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首页> 外文期刊>Bioinformatics >Affinity Density: a novel genomic approach to the identification of transcription factor regulatory targets.
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Affinity Density: a novel genomic approach to the identification of transcription factor regulatory targets.

机译：亲和力密度：一种新颖的基因组学方法，用于识别转录因子调控靶标。

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摘要

METHODS: A new method was developed for identifying novel transcription factor regulatory targets based on calculating Local Affinity Density. Techniques from the signal-processing field were used, in particular the Hann digital filter, to calculate the relative binding affinity of different regions based on previously published in vitro binding data. To illustrate this approach, the complete genomes of Drosophila melanogaster and D.pseudoobscura were analyzed for binding sites of the homeodomain proteinc Tinman, an essential heart development gene in both Drosophila and Mouse. The significant binding regions were identified relative to genomic background and assigned to putative target genes. Valid candidates common to both species of Drosophila were selected as a test of conservation. RESULTS: The new method was more sensitive than cluster searches for conserved binding motifs with respect to positive identification of known Tinman targets. Our Local Affinity Density method also identified a significantly greater proportion of Tinman-coexpressed genes than equivalent, optimized cluster searching. In addition, this new method predicted a significantly greater than expected number of genes with previously published RNAi phenotypes in the heart. AVAILABILITY: Algorithms were implemented in Python, LISP, R and maxima, using MySQL to access locally mirrored sequence data from Ensembl (D.melanogaster release 4.3) and flybase (D.pseudoobscura). All code is licensed under GPL and freely available at http://www.ohsu.edu/cellbio/dev_biol_prog/affinitydensity/.
机译：方法：开发了一种新方法，用于基于计算局部亲和力密度来识别新型转录因子调控靶标。使用信号处理领域的技术，特别是Hann数字滤波器，基于先前发布的体外结合数据来计算不同区域的相对结合亲和力。为了说明这种方法，分析了果蝇和拟果蝇的完整基因组中同源域蛋白Tinman（果蝇和小鼠中必不可少的心脏发育基因）的结合位点。相对于基因组背景鉴定了重要的结合区，并分配给推定的靶基因。选择果蝇这两种物种共有的有效候选物作为保存性测试。结果：相对于对已知的廷曼靶标的阳性鉴定，新方法比聚类搜索保守的结合基序更为灵敏。与等效的优化聚类搜索相比，我们的局部亲和密度方法还确定了Tinman共表达基因的比例明显更高。此外，这种新方法预测心脏中具有先前发表的RNAi表型的基因数量将大大超过预期。可用性：算法是在Python，LISP，R和maxima中实现的，使用MySQL从Ensembl（D.melanogaster版本4.3）和flybase（D.pseudoobscura）访问本地镜像的序列数据。所有代码均根据GPL许可，可从http://www.ohsu.edu/cellbio/dev_biol_prog/affinitydensity/免费获得。

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