COMPUTER SUPPORT FOR MOLECULAR BIOLOGY SEQUENCING AND GENETIC MAPPING

分子生物学越来越依赖于蛋白质的计算机技术 和DNA序列分析，大型数据库的管理，以及全球 通信，从而产生了新的术语，分子生物学信息学。 在大规模的项目中，例如对整个基因或一个大的 病毒，电脑已成为不可或缺的一部分。中超继续其 与克雷格·文特尔博士的分子和细胞实验室合作 NINDS的神经生物学(LMCN)文特尔博士的实验室致力于 对X染色体和4号染色体的区域进行测序，今年 开始了识别和表征大脑中表达的基因的项目 (与DOE的cDNA项目)，并对整个天花病毒进行测序(与 CDC)。CSL通过集成各种计算机来为这些努力做出贡献 工作站、软件和网络资源，并已开始开发 处理和归档LMCN的超大数据量的方法 将会产生。今年的具体活动包括测试Unix 基因计算机组(GCG)序列分析软件的版本 对其他一些序列分析软件包进行评估； 安装新的文件服务器；安装Sybase数据库管理 系统。在接下来的一年里，中超将专注于处理方法 并将超大容量的序列数据存档，有助于使 通过此项目开发的技术可供其他人使用 美国国立卫生研究院的调查人员。例如，我们建议获取一个“快速数据” Finder“系统，并将其作为网络共享资源提供给 高速序列搜索。

Molecular biology relies increasingly on computer technology for protein and DNA sequence analysis, management of large databases, and worldwide communications, giving rise to the new term, molecular biology informatics. In large scale projects, such as sequencing an entire gene or a large virus, computers have become indispensable. CSL continued its collaboration with Dr. Craig Venter's Laboratory of Molecular and Cellular Neurobiology (LMCN) in NINDS. Dr. Venter's laboratory engages in sequencing regions of the X chromosome and Chromosome 4, and this year began projects to identify and characterize genes expressed in the brain (the cDNA Project with DOE) and to sequence the entire smallpox virus (with CDC). CSL contributes to these efforts by integrating the various computer workstations, software, and network resources and has begun to develop methods to process and archive the extremely large volume of data that LMCN will generate. Specific activities this year include beta testing the UNIX version of the Genetic Computer Group's (GCG's) sequence analysis software suite; evaluating a number of other sequence analysis software packages; installing a new fileserver; installing the Sybase database management system. During the coming year, CSL will concentrate on methods to process and archive extremely large volumes of sequence data, and help to make the technologies developed through this project available to other investigators at NIH. For example, we propose to obtain a "fast data finder" system, and make it available as a network sharable resource for high speed sequence searching.