Gene sequencing is a great way to interpret life, and high-throughput sequencing technology is a revolutionary technological innovation in gene sequencing researches. This technology is characterized by low cost and h...Gene sequencing is a great way to interpret life, and high-throughput sequencing technology is a revolutionary technological innovation in gene sequencing researches. This technology is characterized by low cost and high-throughput data. Currently, high-throughput sequencing technology has been widely applied in multi-level researches on genomics, transcriptomics and epigenomics. And it has fundamentally changed the way we approach problems in basic and translational researches and created many new possibilities. This paper presented a general description of high-throughput sequencing technology and a comprehensive review of its application with plain, concisely and precisely. In order to help researchers finish their work faster and better, promote science amateurs and understand it easier and better.展开更多
面向作物表型组大数据获取解析、作物种质资源表型鉴定等亟需高效率、智能化和低成本技术、装备及系统的问题,在系统梳理分析国内外农作物高通量表型平台相关技术产品研究现状的基础上,通过组织多学科的协同技术攻关,突破了作物表型组...面向作物表型组大数据获取解析、作物种质资源表型鉴定等亟需高效率、智能化和低成本技术、装备及系统的问题,在系统梳理分析国内外农作物高通量表型平台相关技术产品研究现状的基础上,通过组织多学科的协同技术攻关,突破了作物表型组大数据高通量获取和智能化解析中的关键技术难题,设计了具有自主知识产权的轻小敏捷型多传感器阵列、通用化成像单元和适用于多生境的固定式、移动式高通量表型平台装备,以及配套算法和软件平台,构建了农作物表型组大数据工厂成套技术装备体系。该体系由大田和设施作物高通量自主作业表型平台、室内器官和显微表型平台、大田和设施环境自动化种植管控设备、作物模型系统、数字孪生智慧管控平台和大数据计算服务中心等构成,可实现多生境、自动化、高通量、高效率、高精度的多源作物表型-环境数据协同采集,涵盖农作物群体、个体、器官和显微多重尺度,能够重建农林作物的三维形态结构并精准解析株型、产品、品质、抗性等表型组指标,是发展数字育种和智慧栽培的新一代信息化基础设施。农作物表型组大数据工厂技术装备体系创新了作物表型组大数据的产生、处理和服务模式,可为作物表型组理论技术的发展、基于AI for Science的平台化科研和工厂化的作物种质资源表型鉴定等提供体系化的技术装备支撑。展开更多
BACKGROUND The broader use of high-throughput technologies has led to improved molecular characterization of hepatocellular carcinoma(HCC).AIM To comprehensively analyze and characterize all publicly available genomic...BACKGROUND The broader use of high-throughput technologies has led to improved molecular characterization of hepatocellular carcinoma(HCC).AIM To comprehensively analyze and characterize all publicly available genomic,gene expression,methylation,miRNA and proteomic data in HCC,covering 85 studies and 3355 patient sample profiles,to identify the key dysregulated genes and pathways they affect.METHODS We collected and curated all well-annotated and publicly available highthroughput datasets from PubMed and Gene Expression Omnibus derived from human HCC tissue.Comprehensive pathway enrichment analysis was performed using pathDIP for each data type(genomic,gene expression,methylation,miRNA and proteomic),and the overlap of pathways was assessed to elucidate pathway dependencies in HCC.RESULTS We identified a total of 8733 abstracts retrieved by the search on PubMed on HCC for the different layers of data on human HCC samples,published until December 2016.The common key dysregulated pathways in HCC tissue across different layers of data included epidermal growth factor(EGFR)andβ1-integrin pathways.Genes along these pathways were significantly and consistently dysregulated across the different types of high-throughput data and had prognostic value with respect to overall survival.Using CTD database,estradiol would best modulate and revert these genes appropriately.CONCLUSION By analyzing and integrating all available high-throughput genomic,transcriptomic,miRNA,methylation and proteomic data from human HCC tissue,we identified EGFR,β1-integrin and axon guidance as pathway dependencies in HCC.These are master regulators of key pathways in HCC,such as the mTOR,Ras/Raf/MAPK and p53 pathways.The genes implicated in these pathways had prognostic value in HCC,with Netrin and Slit3 being novel proteins of prognostic importance to HCC.Based on this integrative analysis,EGFR,andβ1-integrin are master regulators that could serve as potential therapeutic targets in HCC.展开更多
RNA-sequencing(RNA-seq),based on next-generation sequencing technologies,has rapidly become a standard and popular technology for transcriptome analysis.However,serious challenges still exist in analyzing and interpre...RNA-sequencing(RNA-seq),based on next-generation sequencing technologies,has rapidly become a standard and popular technology for transcriptome analysis.However,serious challenges still exist in analyzing and interpreting the RNA-seq data.With the development of high-throughput sequencing technology,the sequencing depth of RNA-seq data increases explosively.The intricate biological process of transcriptome is more complicated and diversified beyond our imagination.Moreover,most of the remaining organisms still have no available reference genome or have only incomplete genome annotations.Therefore,a large number of bioinformatics methods for various transcriptomics studies are proposed to effectively settle these challenges.This review comprehensively summarizes the various studies in RNA-seq data analysis and their corresponding analysis methods,including genome annotation,quality control and pre-processing of reads,read alignment,transcriptome assembly,gene and isoform expression quantification,differential expression analysis,data visualization and other analyses.展开更多
荧光漂白恢复(fluorescence recovery after photobleaching,FRAP)是一种广泛使用的显微镜技术,用于可视化荧光标记分子的动力学.首先梳理了FRAP试验数据采集过程中的关键参数,并针对分子运动速率较快的样本开发了特异性的试验方法,从...荧光漂白恢复(fluorescence recovery after photobleaching,FRAP)是一种广泛使用的显微镜技术,用于可视化荧光标记分子的动力学.首先梳理了FRAP试验数据采集过程中的关键参数,并针对分子运动速率较快的样本开发了特异性的试验方法,从而得到更准确的试验结果.科学试验依赖于对大量样本的统计分析,基于NISElements软件建立了高通量FRAP试验方法,显著提升了数据采集效率并降低了人力成本.在数据分析方面构建了新型的图像高通量处理方法,实现了漂白区域自动校准和荧光强度精准分析,突破了传统手动分析瓶颈,大幅降低了人力与时间成本,为科研工作的高效推进与深度拓展提供了有力支撑.展开更多
Predicting the material stability is essential for accelerating the discovery of advanced materials in renewable energy, aerospace, and catalysis. Traditional approaches, such as Density Functional Theory (DFT), are a...Predicting the material stability is essential for accelerating the discovery of advanced materials in renewable energy, aerospace, and catalysis. Traditional approaches, such as Density Functional Theory (DFT), are accurate but computationally expensive and unsuitable for high-throughput screening. This study introduces a machine learning (ML) framework trained on high-dimensional data from the Open Quantum Materials Database (OQMD) to predict formation energy, a key stability metric. Among the evaluated models, deep learning outperformed Gradient Boosting Machines and Random Forest, achieving up to 0.88 R2 prediction accuracy. Feature importance analysis identified thermodynamic, electronic, and structural properties as the primary drivers of stability, offering interpretable insights into material behavior. Compared to DFT, the proposed ML framework significantly reduces computational costs, enabling the rapid screening of thousands of compounds. These results highlight ML’s transformative potential in materials discovery, with direct applications in energy storage, semiconductors, and catalysis.展开更多
基金Supported by the National Natural Science Foundations of China(3127218631301791)
文摘Gene sequencing is a great way to interpret life, and high-throughput sequencing technology is a revolutionary technological innovation in gene sequencing researches. This technology is characterized by low cost and high-throughput data. Currently, high-throughput sequencing technology has been widely applied in multi-level researches on genomics, transcriptomics and epigenomics. And it has fundamentally changed the way we approach problems in basic and translational researches and created many new possibilities. This paper presented a general description of high-throughput sequencing technology and a comprehensive review of its application with plain, concisely and precisely. In order to help researchers finish their work faster and better, promote science amateurs and understand it easier and better.
文摘面向作物表型组大数据获取解析、作物种质资源表型鉴定等亟需高效率、智能化和低成本技术、装备及系统的问题,在系统梳理分析国内外农作物高通量表型平台相关技术产品研究现状的基础上,通过组织多学科的协同技术攻关,突破了作物表型组大数据高通量获取和智能化解析中的关键技术难题,设计了具有自主知识产权的轻小敏捷型多传感器阵列、通用化成像单元和适用于多生境的固定式、移动式高通量表型平台装备,以及配套算法和软件平台,构建了农作物表型组大数据工厂成套技术装备体系。该体系由大田和设施作物高通量自主作业表型平台、室内器官和显微表型平台、大田和设施环境自动化种植管控设备、作物模型系统、数字孪生智慧管控平台和大数据计算服务中心等构成,可实现多生境、自动化、高通量、高效率、高精度的多源作物表型-环境数据协同采集,涵盖农作物群体、个体、器官和显微多重尺度,能够重建农林作物的三维形态结构并精准解析株型、产品、品质、抗性等表型组指标,是发展数字育种和智慧栽培的新一代信息化基础设施。农作物表型组大数据工厂技术装备体系创新了作物表型组大数据的产生、处理和服务模式,可为作物表型组理论技术的发展、基于AI for Science的平台化科研和工厂化的作物种质资源表型鉴定等提供体系化的技术装备支撑。
文摘BACKGROUND The broader use of high-throughput technologies has led to improved molecular characterization of hepatocellular carcinoma(HCC).AIM To comprehensively analyze and characterize all publicly available genomic,gene expression,methylation,miRNA and proteomic data in HCC,covering 85 studies and 3355 patient sample profiles,to identify the key dysregulated genes and pathways they affect.METHODS We collected and curated all well-annotated and publicly available highthroughput datasets from PubMed and Gene Expression Omnibus derived from human HCC tissue.Comprehensive pathway enrichment analysis was performed using pathDIP for each data type(genomic,gene expression,methylation,miRNA and proteomic),and the overlap of pathways was assessed to elucidate pathway dependencies in HCC.RESULTS We identified a total of 8733 abstracts retrieved by the search on PubMed on HCC for the different layers of data on human HCC samples,published until December 2016.The common key dysregulated pathways in HCC tissue across different layers of data included epidermal growth factor(EGFR)andβ1-integrin pathways.Genes along these pathways were significantly and consistently dysregulated across the different types of high-throughput data and had prognostic value with respect to overall survival.Using CTD database,estradiol would best modulate and revert these genes appropriately.CONCLUSION By analyzing and integrating all available high-throughput genomic,transcriptomic,miRNA,methylation and proteomic data from human HCC tissue,we identified EGFR,β1-integrin and axon guidance as pathway dependencies in HCC.These are master regulators of key pathways in HCC,such as the mTOR,Ras/Raf/MAPK and p53 pathways.The genes implicated in these pathways had prognostic value in HCC,with Netrin and Slit3 being novel proteins of prognostic importance to HCC.Based on this integrative analysis,EGFR,andβ1-integrin are master regulators that could serve as potential therapeutic targets in HCC.
文摘RNA-sequencing(RNA-seq),based on next-generation sequencing technologies,has rapidly become a standard and popular technology for transcriptome analysis.However,serious challenges still exist in analyzing and interpreting the RNA-seq data.With the development of high-throughput sequencing technology,the sequencing depth of RNA-seq data increases explosively.The intricate biological process of transcriptome is more complicated and diversified beyond our imagination.Moreover,most of the remaining organisms still have no available reference genome or have only incomplete genome annotations.Therefore,a large number of bioinformatics methods for various transcriptomics studies are proposed to effectively settle these challenges.This review comprehensively summarizes the various studies in RNA-seq data analysis and their corresponding analysis methods,including genome annotation,quality control and pre-processing of reads,read alignment,transcriptome assembly,gene and isoform expression quantification,differential expression analysis,data visualization and other analyses.
文摘荧光漂白恢复(fluorescence recovery after photobleaching,FRAP)是一种广泛使用的显微镜技术,用于可视化荧光标记分子的动力学.首先梳理了FRAP试验数据采集过程中的关键参数,并针对分子运动速率较快的样本开发了特异性的试验方法,从而得到更准确的试验结果.科学试验依赖于对大量样本的统计分析,基于NISElements软件建立了高通量FRAP试验方法,显著提升了数据采集效率并降低了人力成本.在数据分析方面构建了新型的图像高通量处理方法,实现了漂白区域自动校准和荧光强度精准分析,突破了传统手动分析瓶颈,大幅降低了人力与时间成本,为科研工作的高效推进与深度拓展提供了有力支撑.
文摘Predicting the material stability is essential for accelerating the discovery of advanced materials in renewable energy, aerospace, and catalysis. Traditional approaches, such as Density Functional Theory (DFT), are accurate but computationally expensive and unsuitable for high-throughput screening. This study introduces a machine learning (ML) framework trained on high-dimensional data from the Open Quantum Materials Database (OQMD) to predict formation energy, a key stability metric. Among the evaluated models, deep learning outperformed Gradient Boosting Machines and Random Forest, achieving up to 0.88 R2 prediction accuracy. Feature importance analysis identified thermodynamic, electronic, and structural properties as the primary drivers of stability, offering interpretable insights into material behavior. Compared to DFT, the proposed ML framework significantly reduces computational costs, enabling the rapid screening of thousands of compounds. These results highlight ML’s transformative potential in materials discovery, with direct applications in energy storage, semiconductors, and catalysis.
