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: 3759297
• 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/3759297
• 关键词: chemical information system; computer assisted sequence analysis; computer data analysis; computer program /software; computer system design /evaluation; method development

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.

生物学解释的数据产生的大规模 排序项目受到难以管理的大型项目输出的困扰， 其大小和噪声/信号比掩盖了真正相关的 调查结果。这个问题可以归因于各种原因： 增加数据库的大小，其质量（冗余，实验 和笔误）以及生物学的一些内在性质 序列，如重复的元素和局部成分偏差。在 为了缓解这些问题，我们开发了一套程序， 现在通常用于信息增强步骤， 大量序列数据的分析。按照这些步骤， 基因鉴定和序列比较程序的输出成为 无需进一步处理即可在生物学和统计学上解释。 此外，我们还开发了一套独立的模块， 用于对大型实验体的自动分析 数据这些工具的威力已在以下方面得到证明： 与实验小组的合作产生了大量的 序列（参见项目Z 01-LM-00011-01-BRB）。与此同时，我们还 开发新的序列分析方法， 已经在数据库中的序列的质量控制或 新定义的解释。