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
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
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
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
Rice and wheat provide nearly 40%of human calorie and protein requirements.They share a common ancestor and belong to the Poaceae(grass)family.Characterizing their genetic homology is crucial for developing new cultiv...Rice and wheat provide nearly 40%of human calorie and protein requirements.They share a common ancestor and belong to the Poaceae(grass)family.Characterizing their genetic homology is crucial for developing new cultivars with enhanced traits.Several wheat genes and gene families have been characterized based on their rice orthologs.Rice–wheat orthology can identify genetic regions that regulate similar traits in both crops.Rice–wheat comparative genomics can identify candidate wheat genes in a genomic region identified by association or QTL mapping,deduce their putative functions and biochemical pathways,and develop molecular markers for marker-assisted breeding.A knowledge of gene homology facilitates the transfer between crops of genes or genomic regions associated with desirable traits by genetic engineering,gene editing,or wide crossing.展开更多
Mountains are rich in biodiversity,and butterflies are species-rich and have a good ecological and evolutionary research foundation.This review addresses the potential and progress of studying mountain biodiversity us...Mountains are rich in biodiversity,and butterflies are species-rich and have a good ecological and evolutionary research foundation.This review addresses the potential and progress of studying mountain biodiversity using butterflies as a model.We discuss the uniqueness of mountain ecosystems,factors influencing the distribution of mountain butterflies,representative genetic and evolutionary models in butterfly research,and evolutionary studies of mountain biodiversity involving butterfly genetics and genomics.Finally,we demonstrate the necessity of studying mountain butterflies and propose future perspectives.This review provides insights for studying the biodiversity of mountain butterflies as well as a summary of research methods for reference.展开更多
Global climate change has increased concerns regarding biodiversity loss.However,many key conservation issues still required further research,including demographic history,deleterious mutation load,adaptive evolution,...Global climate change has increased concerns regarding biodiversity loss.However,many key conservation issues still required further research,including demographic history,deleterious mutation load,adaptive evolution,and putative introgression.Here we generated the first chromosome-level genome of the endangered Chinese hazelnut,Corylus chinensis,and compared the genomic signatures with its sympatric widespread C.kwechowensis-C yunnanensis complex.We found large genome rearrangements across all Corylus species and identified species-specific expanded gene families that may be involved in adaptation.Population genomics revealed that both C.chinensis and the C.kwechowensis-C.yunnanensis complex had diverged into two genetic lineages,forming a consistent pattern of southwestern-northern differentiation.Population size of the narrow southwestern lineages of both species have decreased continuously since the late Miocene,whereas the widespread northern lineages have remained stable(C.chinensis) or have even recovered from population bottlenecks(C.kwechowensis-C.yunnanensis complex) during the Quaternary.Compared with C.kwechowensis-C. yunnanensis complex,C.chinensis showed significantly lower genomic diversity and higher inbreeding level.However,C.chinensis carried significantly fewer deleterious mutations than C.kwechowensis-C. yunnanensis complex,as more effective purging selection reduced the accumulation of homozygous variants.We also detected signals of positive selection and adaptive introgression in different lineages,which facilitated the accumulation of favorable variants and formation of local adaptation.Hence,both types of selection and exogenous introgression could have mitigated inbreeding and facilitated survival and persistence of C.chinensis.Overall,our study provides critical insights into lineage differentiation,local adaptation,and the potential for future recovery of endangered trees.展开更多
In this editorial I comment on the article“Network pharmacological and molecular docking study of the effect of Liu-Wei-Bu-Qi capsule on lung cancer”published in the recent issue of the World Journal of Clinical Cas...In this editorial I comment on the article“Network pharmacological and molecular docking study of the effect of Liu-Wei-Bu-Qi capsule on lung cancer”published in the recent issue of the World Journal of Clinical Cases 2023 November 6;11(31):7593-7609.Almost all living forms are able to manufacture particular chemicals-metabolites that enable them to differentiate themselves from one another and to overcome the unique obstacles they encounter in their natural habitats.Numerous methods for chemical warfare,communication,nutrition acquisition,and stress prevention are made possible by these specialized metabolites.Metabolomics is a popular technique for collecting direct mea-surements of metabolic activity from many biological systems.However,con-fusing metabolite identification is a typical issue,and biochemical interpretation is frequently constrained by imprecise and erroneous genome-based estimates of enzyme activity.Metabolite annotation and gene integration uses a biochemical reaction network to obtain a metabolite-gene association so called metabologe-nomics.This network uses an approach that emphasizes metabolite-gene consensus via biochemical processes.Combining metabolomics and genomics data is beneficial.Furthermore,computer networking proposes that using meta-bolomics data may improve annotations in sequenced species and provide testable hypotheses for specific biochemical processes.CONCLUSION The genome and metabolites of biological organisms are not fully characterized with current technologies.However,increasing high-throughput metabolomics and genomics data provide promising generation of paired data sets to understand the molecular mechanism of biochemical processes as well as determining targets for pharmaceutical drug design.Contemporary network infrastructures to integrate omics analysis can provide molecular mechanism of biochemical pathways.Furthermore,clinical data may be integrated to gene expression–metabolite expression by system genetics approach.Calculating pair-wise correlations and weighted correlation network analysis provide the basis of this integration[11-13].The occurrence of strong correlations between classified metabolites and co-expression transcripts implies either various roles of metabolites or linkages between metabolic pathways and the immune system.展开更多
This review comprehensively explores the core application of artificial intelligence (AI) in the fields of genomics and bioinformatics, and deeply analyzes how it leads the innovative progress of science. In the cutti...This review comprehensively explores the core application of artificial intelligence (AI) in the fields of genomics and bioinformatics, and deeply analyzes how it leads the innovative progress of science. In the cutting-edge fields of genomics and bioinformatics, the application of AI is propelling a deeper understanding of complex genetic mechanisms and the development of innovative therapeutic approaches. The precision of AI in genomic sequence analysis, coupled with breakthroughs in precise gene editing, such as AI-designed gene editors, significantly enhances our comprehension of gene functions and disease associations . Moreover, AI’s capabilities in disease prediction, assessing individual disease risks through genomic data analysis, provide robust support for personalized medicine. AI applications extend beyond gene identification, gene expression pattern prediction, and genomic structural variant analysis, encompassing key areas such as epigenetics, multi-omics data integration, genetic disease diagnosis, evolutionary genomics, and non-coding RNA function prediction. Despite challenges including data privacy, algorithm transparency, and bioethical issues, the future of AI is expected to continue revolutionizing genomics and bioinformatics, ushering in a new era of personalized medicine and precision treatments.展开更多
Partial separation of a peripheral population may lead to its divergence and,potentially,speciation due to genetic drift followed by selection and geographic isolation.This process may cause taxonomic uncertainty beca...Partial separation of a peripheral population may lead to its divergence and,potentially,speciation due to genetic drift followed by selection and geographic isolation.This process may cause taxonomic uncertainty because reproductive isolation in allopatry cannot be verifed directly.The two Nearctic allopatric species of magpies(Aves,Corvidae:Pica)serve as a good example of these problems.The Black-billed magpie Pica hudsonia is widely distributed in North America,whereas the Yellow-billed Magpie Pica nuttalli is endemic to a restricted range in California.Their relationships with Palearctic species have been little studied.We obtained complete mitochondrial genomes of both Nearctic magpie species,along with the Eurasian Magpie(Pica pica)and the Oriental Magpie(Pica serica),20 mitogenomes in total.Phylogenetic analysis reveals a basal position of P.serica,and P.pica as a sister clade to the two Nearctic species.P.hudsonia and P.nuttalli form reciprocal monophyletic subclades,showing recent divergence between and within them.Our data show that the Nearctic magpie lineage diverged from the common ancestor with P.pica,with a single migration wave via the Beringia.Within the Nearctic,we hypothesize a peripatric mode of speciation among Pica taxa due to the divergence and separation of the small marginal population in California below the Sierra-Nevada mountains.Diversifying amino acid substitutions in ND4-ND5-ND6 genes along the branch leading to the New World clade may indicate selection for heat-tolerance.Considering the clear phenotypic differences between P.hudsonia and P.nuttalli,our data,showing their reciprocal monophylies and genetic distinctness,is consistent with the two-species taxonomy.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
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
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.
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.
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.
文摘Rice and wheat provide nearly 40%of human calorie and protein requirements.They share a common ancestor and belong to the Poaceae(grass)family.Characterizing their genetic homology is crucial for developing new cultivars with enhanced traits.Several wheat genes and gene families have been characterized based on their rice orthologs.Rice–wheat orthology can identify genetic regions that regulate similar traits in both crops.Rice–wheat comparative genomics can identify candidate wheat genes in a genomic region identified by association or QTL mapping,deduce their putative functions and biochemical pathways,and develop molecular markers for marker-assisted breeding.A knowledge of gene homology facilitates the transfer between crops of genes or genomic regions associated with desirable traits by genetic engineering,gene editing,or wide crossing.
基金the National Natural Science Foundation of China(32170420 and 31871271)the Beijing Natural Science Foundation(JQ19021)the Peking-Tsinghua Center for Life Science,the State Key Laboratory of Protein and Plant Gene Research,the Qidong-SLS Innovation Fund,Benyuan Charity Young Investigator Exploration Fellowship in Life Science to W.Z.,and grants from the China Postdoctoral Science Foundation(2023M730082 and BX20230026)to S.W.
文摘Mountains are rich in biodiversity,and butterflies are species-rich and have a good ecological and evolutionary research foundation.This review addresses the potential and progress of studying mountain biodiversity using butterflies as a model.We discuss the uniqueness of mountain ecosystems,factors influencing the distribution of mountain butterflies,representative genetic and evolutionary models in butterfly research,and evolutionary studies of mountain biodiversity involving butterfly genetics and genomics.Finally,we demonstrate the necessity of studying mountain butterflies and propose future perspectives.This review provides insights for studying the biodiversity of mountain butterflies as well as a summary of research methods for reference.
基金supported by the National Natural Science Foundation of China(Grant No.32101541)the National Key R&D Program of China(Grant No.2022YFD2200400).
文摘Global climate change has increased concerns regarding biodiversity loss.However,many key conservation issues still required further research,including demographic history,deleterious mutation load,adaptive evolution,and putative introgression.Here we generated the first chromosome-level genome of the endangered Chinese hazelnut,Corylus chinensis,and compared the genomic signatures with its sympatric widespread C.kwechowensis-C yunnanensis complex.We found large genome rearrangements across all Corylus species and identified species-specific expanded gene families that may be involved in adaptation.Population genomics revealed that both C.chinensis and the C.kwechowensis-C.yunnanensis complex had diverged into two genetic lineages,forming a consistent pattern of southwestern-northern differentiation.Population size of the narrow southwestern lineages of both species have decreased continuously since the late Miocene,whereas the widespread northern lineages have remained stable(C.chinensis) or have even recovered from population bottlenecks(C.kwechowensis-C.yunnanensis complex) during the Quaternary.Compared with C.kwechowensis-C. yunnanensis complex,C.chinensis showed significantly lower genomic diversity and higher inbreeding level.However,C.chinensis carried significantly fewer deleterious mutations than C.kwechowensis-C. yunnanensis complex,as more effective purging selection reduced the accumulation of homozygous variants.We also detected signals of positive selection and adaptive introgression in different lineages,which facilitated the accumulation of favorable variants and formation of local adaptation.Hence,both types of selection and exogenous introgression could have mitigated inbreeding and facilitated survival and persistence of C.chinensis.Overall,our study provides critical insights into lineage differentiation,local adaptation,and the potential for future recovery of endangered trees.
文摘In this editorial I comment on the article“Network pharmacological and molecular docking study of the effect of Liu-Wei-Bu-Qi capsule on lung cancer”published in the recent issue of the World Journal of Clinical Cases 2023 November 6;11(31):7593-7609.Almost all living forms are able to manufacture particular chemicals-metabolites that enable them to differentiate themselves from one another and to overcome the unique obstacles they encounter in their natural habitats.Numerous methods for chemical warfare,communication,nutrition acquisition,and stress prevention are made possible by these specialized metabolites.Metabolomics is a popular technique for collecting direct mea-surements of metabolic activity from many biological systems.However,con-fusing metabolite identification is a typical issue,and biochemical interpretation is frequently constrained by imprecise and erroneous genome-based estimates of enzyme activity.Metabolite annotation and gene integration uses a biochemical reaction network to obtain a metabolite-gene association so called metabologe-nomics.This network uses an approach that emphasizes metabolite-gene consensus via biochemical processes.Combining metabolomics and genomics data is beneficial.Furthermore,computer networking proposes that using meta-bolomics data may improve annotations in sequenced species and provide testable hypotheses for specific biochemical processes.CONCLUSION The genome and metabolites of biological organisms are not fully characterized with current technologies.However,increasing high-throughput metabolomics and genomics data provide promising generation of paired data sets to understand the molecular mechanism of biochemical processes as well as determining targets for pharmaceutical drug design.Contemporary network infrastructures to integrate omics analysis can provide molecular mechanism of biochemical pathways.Furthermore,clinical data may be integrated to gene expression–metabolite expression by system genetics approach.Calculating pair-wise correlations and weighted correlation network analysis provide the basis of this integration[11-13].The occurrence of strong correlations between classified metabolites and co-expression transcripts implies either various roles of metabolites or linkages between metabolic pathways and the immune system.
文摘This review comprehensively explores the core application of artificial intelligence (AI) in the fields of genomics and bioinformatics, and deeply analyzes how it leads the innovative progress of science. In the cutting-edge fields of genomics and bioinformatics, the application of AI is propelling a deeper understanding of complex genetic mechanisms and the development of innovative therapeutic approaches. The precision of AI in genomic sequence analysis, coupled with breakthroughs in precise gene editing, such as AI-designed gene editors, significantly enhances our comprehension of gene functions and disease associations . Moreover, AI’s capabilities in disease prediction, assessing individual disease risks through genomic data analysis, provide robust support for personalized medicine. AI applications extend beyond gene identification, gene expression pattern prediction, and genomic structural variant analysis, encompassing key areas such as epigenetics, multi-omics data integration, genetic disease diagnosis, evolutionary genomics, and non-coding RNA function prediction. Despite challenges including data privacy, algorithm transparency, and bioethical issues, the future of AI is expected to continue revolutionizing genomics and bioinformatics, ushering in a new era of personalized medicine and precision treatments.
基金supported by a grant from the Harvard Global Institute for research on biodiversity of ChinaThe research was carried out within the state assignment of Ministry of Science and Higher Education of the Russian Federation(theme No.121031500274-4)。
文摘Partial separation of a peripheral population may lead to its divergence and,potentially,speciation due to genetic drift followed by selection and geographic isolation.This process may cause taxonomic uncertainty because reproductive isolation in allopatry cannot be verifed directly.The two Nearctic allopatric species of magpies(Aves,Corvidae:Pica)serve as a good example of these problems.The Black-billed magpie Pica hudsonia is widely distributed in North America,whereas the Yellow-billed Magpie Pica nuttalli is endemic to a restricted range in California.Their relationships with Palearctic species have been little studied.We obtained complete mitochondrial genomes of both Nearctic magpie species,along with the Eurasian Magpie(Pica pica)and the Oriental Magpie(Pica serica),20 mitogenomes in total.Phylogenetic analysis reveals a basal position of P.serica,and P.pica as a sister clade to the two Nearctic species.P.hudsonia and P.nuttalli form reciprocal monophyletic subclades,showing recent divergence between and within them.Our data show that the Nearctic magpie lineage diverged from the common ancestor with P.pica,with a single migration wave via the Beringia.Within the Nearctic,we hypothesize a peripatric mode of speciation among Pica taxa due to the divergence and separation of the small marginal population in California below the Sierra-Nevada mountains.Diversifying amino acid substitutions in ND4-ND5-ND6 genes along the branch leading to the New World clade may indicate selection for heat-tolerance.Considering the clear phenotypic differences between P.hudsonia and P.nuttalli,our data,showing their reciprocal monophylies and genetic distinctness,is consistent with the two-species taxonomy.
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 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.
