SECOND GENERATION EST CLUSTER AND ANALYSIS TOOLS

据估计，人类基因组由大约10万个基因组成。然而，在任何时候，在给定的细胞类型中，只有一小部分是活跃的。通过聚类和注释对表达序列标签(EST)进行系统分类，对于理解正常的动态平衡和疾病过程具有重要作用。EST聚类涉及组装大型序列数据集，并且由于来自高度相似的基因或由于RNA剪接而具有不同形式的序列的变异性和复杂性而变得复杂。EST集群的完整解决方案需要用于组织、组装、验证、查看和注释单个序列和数据集的软件组件。目前，EST分析仅限于负担得起计算机专家费用的实验室，超出了大多数科学家的能力范围。Geospiza建议为EST集群开发广泛可用的解决方案，其中包括质量信息和比较分析，以区分生物变异和实验错误，并检测替代拼接的信息。还将设计和制作用于表情分析的查看器和界面，以及用于衡量集群准确性的组群。聚类策略的性能和准确性将在研究前列腺癌的模型系统中进行评估。提出的EST聚类和分析的商业应用方法需求很大。ESTs变异的自动检测在药物开发、基础研究和临床研究中有着广泛的应用。侧面受益于Geospiza S现有的技术来存储数以百万计的色谱图数据文件，为支持研究界的服务提供了机会。新的数据库和信息资源也将成为可能，例如拼接变异数据库。以此类推，开发前列腺模型系统所学到的经验将直接应用于癌症生物学的其他领域。

The human genome is estimated to comprise approximately 100,000 genes. However, only a fraction of this total are active in a given cell type at any time. Systematic categorization of Expressed Sequence Tags (ESTs) by clustering and annotation play important roles in understanding normal homeostasis and disease processes. EST clustering involves assembling large data sets of sequences and is complicated by the variability and complexity of sequences that originate from highly similar genes or have alternate forms due to RNA splicing. A complete solution for EST clustering requires software components that are used to organize, assemble, validate, view and annotate individual sequences and sets of data. Currently EST analysis is limited to laboratories that can afford computer specialists and is beyond the reach of most scientists. Geospiza proposes to develop widely available solutions for EST clustering that incorporate quality information and comparative analyses to distinguish biological variation from experimental error and detect alternatively spliced messages. Viewers and interfaces for expression analysis and curating sets of clusters for measuring clustering accuracy will also be designed and prototyped. Performance and accuracy of clustering strategies will be evaluated in a model system studying prostate cancer. PROPOSED COMMERCIAL APPLICATION Methods for EST clustering and analysis are in high demand. Automatically detecting variation in ESTs has numerous applications in drug development, basic research, and clinical studies. Side benefits from scaling Geospiza s existing technology to store millions of chromatogram data files present opportunities for services to support the research community. New databases and information resources will also become possible such as databases of splice variation. Lessons learned developing the prostate model system will have, by analogy, direct application other areas of cancer biology.