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首页> 外文期刊>Nucleic Acids Research >Modular discovery of monomeric and dimeric transcription factor binding motifs for large data sets
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Modular discovery of monomeric and dimeric transcription factor binding motifs for large data sets

机译：用于大数据集的单体和二聚体转录因子绑定基序的模块化发现

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摘要

In some dimeric cases of transcription factor (TF) binding, the specificity of dimeric motifs has been observed to differ notably from what would be expected were the two factors to bind to DNA independently of each other. Current motif discovery methods are unable to learn monomeric and dimeric motifs in modular fashion such that deviations from the expected motif would become explicit and the noise from dimeric occurrences would not corrupt monomeric models. We propose a novel modeling technique and an expectation maximization algorithm, implemented as software tool MODER, for discovering monomeric TF binding motifs and their dimeric combinations. Given training data and seeds for monomeric motifs, the algorithm learns in the same probabilistic framework a mixture model which represents monomeric motifs as standard position-specific probability matrices (PPMs), and dimeric motifs as pairs of monomeric PPMs, with associated orientation and spacing preferences. For dimers the model represents deviations from pure modular model of two independent monomers, thus making co-operative binding effects explicit. MODER can analyze in reasonable time tens of Mbps of training data. We validated the tool on HT-SELEX and ChIP-seq data. Our findings include some TFs whose expected model has palindromic symmetry but the observed model is directional.
机译：在转录因子（TF）结合的一些二聚体病例中，已经观察到二聚体基序的特异性尤其不同，因此预期的是与彼此独立地结合DNA的两个因素。目前的主题发现方法不能以模块化方式学习单体和二聚体基序，使得与预期图案的偏差将明确，二聚体出现的噪声不会损坏单体模型。我们提出了一种新颖的建模技术和期望最大化算法，实现为软件工具调节器，用于发现单体TF绑定主题及其二聚体组合。给定单体图案的培训数据和种子，算法在相同的概率框架中学习混合模型，该模型代表单体基序作为标准位置特异性概率矩阵（PPMS），以及作为单体PPM的对的二聚体基序，具有相关的方向和间距偏好。对于二聚体，该模型代表了两种独立单体的纯模块模型的偏差，从而使合作结合效应明确。 SOMER可以分析合理的时间训练数据。我们在HT-SELEX和CHIP-SEQ数据上验证了该工具。我们的研究结果包括一些TFS，其预期模型具有回文对称性，但观察到的模型是定向的。

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  • 来源
    《Nucleic Acids Research》 |2018年第8期|共16页
  • 作者

    Toivonen Jarkko; Kivioja Teemu; Jolma Arttu; Yin Yimeng; Taipale Jussi; Ukkonen Esko;

  • 作者单位

    Univ Helsinki Dept Comp Sci POB 68 FI-00014 Helsinki Finland;

    Univ Helsinki Genome Scale Biol Program POB 63 FI-00014 Helsinki Finland;

    Karolinska Inst Div Funct Genom &

    Syst Biol Dept Med Biochem &

    Biophys SE-14183 Stockholm Sweden;

    Karolinska Inst Div Funct Genom &

    Syst Biol Dept Med Biochem &

    Biophys SE-14183 Stockholm Sweden;

    Univ Helsinki Genome Scale Biol Program POB 63 FI-00014 Helsinki Finland;

    Univ Helsinki Dept Comp Sci POB 68 FI-00014 Helsinki Finland;

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  • 正文语种 eng
  • 中图分类 生物化学;
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