SEQUENCE ANALYSIS METHODS ADAPTED TO LARGE SCALE SEQUENCING PROJECTS

生物学解释的数据产生的大规模 测序项目受到高假阳性率的困扰 关于外显子和编码区的位置，和/或 发现与现有 数据库。这种情况会导致无法管理的大型程序输出， 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大的波动， 自然序列和数据库序列的局部信息内容。后 将冗余（重复）分为三类，我们有 开发了两个程序（XNU和Xblast），现在在一个 信息增强步骤之前，分析大的身体， 序列数据在此步骤之后，基因鉴定的输出和 序列比较程序在生物学和统计学上 无需进一步处理即可解释。这种方法的力量在于 通过分析大的人类基因组重叠群（90 kb，来自 6号染色体上的HLA III类区域，距离Xp22.3区域67 kb）以及 通过对大量EST数据集的分析。

The biological interpretation of the data generated by large scale sequencing projects is plagued by a high rate of false positive results concerning the location of exons and coding regions, and /or the statistical significance of similarities found with the existing databases. This situation leads to unmanageable large program outputs the size and the noise/signal ratio of which obscure the truly relevant findings. This problem can be attributed to large fluctuations in the local information content of both natural and database sequences. After classifying the redundancies (repeats) in three categories, we have developed two programs (XNU and Xblast) 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. The power of this approach was illustrated by the analysis of large human genomic contigs (90 kb from the HLA class III region on chromosome 6, 67 kb from Xp22.3 region) as well as from the analysis of large EST data sets.