后基因组时代生物信息学的发展趋势

介绍生物信息学产生背景、发展过程以及研究现状,讨论了后基因组时代分子生物学的主要研究领域功能基因组学、蛋白质组学、比较基因组学、药物基因组学之间的关系。在分析基因组时代和后基因组时代生物信息学所研究内容的差异基础上．说明了基于分层递阶结构的系统结构、特征分析方法以及相应的软件系统开发将成为生物信息学发展的基本趋势之一。     

人类基因组计划与后基因组时代

2003年4月14日生命科学诞生了一个新的重要里程碑,人类基因组计划完成,后基因组时代正式来临。着重介绍了人类基因组计划的提出、目标与任务、实施与进展等方面的基本情况,讨论了后基因组时代的时间界定,分析展望了后基因组时代与人类基因组计划密切相关的生物信息学、功能基因组学、蛋白质组学、药物基因组学等几个重要研究领域 。     

“后基因组时代”的生物学

在2001年提前完成《人类基因组计划》目标后,更艰巨的任务是阐明基因组的功能,即细胞的全部基因表达图式和全部蛋白质图式。分子生物学的重点将从“基因组转到蛋白质组”。生物学也将进入一个新时代──后基因组时代。本文讨论了后基因组时代生物学在方法论上的特征、主要研究领域和发展前景。未来生物学将从基因组而不只是个别基因的功能调控及不同生物基因组比较进化的观点重新探讨生物的遗传、发育和进化的关系以及功能活动等问题。     

后基因组时代的质谱技术

人类基因组测序已完成 ,生命科学研究进入了后基因组时代。今后的人类基因组研究发展趋势是解决基因的功能和不同种族间基因的差异等更为复杂的问题。随之而来的繁重任务迫切需要各种先进技术[1,2 ] 。质谱技术因其具有超高分辨率、灵敏度 ,以及高通量、良好的自动化前景等优点而备受关注。1 .质谱技术的现状质谱仪一般由离子源 ,质量分析器和离子检测器 3部分组成。质谱仪操作可简化为 :产生气相离子 ;按离子的质荷比 (ｍａｓｓｔｏｃｈａｒｇｅｒａｔｉｏ ,ｍ/ｚ)将离子在空间或时间上分离 ;测定不同质荷比下离子的数量。如今针对多肽、蛋…     

后基因组时代的生物信息学

随着人类基因组计划的完成,不断积累的巨量的生物学数据和快速发展的信息学技术,给后基因组时代的生物信息学研究带来了新的挑战。该文对后基因组时代的生物信息学研究内容进行了比较全面的描述,分别就其研究对象和研究方向作了区别讨论,分析了生物信息学研究的现状和趋势,比较了国内外的研究发展情况和差距。针对我国在研究中所存在的主要问题,提出了建议并做了展望。     

后基因组时代生命科学研究的几点思考

由美国发起多国科学家参加的、比曼哈顿原子弹计划和阿波罗登月计划更具影响力的人类基因组计划 (ＨｕｍａｎＧｅｎｏｍｅＰｒｏｊｅｃｔ,ＨＧＰ) ,自 1990年正式启动以来 ,由于技术的成熟与基因组测序的规模化 ,以及来自商业竞争方面的压力 ,使原计划 15年完成的工作大大提前 ,于 2 0 0 0年 6月 2 6日美、英、日、德、法、中六国科学家共同宣布人类基因组工作框架图 (ｗｏｒｋｉｎｇｄｒａｆｔ)构建完成 ,今年 2月他们又联合公布经过整理的更为准确的人类基因组图谱 ,这一伟大成就标志着生命科学研究进入了一个崭新的时代———后基因组时…     

后基因组时代药理学研究趋向

药理基因组学（药物基因组学,pharmacogenomics)将成为后基因组时代药理学研究的新领域,与此相应,高通量筛选（high-throughput screening,HTS)、in silico研究以及多种功能可视化技术已开始成为药理学研究的新方法,本文同时介绍上述新思路与新方法应用于药效学,药动学研究的某些进展。     

后基因组时代的病毒学

人类基因组计划的提出及实施完成了人类基因组全序列的测定,与此同时,其他多个物种的基因组序列也相继被获知[1],加速了学界从分子水平破译人类所有DNA序列和识别其中所有基因的进程.     

后基因组时代的代谢工程:机遇与挑战

代谢工程的研究目的是要改进细胞性质,增加工业生物产品的收率及生产能力。现在越来越多的生物基因测序的完成及功能基因组学研究的进展正把代谢工程研究引入一个新时代。代谢工程正面临一个极好的机遇,同时也遇到一些严重的挑战。这是由于细胞内存在着复杂的,非线性的,在酶、调控子及代谢物之间未知的相互作用．从而使代谢途径的优化极为困难。为了促进后基因组时代代谢工程的研究,综述了过去13年基于功能基因组学的代谢工程原理与方法的国内外研究进展,功能基因组学与代谢工程之间的关系以及后基因组时代代谢工程面临的机遇与挑战。     

后基因组时代基因功能分析的策略

人类基因组计划的完成标志着生命科学已经开始迈进后基因组时代,大规模基因的功能研究正成为生命科学研究的热点。基因功能的分析方法日趋成熟和多样化,综述了基因敲除、反义技术、“dominantnegative”策略(“显性失活”策略)、基因诱导超表达、RNA干涉以及生物信息学等基因功能分析策略的特异性及其优缺点。     

Bioinformatics in the post-genome era

Recent years saw a dramatic increase in genomic and proteomic data in public archives. Now with the complete genome sequences of human and other species in hand, detailed analyses of the genome sequences will undoubtedly improve our understanding of biological systems and at the same time require sophisticated bioinformatic tools. Here we review what computational challenges are ahead and what are the new exciting developments in this exciting field.     

Functional genomics in the post-genome era

As the biomedical research community enters the post-genome era, studying gene expression patterns and phenotypes in model organisms will be an important part of analyzing the role of genes in human health and disease. New technologies involving DNA chips will improve the ability to evaluate the differential expression of a large number of genes simultaneously. Also, new approaches for generating mutations in mice will significantly decrease the cost and increase the rate of generating mutant lines that model human disease.     

Arabidopsis map-based cloning in the post-genome era

Map-based cloning is an iterative approach that identifies the underlying genetic cause of a mutant phenotype. The major strength of this approach is the ability to tap into a nearly unlimited resource of natural and induced genetic variation without prior assumptions or knowledge of specific genes. One begins with an interesting mutant and allows plant biology to reveal what gene or genes are involved. Three major advances in the past 2 years have made map-based cloning in Arabidopsis fairly routine: sequencing of the Arabidopsis genome, the availability of more than 50,000 markers in the Cereon Arabidopsis Polymorphism Collection, and improvements in the methods used for detecting DNA polymorphisms. Here, we describe the Cereon Collection and show how it can be used in a generic approach to mutation mapping in Arabidopsis. We present the map-based cloning of the VTC2 gene as a specific example of this approach.     

Yeast genome evolution in the post-genome era.

The Saccharomyces cerevisiae genome sequence, augmented by new data on gene expression and function, continues to yield new findings about eukaryote genome evolution. Analysis of the duplicate gene pairs formed by whole-genome duplication indicates that selection for increased levels of gene expression was a significant factor determining which genes were retained as duplicates and which were returned to a single-copy state, possibly in addition to selection for novel gene functions. Proteome comparisons between worm and yeast show that genes for core metabolic processes are shared among eukaryotes and unchanging in function, while comparisons between different yeast species identify 'orphan' genes as the most rapidly evolving fraction of the proteome. Natural hybridisation among yeast species is frequent, but its long-term evolutionary significance is unknown.     

Bioinformatics for the genomic sciences and towards systems biology. Japanese activities in the post-genome era

The knowledge gleaned from genome sequencing and post-genome analyses is having a very significant impact on a whole range of life sciences and their applications. 'Genome-wide analysis' is a good keyword to represent this tendency. Thanks to innovations in high-throughput measurement technologies and information technologies, genome-wide analysis is becoming available in a broad range of research fields from DNA sequences, gene and protein expressions, protein structures and interactions, to pathways or networks analysis. In fact, the number of research targets has increased by more than two orders in recent years and we should change drastically the attitude to research activities. The scope and speed of research activities are expanding and the field of bioinformatics is playing an important role. In parallel with the data-driven research approach that focuses on speedy handling and analyzing of the huge amount of data, a new approach is gradually gaining power. This is a 'model-driven research' approach, that incorporates biological modeling in its research framework. Computational simulations of biological processes play a pivotal role. By modeling and simulating, this approach aims at predicting and even designing the dynamic behaviors of complex biological systems, which is expected to make rapid progress in life science researches and lead to meaningful applications to various fields such as health care, food supply and improvement of environment. Genomic sciences are now advancing as great frontiers of research and applications in the 21st century.This article starts with surveying the general progress of bioinformatics (Section 1), and describes Japanese activities in bioinformatics (Section 2). In Section 3, I will introduce recent developments in Systems Biology which I think will become more important in the future.