COMPUTER SUPPORT FOR MOLECULAR BIOLOGY SEQUENCING AND GENETIC MAPPING

分子生物学越来越依赖于计算机技术来研究蛋白质 和DNA序列分析，大型数据库的管理，以及全球范围内的 分子生物学信息学（molecular biology informatics）。 在大规模的项目中，例如对整个基因或大的 病毒，电脑已成为不可或缺的。 CSL继续其 与克雷格文特尔博士的分子和细胞实验室合作 神经生物学（LMCN）在NINDS。 文特尔博士的实验室 X染色体和4号染色体的测序区域， 开始了识别和描述大脑中表达的基因的项目 (the cDNA项目）和对整个天花病毒进行测序（ CDC）。 CSL通过整合各种计算机， 工作站、软件和网络资源，并已开始开发 处理和归档LMCN所需的大量数据的方法 将产生。 今年的具体活动包括UNIX的beta测试 Genetic Computer Group（GCG）的序列分析软件版本 套件;评估其他一些序列分析软件包; 安装新的文件服务器;安装Windows数据库管理 系统 在未来的一年里，CSL将专注于处理 并存档大量的序列数据， 通过该项目开发的技术可供其他 NIH的研究人员。 例如，我们建议获得“快速数据 finder”系统，并使其可作为网络共享资源， 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.