SEQUENCE ANALYSIS METHODS ADAPTED TO LARGE SCALE SEQUENCING PROJECTS

适用于大规模测序项目的序列分析方法

• 批准号: 3781259
• 负责人: J M CLAVERIE
• 金额: --
• 依托单位: NATIONAL LIBRARY OF MEDICINE
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/3781259
• 关键词: computer assisted sequence analysis; computer data analysis; computer program /software; computer system design /evaluation

The biological interpretation of the data generated by large scale sequencing projects is plagued by unmanageable large program outputs the size and the noise/signal ratio of which obscure the truly relevant findings. This problem can be attributed to various causes: the increasing size of the databases, their quality (redundancy, experimental and clerical errors) and some intrinsic properties of biological sequences such as repeated elements and local compositional bias. In order to alleviate these problems we have developed a set of programs that are now routinely used in an information enhancement step prior to the analysis of large body of sequence data. Following this steps, the output of gene identification and sequence comparison programs become biologically and statistically interpretable without further processing. In addition we have developed a suite of independent modules that can be used in sequence to automatically analyze large body of experimental data. The power of these tools has been demonstrated in the context of collaborations with experimental groups generating a large amount of sequences (see project Z01-LM-00011-01-BRB). In the meantime, we are also developing new sequence analysis methods than can be both applied to the quality control of sequences already in the databases or to the interpretation of newly determined ones.

大比例尺数据的生物解释 项目排序受到难以管理的大程序输出的困扰 它们的大小和噪声/信号比掩盖了真正相关的 调查结果。这个问题可以归因于多种原因： 不断增加的数据库大小、质量(冗余、 实验错误和文书错误)和一些固有的性质 生物序列，如重复元素和局部组成 偏见。为了缓解这些问题，我们开发了一套 现在经常用于信息增强步骤的程序 在对大量的序列数据进行分析之前。在此之后 步骤，基因鉴定和序列比对的输出 程序变得在生物学和统计学上是可解释的 进一步加工。此外，我们还开发了一套 可按顺序使用的独立模块，以自动 对大量的实验数据进行分析。这些工具的威力有 在与实验合作的背景下进行了演示 生成大量序列的组(请参阅项目 Z01-LM-00011-01-BRB)。与此同时，我们还在开发新的 序列分析的方法比既能应用于质量 对数据库中已有序列的控制或对解释的控制 新确定的。