for important agricultural traits.Lastly,we discuss and synthesize the current challenges and prospects for advancing molecular design breeding by gene-editing and polymerization of the excellent alleles,with the aim of accelerating the development of desired sorghum varieties.
基金supported by the National Basic Research Program of China(No.2004CB117200)the National Natural Science Foundation of China(No.31261140368)
文摘Plant germplasm underpins much of crop genetic improvement. Millions of germplasm accessions have been collected and conserved ex situ and/or in situ, and the major challenge is now how to exploit and utilize this abundant resource. Genomics-based plant germplasm research （GPGR） or ＂Cenoplasmics＂ is a novel cross-disciplinary research field that seeks to apply the principles and techniques of genomics to germplasm research. We describe in this paper the concept, strategy, and approach behind GPGR, and summarize current progress in the areas of the definition and construction of core collections, enhancement of germplasm with core collections, and gene discovery from core collections. GPGR is opening a new era in germplasm research. The contribution, progress and achievements of GPGR in the future are predicted.
基金supported by the China Postdoctoral Science Foundation(2022M722020)to Z.L.Key Project of Scientific Research Program of Shaanxi Provincial Education Department(23JY020)to Z.L.+5 种基金Natural Science Basic Research Program of Shaanxi(2024JCYBMS-152)to Z.L.Key Projects of Shaanxi University of Technology(SLGKYXM2302)to Z.L.Opening Foundation of Shaanxi University of Technology(SLGPT2019KF02-02)to Z.L.Natural Science Basic Research Program of Shaanxi(2020JM-280)to G.L.Fundamental Research Funds for the Central Universities(GK201902008)to G.LNational Natural Science Foundation of China(31570378)to X.M.
文摘Horseshoe bats(genus Rhinolophus,family Rhinolophidae)represent an important group within chiropteran phylogeny due to their distinctive traits,including constant high-frequency echolocation,rapid karyotype evolution,and unique immune system.Advances in evolutionary biology,supported by high-quality reference genomes and comprehensive whole-genome data,have significantly enhanced our understanding of species origins,speciation mechanisms,adaptive evolutionary processes,and phenotypic diversity.However,genomic research and understanding of the evolutionary patterns of Rhinolophus are severely constrained by limited data,with only a single published genome of R.ferrumequinum currently available.In this study,we constructed a high-quality chromosome-level reference genome for the intermediate horseshoe bat(R.affinis).Comparative genomic analyses revealed potential genetic characteristics associated with virus tolerance in Rhinolophidae.Notably,we observed expansions in several immune-related gene families and identified various genes functionally associated with the SARS-CoV-2 signaling pathway,DNA repair,and apoptosis,which displayed signs of rapid evolution.In addition,we observed an expansion of the major histocompatibility complex class II(MHC-II)region and a higher copy number of the HLA-DQB2 gene in horseshoe bats compared to other chiropteran species.Based on whole-genome resequencing and population genomic analyses,we identified multiple candidate loci(e.g.,GLI3)associated with variations in echolocation call frequency across R.affinis subspecies.This research not only expands our understanding of the genetic characteristics of the Rhinolophus genus but also establishes a valuable foundation for future research.
基金supported by NINDS/NIH(JZ),Coldwell Foundation(JZ) and TTUHSC(JZ)
文摘This article reviews basic concepts, general applications, and the potential impact of next-generation sequencing （NGS） technologies on genomics, with particular reference to currently available and possible future platforms and bioinformatics. NGS technologies have demon- strated the capacity to sequence DNA at unprecedented speed, thereby enabling previously unimaginable scientific achievements and novel biological applications. But, the massive data produced by NGS also presents a significant challenge for data storage, analyses, and management solutions. Advanced bioinformatic tools are essential for the successful application of NGS technology. As evidenced throughout this review, NGS technologies will have a striking impact on genomic research and the entire biological field. With its ability to tackle the unsolved challenges unconquered by previous genomic technologies, NGS is likely to unravel the complexity of the human genome in terms of genetic variations, some of which may be confined to susceptible loci for some common human conditions. The impact of NGS technologies on genomics will be far reaching and likely change the field for years to come.
文摘The study of gene function in filamentous fungi is a field of research that has made great advances in very recent years. A number of transformation and gene manipulation strategies have been developed and applied to a diverse and rapidly expanding list of economically important filamentous fungi and oomycetes. With the significant number of fungal genomes now sequenced or being sequenced, functional genomics promises to uncover a great deal of new information in coming years. This review discusses recent advances that have been made in examining gene function in filamentous fungi and describes the advantages and limitations of the different approaches.
基金supported by the National Natural Science Foundation of China (Grant 81503118)CAMS Initiative for Innovative Medicine (CAMS-I2 M-1-010)The National Science and Technology Major Project of the Ministry of Science and Technology of China (2018ZX09711003-005-004)
文摘Enterovirus 71(EV71) is one of the main pathogens that causes hand-foot-and-mouth disease(HFMD). HFMD caused by EV71 infection is mostly self-limited; however, some infections can cause severe neurological diseases, such as aseptic meningitis, brain stem encephalitis, and even death. There are still no effective clinical drugs used for the prevention and treatment of HFMD. Studying EV71 protein function is essential for elucidating the EV71 replication process and developing anti-EV71 drugs and vaccines. In this review, we summarized the recent progress in the studies of EV71 noncoding regions(50 UTR and 30 UTR) and all structural and nonstructural proteins, especially the key motifs involving in viral infection, replication, and immune regulation. This review will promote our understanding of EV71 virus replication and pathogenesis, and will facilitate the development of novel drugs or vaccines to treat EV71.
基金supported by the United States Department of Agriculture NIFA_AFRP(2015-70005-24071)the Agricultural Research Service base fund
文摘Alfalfa(M. sativa L.) is a highly valuable forage crop, providing >58 Mt of hay, silage, and pasture each year in the United States. As alfalfa is an outcrossing autotetraploid crop,however, breeding for enhanced agronomic traits is challenging and progress has historically not been rapid. Methods that make use of genotypic information and statistical models to generate a genomic estimated breeding value(GEBV) for each plant at a young age hold a great deal of promise to accelerate breeding gains. An emerging genomic breeding pipeline employs SNP chips or genotyping-by-sequencing(GBS) to identify SNP markers in a training population, followed by the use of a statistical model to find associations between the discovered SNPs and traits of interest, followed by genomic selection(GS), a breeding program utilizing the trained model to predict breeding values and making selections based on the estimated breeding value(EBV). Much work has been done in recent years in all of these areas, to generate marker sets and discover SNPs associated with desirable traits, and the application of these technologies in alfalfa breeding programs is under way. However, GBS/GWAS/GS is still a new breeding paradigm,and work is ongoing to evaluate different models, software, and methods for use in such programs. In this review, we look at the progress of alfalfa genomics over the past halfdecade, and review work comparing models and methods relevant to this new type of breeding strategy.
文摘Proso millet (Panicummiliaceum) has highwater use efficiency (WUE), a short growing-season, and is highly adapted to a semi-arid climate. Genomic resources for proso millet are very limited. Large numbers of DNA markers and other genomic tools in proso millet can readily be developed by using genomic resources in related grasses. The objectives of the present report were to 1) test and characterize switchgrass SSR markers for use in proso millet, and 2) elucidate repeat-motifs in proso millet based on new SSR marker analysis. A total of 548 SSR markers were tested on 8 proso millet genotypes. Out of these, 339 amplified SSR markers in proso millet. This showed that 62% of the switchgrass SSR markers were transferable to proso millet. Of these 339 markers, 254 were highly polymorphic among the 8 proso genotypes. The resolving power of these 254 polymorphic SSR markers ranged from 0.25-14.75 with an average of 2.71. The 254 polymorphic SSR markers amplified 984 alleles in the ranges of 50 bp to 1300 bp. The majority of the SSR markers (221 of 254) amplified dinucleotide repeats. Based on SSR marker analysis, AG/GA was the most abundant repeat-motifs in proso millet. Switchgrass genomic information seems to be the most useful for developing DNA markers in proso millet. Markers developed in this study will be helpful for linkage map construction, mapping agronomic traits and future molecular breeding efforts in proso millet.
基金supported by the DoD Breast Cancer Research Program Breakthrough Level 1 Award W81XWH-19-1-0668,NIH-NCI R21 CA253108-01DoD Prostate Cancer Research Program Idea Development Award W81XWH-18-1-0524+2 种基金Key R&D Program of Guangdong Province,China(No.2021B0101420006)National Science Fund for Distinguished Young Scholars,China(No.81925023)National Natural Science Foundation of China(No.62002082,62102103,61906050,81771912)。
文摘In the last decade,the focus of computational pathology research community has shifted from replicating the pathological examination for diagnosis done by pathologists to unlocking and discovering"sub-visual"prognostic image cues from the histopathological image.While we are getting more knowledge and experience in digital pathology,the emerging goal is to integrate other-omics or modalities that will contribute for building a better prognostic assay.In this paper,we provide a brief review of representative works that focus on integrating pathomics with radiomics and genomics for cancer prognosis.It includes:correlation of pathomics and genomics;fusion of pathomics and genomics;fusion of pathomics and radiomics.We also present challenges,potential opportunities,and avenues for future work.
