SEQUENCE ANALYSIS METHODS ADAPTED TO LARGE SCALE SEQUENCING PROJECTS

大规模产生的数据的生物学解释 测序项目受到难以管理的大型程序输出的困扰 尺寸和噪声/信号比掩盖了真正相关的信息 调查结果。此问题可归因于多种原因： 数据库规模不断增加，数据库质量不断增加（冗余、实验性） 和笔误）以及生物的一些内在特性 序列，例如重复元素和局部成分偏差。在 为了缓解这些问题我们制定了一套方案 现在通常用于之前的信息增强步骤 大量序列数据的分析。按照此步骤， 基因鉴定和序列比较程序的输出变成 无需进一步处理即可进行生物学和统计学解释。 此外，我们还开发了一套独立的模块，可以 按顺序自动分析大量实验数据 数据。这些工具的威力已在以下环境中得到了证明： 与实验小组的合作产生了大量 序列（参见项目 Z01-LM-00011-01-BRB）。与此同时，我们也在 开发新的序列分析方法，可同时应用于 对数据库中已有的序列进行质量控制或 新确定的解释。

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.