A method for similarity search of genomic positional expression using CAGE
A method for similarity search of genomic positional expression using CAGE
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DOI：
10.1371/journal.pgen.0020044
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发表时间：
2006-04-01
期刊：
影响因子：
4.5
通讯作者：
Matsuda, Hideo
中科院分区：
文献类型：
--
作者：
Seno, Shigeto;Takenaka, Yoichi;Matsuda, Hideo
随着基因组研究的进展，基因在基因组上的分布并不是随机的，这一点越来越明显。在几个真核生物中已经报道了分布在局部基因组位置的基因簇。根据组织类型和发育阶段的不同，染色体上邻近或邻近位置的基因表达之间存在着不同的相关性。此外，在几个案例中，它们的转录本通过转录干扰和基因组印记等过程控制表观遗传转录，呈簇状出现。具有相似机制的基因组区域表现出相似的表达模式，同一基因组区域的表达特征因组织类型和发育阶段的不同而不同，这是合理的。在这项研究中，我们使用帽分析基因表达(CAGE)方法来分析基因表达模式，以探索小鼠转录组的系统观点。通过计算固定长度区域映射的笼形标签的数量，我们可以确定基因组表达水平。这些表达水平被标准化、量化，并转换成四种类型的描述符，允许基因组上的表达模式用字符串表示。我们使用与序列分析相同的方式使用动态编程对它们进行分析。我们开发了一种新的算法，从基因组位置表达的角度提供了对基因组的新观点。在对染色体和组织表达模式的相似性搜索中，我们发现了具有基因簇的区域，这些基因簇根据组织类型显示出彼此相似的表达模式。我们的结果表明，具有正义-反义转录的区域可能在正向和反向链之间显示了类似的表达模式。
With the advancement of genome research, it is becoming clear that genes are not distributed on the genome in random order. Clusters of genes distributed at localized genome positions have been reported in several eukaryotes. Various correlations have been observed between the expressions of genes in adjacent or nearby positions along the chromosomes depending on tissue type and developmental stage. Moreover, in several cases, their transcripts, which control epigenetic transcription via processes such as transcriptional interference and genomic imprinting, occur in clusters. It is reasonable that genomic regions that have similar mechanisms show similar expression patterns and that the characteristics of expression in the same genomic regions differ depending on tissue type and developmental stage. In this study, we analyzed gene expression patterns using the cap analysis gene expression ( CAGE) method for exploring systematic views of the mouse transcriptome. Counting the number of mapped CAGE tags for fixed-length regions allowed us to determine genomic expression levels. These expression levels were normalized, quantified, and converted into four types of descriptors, allowing the expression patterns along the genome to be represented by character strings. We analyzed them using dynamic programming in the same manner as for sequence analysis. We have developed a novel algorithm that provides a novel view of the genome from the perspective of genomic positional expression. In a similarity search of expression patterns across chromosomes and tissues, we found regions that had clusters of genes that showed expression patterns similar to each other depending on tissue type. Our results suggest the possibility that the regions that have sense - antisense transcription show similar expression patterns between forward and reverse strands.