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Motif discovery in upstream sequences of coordinately expressed genes

机译:协调表达基因上游序列中的基序发现

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The paper presents a genetic mining approach to discover highly conserved motifs amongst upstream sequences of co-regulated genes. These motifs represent putative cis-regulatory elements that could play an important role in the co-ordinated expression of these genes. A structured genetic algorithm (St-GA) was used to evolve candidate motifs of variable length. Fitness values were assigned as a function of high scoring alignments performed with NCBI BLAST. The St-GA performed favorable with respect to existing methods on simple (l,k) insertion problems, but was unable to overcome the (l,4) insertion problem that has proved elusive to other methods. Deterministic crowding was added to the St-GA to help cope with the multimodal nature of real-world genomic data. The genetic search was performed on a set of genes selected based on their expression values as highly predictive of a subtype of pediatric ALL. Four high scoring motifs were obtained that successfully matched subsequences of cis-elements found in the TRANSFAC database. Results demonstrated that the St-GA approach to motif finding has the potential to be a competitive method for this type of problem.
机译:本文提出了一种遗传挖掘方法,以在共调控基因的上游序列中发现高度保守的基序。这些基序代表假定的顺式调控元件,这些元件可能在这些基因的协同表达中发挥重要作用。结构遗传算法(St-GA)用于进化可变长度的候选基序。将适应度值分配为使用NCBI BLAST执行的高分比对的函数。关于简单的(l,k)插入问题,St-GA相对于现有方法表现良好,但无法克服已证明无法通过其他方法进行的(l,4)插入问题。确定性拥挤已添加到St-GA中,以帮助应对现实世界中基因组数据的多峰性质。遗传搜索是根据一组基因的表达值进行的,这些基因的表达值可高度预测小儿ALL的亚型。获得了四个高得分的基序,这些基序成功地匹配了在TRANSFAC数据库中发现的顺式子序列。结果表明,St-GA进行主题查找的方法有可能成为解决此类问题的一种竞争方法。

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