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首页> 外文会议>The 7th Asia-Pacific Bioinformatics Conference（第七届亚太生物信息学大会） >ModuleDigger: an itemset mining framework for the detection of cis-regulatory modules
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ModuleDigger: an itemset mining framework for the detection of cis-regulatory modules

机译：ModuleDigger：用于检测顺式调控模块的项目集挖掘框架

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摘要

Background: The detection of cis-regulatory modules (CRMs) that mediate transcriptional responses in eukaryotes remains a key challenge in the postgenomic era. A CRM is characterized by a set of co-occurring transcription factor binding sites (TFBS). In silico methods have been developed to search for CRMs by determining the combination of TFBS that are statistically overrepresented in a certain geneset. Most of these methods solve this combinatorial problem by relying on computational intensive optimization methods. As a result their usage is limited to finding CRMs in small datasets (containing a few genes only) and using binding sites for a restricted number of transcription factors (TFs) out of which the optimal module will be selected.Results: We present an itemset mining based strategy for computationally detecting cisregulatory modules (CRMs) in a set of genes. We tested our method by applying it on a large benchmark data set, derived from a ChiP-Chip analysis and compared its performance with other well known cis-regulatory module detection tools.Conclusions: We show that by exploiting the computational efficiency of an itemset mining approach and combining it with a well designed statistical scoring scheme, we were able to prioritize the biologically valid CRMs in a large set of coregulated genes using binding sites for a large number of potential TFs as input.
机译：背景：检测介导真核生物转录反应的顺式调控模块（CRM）仍然是后基因组时代的关键挑战。 CRM的特征是一组共同出现的转录因子结合位点（TFBS）。已开发出计算机方法，通过确定在某些基因组中统计学上过表达的TFBS的组合来搜索CRM。这些方法中的大多数通过依靠计算密集型优化方法来解决此组合问题。因此，它们的用途仅限于在小型数据集中（仅包含几个基因）寻找CRM，并使用有限数量的转录因子（TF）的结合位点，从中选择最佳模块。结果：我们提出了一个项目集基于挖掘的策略，用于计算检测一组基因中的顺式调控模块（CRM）。我们通过将其应用到大量基于ChiP-Chip分析的基准数据集上来测试了我们的方法，并将其性能与其他众所周知的顺式调控模块检测工具进行了比较。结论：我们表明，通过利用项集挖掘的计算效率通过将其与精心设计的统计评分方案相结合，我们能够使用大量潜在TF的结合位点作为输入，对一大批有核心基因的生物有效CRM进行优先排序。

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  • 会议地点 Beijing(CN);Beijing(CN)
  • 作者

    Hong Sun; Tijl De Bie; Valerie Storms; Qiang Fu; Thomas Dhollander; Karen Lemmens; Annemieke Verstuyf; Bart De Moor; Kathleen Marchal;

  • 作者单位

    Department of Electrical Engineering, Katholieke Universiteit Leuven, Kasteelpart Arenberg 10, 3001 Leuven, Belgium;

    Department of Engineering Mathematics, university of Bristol, Bristol BS8 1TR, UK;

    Department of Microbial and Molecular systems, Katholieke Universiteit Leuven, Kasteelpart Arenberg 20, 3001 Leuven, Belgium;

    Department of Microbial and Molecular systems, Katholieke Universiteit Leuven, Kasteelpart Arenberg 20, 3001 Leuven, Belgium;

    Department of Electrical Engineering, Katholieke Universiteit Leuven, Kasteelpart Arenberg 10, 3001 Leuven, Belgium;

    Department of Electrical Engineering, Katholieke Universiteit Leuven, Kasteelpart Arenberg 10, 3001 Leuven, Belgium;

    Laboratory for experimental medicine and endocrinology, Katholieke Universiteit Leuven, 3000 Leuven, Belgium;

    Department of Electrical Engineering, Katholieke Universiteit Leuven, Kasteelpart Arenberg 10, 3001 Leuven, Belgium;

    Department of Microbial and Molecular systems, Katholieke Universiteit Leuven, Kasteelpart Arenberg 20, 3001 Leuven, Belgium;

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