首页> 外文期刊>IEEE/ACM transactions on computational biology and bioinformatics >Identifying TF Binding Motifs from a Partial Set of Target Genes and its Application to Regulatory Network Inference
【24h】

Identifying TF Binding Motifs from a Partial Set of Target Genes and its Application to Regulatory Network Inference

机译:从部分靶基因识别TF结合基序及其在监管网络推理的应用中

获取原文
获取原文并翻译 | 示例

摘要

Motif identification has been one of the most widely studied problems in bioinformatics. Many methods have been developed to discover binding motifs from a large set of genes. But when the given genes are only a partial set of target genes, the statistical significance usually contains a bias towards the input. If we can identify the TF binding motif from a partial set of target genes, we can save the labor costs and resources for doing many experiments. In this paper, we propose a method MISA (Motif Identification through Segments Assembly) to identify binding motifs from a subset of target genes. By ranking and assembling the segments, MISA discovers a set of binding motifs with the best length to fit our proposed objective function. We also predict the additional target genes as an application of regulatory network inference. We compare our approach with two widely used methods MEME and AlignACE by analyzing both the quality of the binding motif and network inference. Using two model organisms S. cerevisiae and E. coli, we show that with 20 percent of the target genes (minimum sample size of 20), we can achieve a motif similarity of 82 percent with the known motifs. Our results also show that 73 percent of target genes on average can be correctly predicted without introducing many false target genes.
机译:主题识别是生物信息学中最广泛研究的问题之一。已经开发了许多方法来发现来自一大组基因的结合图案。但是,当给定的基因只是部分靶基因时,统计显着性通常含有朝向输入的偏差。如果我们可以从部分靶基因识别TF结合图案,我们可以节省劳动力成本和资源以进行许多实验。在本文中,我们提出了一种方法MISA(通过区段组装的基序识别),以鉴定来自靶基因的子集的结合基序。通过排序和组装段,MISA发现一组绑定图案,具有最佳长度,以适应我们所提出的客观函数。我们还将额外的目标基因预测为监管网络推论的应用。我们通过分析绑定主题和网络推断的质量来比较我们的方法和对准的两个广泛使用的方法和对准。使用两种模型生物体S.酿酒酵母和大肠杆菌,我们表明,20%的靶基因(最小样本大小为20),我们可以通过已知的基序达到82%的主题相似性。我们的结果还表明,在不引入许多假靶基因的情况下,可以正确预测73%的目标基因。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号