Mirsynergy：通过重叠邻域扩展检测协同 miRNA 调控模块。
Mirsynergy: detecting synergistic miRNA regulatory modules by overlapping neighbourhood expansion.
机构信息
Department of Computer Science, The Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada, College of Information Science and Engineering, Hunan University, Changsha, Hunan, 410082, China and Banting and Best Department of Medical Research and Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada Department of Computer Science, The Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada, College of Information Science and Engineering, Hunan University, Changsha, Hunan, 410082, China and Banting and Best Department of Medical Research and Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada.
Department of Computer Science, The Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada, College of Information Science and Engineering, Hunan University, Changsha, Hunan, 410082, China and Banting and Best Department of Medical Research and Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada.
出版信息
Bioinformatics. 2014 Sep 15;30(18):2627-35. doi: 10.1093/bioinformatics/btu373. Epub 2014 Jun 3.
MOTIVATION
Identification of microRNA regulatory modules (MiRMs) will aid deciphering aberrant transcriptional regulatory network in cancer but is computationally challenging. Existing methods are stochastic or require a fixed number of regulatory modules.
RESULTS
We propose Mirsynergy, an efficient deterministic overlapping clustering algorithm adapted from a recently developed framework. Mirsynergy operates in two stages: it first forms MiRMs based on co-occurring microRNA (miRNA) targets and then expands each MiRM by greedily including (excluding) mRNAs into (from) the MiRM to maximize the synergy score, which is a function of miRNA-mRNA and gene-gene interactions. Using expression data for ovarian, breast and thyroid cancer from The Cancer Genome Atlas, we compared Mirsynergy with internal controls and existing methods. Mirsynergy-MiRMs exhibit significantly higher functional enrichment and more coherent miRNA-mRNA expression anti-correlation. Based on Kaplan-Meier survival analysis, we proposed several prognostically promising MiRMs and envisioned their utility in cancer research.
AVAILABILITY AND IMPLEMENTATION
Mirsynergy is implemented/available as an R/Bioconductor package at www.cs.utoronto.ca/∼yueli/Mirsynergy.html.
动机
识别 microRNA 调控模块 (MiRMs) 将有助于破解癌症中异常的转录调控网络，但这在计算上具有挑战性。现有的方法是随机的，或者需要固定数量的调控模块。
结果
我们提出了 Mirsynergy，这是一种从最近开发的框架中改编的高效确定性重叠聚类算法。Mirsynergy 分两个阶段运行：首先根据共现的 microRNA (miRNA) 靶标形成 MiRMs，然后通过贪婪地将 mRNA 纳入（排除）MiRM 来最大化协同评分来扩展每个 MiRM，协同评分是 miRNA-mRNA 和基因-基因相互作用的函数。使用来自癌症基因组图谱的卵巢癌、乳腺癌和甲状腺癌的表达数据，我们将 Mirsynergy 与内部对照和现有方法进行了比较。Mirsynergy-MiRMs 表现出明显更高的功能富集和更一致的 miRNA-mRNA 表达负相关。基于 Kaplan-Meier 生存分析，我们提出了几个有前途的预后 MiRMs，并设想它们在癌症研究中的应用。
可用性和实现
Mirsynergy 作为一个 R/Bioconductor 包在 www.cs.utoronto.ca/∼yueli/Mirsynergy.html 上实现/可用。
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