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排序方式: 共有1350条查询结果,搜索用时 31 毫秒
1.
植物基因组表达序列标签(EST)计划研究进展   总被引:59,自引:0,他引:59       下载免费PDF全文
植物表达序列标签(EST)计划是随机挑选cDNA克隆,并对其3′或5′端进行大规模一次性测序,将得到的150~500 bp长度的DNA片段与数据库中的序列进行比较,获得对基因组结构、组织、表达等认识的基因组研究策略.就近年来国际植物EST计划的实施情况、植物EST计划的研究范围、生物信息学在EST研究中的应用、EST数据库及查询、植物EST研究中遇到的问题等方面内容进行了综述.  相似文献
2.
Plant metabolomics: large-scale phytochemistry in the functional genomics era   总被引:52,自引:0,他引:52  
Metabolomics or the large-scale phytochemical analysis of plants is reviewed in relation to functional genomics and systems biology. A historical account of the introduction and evolution of metabolite profiling into today's modern comprehensive metabolomics approach is provided. Many of the technologies used in metabolomics, including optical spectroscopy, nuclear magnetic resonance, and mass spectrometry are surveyed. The critical role of bioinformatics and various methods of data visualization are summarized and the future role of metabolomics in plant science assessed.  相似文献
3.
功能基因组学的研究内容与方法   总被引:37,自引:0,他引:37       下载免费PDF全文
基因组学的研究已从结构基因组学转向功能基因组学.综述了功能基因组学研究的内容和方法,主要包括应用微点阵、基因表达系列分析(SAGE)、蛋白质组、生物信息学等方法来研究基因组表达概况、基因组多样性、模式生物体等.  相似文献
4.
基于生物信息学的SNP候选位点搜寻方法   总被引:22,自引:3,他引:19  
陈炜  张戈  张思仲 《遗传》2001,23(2):153-156
单核苷酸多态性(Single Nucleotide Polymorphism,SNP)是人类基因组中最常见的遗传多态,在遗传学研究的很多方面具有重要的作用。它的搜寻正受到广泛关注。近年来,国际上出现了一种基于生物信息学的发掘SNP新方法,本对方法的两种策略及其各自所存在的问题作一介绍。  相似文献
5.
GSDS: 基因结构显示系统   总被引:22,自引:1,他引:21       下载免费PDF全文
郭安源  朱其慧  陈新  罗静初 《遗传》2007,29(8):1023-1026
构建了一个用于绘制基因结构示意图的网站系统(http://gsds.cbi.pku.edu.cn/)。用户可提交核酸序列、NCBI核酸序列号或基因外显子位置信息, 得到基因结构示意图; 并可指定在基因结构图上标注某些特定区域。系统允许用户同时输入多个基因, 并指定输出次序和标注区域。结果可用位图和矢量图两种图形格式显示。点击位图格式结果, 可以查看相应序列。系统提供中英文两种用户界面。  相似文献
6.
生物芯片、生物传感器和生物信息学   总被引:19,自引:1,他引:18  
近年来,在生物技术和医学研究领域涌现出了许多新技术平台,其中就包括生物芯片技术和生物传感器技术。生物芯片和生物传感器的构建都必须以生物信息学为基础,而两种技术平台应用所得出的数据和结果又反过来大大丰富和充实了生物信息学本身。本分析概述了生物芯片和生物传感器两种技术平台以及生物信息学,对三之间的相互关系进行了讨论。  相似文献
7.
人类新基因C17orf32的电子克隆和编码区序列RT-PCR验证   总被引:19,自引:3,他引:16       下载免费PDF全文
利用生物信息学与实验验证的技术路线,成功地克隆了人类新基因C17orf32的cDNA(GenBank登记号:AY074907和TPA: BK000260),发现C17orf32的完整开放阅读框架(ORF,31~657 bp)cDNA(627 bp)与人类假定基因LOC124919 ORF(25~807 bp)的25~651位只有一个碱基不同.经RT-PCR验证并cDNA测序、人类表达序列标签(EST)数据库的BLAST检索和基因组成规律分析三方面的结果,均支持C17orf32的序列,而不支持LOC124919的编码序列.C17orf32基因组序列全长4.610 kb,含有6个外显子和5个内含子,cDNA序列全长1 679 bp, ORF横跨全部6个外显子.该基因ORF翻译起始处符合Kozak规则,ORF起始码上游同一相位有终止码,ORF后有2个加尾信号和PolyA尾.C17orf32基因的成功克隆表明,NCBI GENOME Annotation Project在2001年12月预测的人类假定蛋白XP-058865编码基因LOC124919的模式参考序列XM-058865中存在偏差,即在C17orf32基因cDNA的406与407位碱基之间错误插入一个碱基G, 从而导致在插入位点后,ORF编码125位氨基酸以后蛋白质序列的改变,出现260个氨基酸的多肽.因此,应慎重看待计算机注释的人类基因组编码序列.建立的技术路线有助于发现更多新的人类功能基因.  相似文献
8.
Proteomics of calcium-signaling components in plants   总被引:17,自引:0,他引:17  
Reddy VS  Reddy AS 《Phytochemistry》2004,65(12):1745-1776
Calcium functions as a versatile messenger in mediating responses to hormones, biotic/abiotic stress signals and a variety of developmental cues in plants. The Ca(2+)-signaling circuit consists of three major "nodes"--generation of a Ca(2+)-signature in response to a signal, recognition of the signature by Ca2+ sensors and transduction of the signature message to targets that participate in producing signal-specific responses. Molecular genetic and protein-protein interaction approaches together with bioinformatic analysis of the Arabidopsis genome have resulted in identification of a large number of proteins at each "node"--approximately 80 at Ca2+ signature, approximately 400 sensors and approximately 200 targets--that form a myriad of Ca2+ signaling networks in a "mix and match" fashion. In parallel, biochemical, cell biological, genetic and transgenic approaches have unraveled functions and regulatory mechanisms of a few of these components. The emerging paradigm from these studies is that plants have many unique Ca2+ signaling proteins. The presence of a large number of proteins, including several families, at each "node" and potential interaction of several targets by a sensor or vice versa are likely to generate highly complex networks that regulate Ca(2+)-mediated processes. Therefore, there is a great demand for high-throughput technologies for identification of signaling networks in the "Ca(2+)-signaling-grid" and their roles in cellular processes. Here we discuss the current status of Ca2+ signaling components, their known functions and potential of emerging high-throughput genomic and proteomic technologies in unraveling complex Ca2+ circuitry.  相似文献
9.
英特网上生物信息资源的利用   总被引:17,自引:0,他引:17       下载免费PDF全文
充分利用英特网上完善的生物信息资源是十分有益且必要的.介绍了利用网上生物资源的几种主要方式和一些重要的站点和生物信息搜索引擎.  相似文献
10.
With recent advances in mass spectrometry techniques, it is now possible to investigate proteins over a wide range of molecular weights in small biological specimens. This advance has generated data-analytic challenges in proteomics, similar to those created by microarray technologies in genetics, namely, discovery of 'signature' protein profiles specific to each pathologic state (e.g. normal vs. cancer) or differential profiles between experimental conditions (e.g. treated by a drug of interest vs. untreated) from high-dimensional data. We propose a data-analytic strategy for discovering protein biomarkers based on such high-dimensional mass spectrometry data. A real biomarker-discovery project on prostate cancer is taken as a concrete example throughout the paper: the project aims to identify proteins in serum that distinguish cancer, benign hyperplasia, and normal states of prostate using the Surface Enhanced Laser Desorption/Ionization (SELDI) technology, a recently developed mass spectrometry technique. Our data-analytic strategy takes properties of the SELDI mass spectrometer into account: the SELDI output of a specimen contains about 48,000 (x, y) points where x is the protein mass divided by the number of charges introduced by ionization and y is the protein intensity of the corresponding mass per charge value, x, in that specimen. Given high coefficients of variation and other characteristics of protein intensity measures (y values), we reduce the measures of protein intensities to a set of binary variables that indicate peaks in the y-axis direction in the nearest neighborhoods of each mass per charge point in the x-axis direction. We then account for a shifting (measurement error) problem of the x-axis in SELDI output. After this pre-analysis processing of data, we combine the binary predictors to generate classification rules for cancer, benign hyperplasia, and normal states of prostate. Our approach is to apply the boosting algorithm to select binary predictors and construct a summary classifier. We empirically evaluate sensitivity and specificity of the resulting summary classifiers with a test dataset that is independent from the training dataset used to construct the summary classifiers. The proposed method performed nearly perfectly in distinguishing cancer and benign hyperplasia from normal. In the classification of cancer vs. benign hyperplasia, however, an appreciable proportion of the benign specimens were classified incorrectly as cancer. We discuss practical issues associated with our proposed approach to the analysis of SELDI output and its application in cancer biomarker discovery.  相似文献
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