UCSC Center for Genomic Science

我们计划为人类基因组计划提供生物信息学支持，并与怀特黑德生物医学研究所/麻省理工学院基因组研究中心合作，对人类和其他脊椎动物基因组进行比较。我们将继续每月提供我们自2000年5月以来提供的现有人类基因组序列汇编。沿着每一次组装，我们将提供一个集成的数据库，其中包含EST、mRNA、SNP、基因预测、标记、等距离、重叠群、缺口、克隆和与其他脊椎动物的同源性的链接，这些链接映射到组装基因组上的特定位置，并通过交互式浏览器在网络上并排查看。该网络浏览器将促进世界各地的医学和科学研究人员使用NCBI和EBI存储的初步工作草案和完成的人类基因组数据。除了我们的支持作用，我们建议开发新的方法，使用比较基因组学，计算算法和微阵列技术来揭示人类基因的结构和功能。与其他脊椎动物基因的序列同源性，结合EST和mRNA数据，将用于确定人类基因的基因结构，选择性剪接和调控基序。定制的微阵列将建立特定的寡核苷酸设计，以确认剪接和调控的最重要的医学基因。将建立隐马尔可夫模型来模拟蛋白质产物的结构域结构，并建议通过同源性进行功能分类。表达数据将与预测的结构特征和外部来源的数据相结合，以产生最全面的计算和实验分类。该提案的核心是一个1000 CPU的计算机集群，由霍华德休斯医学研究所和NHGRI共同资助，这将使我们能够执行必要的全基因组计算。来自斯隆基金会的资金和UCSC校园的内部资金也将为MS的发展提供支持。和博士在生物信息学中。该提案的另一个财政部分是对教职员工和学生的支持。通过集中来自多个来源的资金，我们能够培养新一代的科学家，探索新的研究方法，并为人类基因组计划提供重要的服务。

We plan to provide bioinformatics support to the Human Genome Project, and work with the Whitehead Institute for Biomedical Research/MIT Center for Genome Research on comparisons between human and other vertebrate genomes. We will continue to supply monthly assemblies of the currently available human genome sequences, which we have provided since May 2000. Along with each assembly we will provide an integrated database of links to ESTs, mRNAs, SNPs, gene predictions, markers, isochores, contigs, gaps, clones, and homologies with other vertebrates, mapped to specific positions on the assembled genome and viewable side-by-side on the web through an interactive browser. This web browser will facilitate the use of the primary working draft and finished human genome data, stored by NCBI and EBI, by medical and scientific researchers worldwide. In addition to our support role, we propose to develop new methods to use comparative genomics, computational algorithms, and microarray technology to uncover the structure and function of human genes. Sequence homologies with the genes of other vertebrates, combined with EST and mRNA data, will be used to determine gene structure, alternative splicing, and regulatory motifs for human genes. Custom microarrays will be built with specific oligos designed to confirm splicing and regulation for the most medically important genes. Hidden Markov models will be built to model the domain structure of the protein products, and suggest functional classification by homology. Expression data will be combined with predicted structural features and data from external sources to produce the most comprehensive computational and experimental classification possible. The centerpiece of the proposal is a 1000 CPU computer cluster, funded jointly by Howard Hughes Medical Institute and through this award from NHGRI, which will enable us to perform the necessary genome-wide computations. Funding from the Sloan Foundation and internal funding from the UCSC campus will also provide support for the development of M.S. and Ph.D. programs in bioinformatics. The other financial component of the proposal is for staff and student support. By focusing funds from several sources, we are able to train a new generation of scientists, explore new research methodologies, and provide a vital service for the Human Genome Project.