FiRePat-Finding Regulatory Patterns between sRNAs and Genes
FiRePat-Finding Regulatory Patterns between sRNAs and Genes
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FiRePat-寻找 sRNA 和基因之间的调控模式
DOI：
10.1002/widm.1053
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发表时间：
2012
期刊：
影响因子：
--
通讯作者：
Mohorianu I
中科院分区：
文献类型：
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作者：
Mohorianu I
小RNA是通过RNA沉默来调节基因表达的调控RNA片段。由于sRNA是负调节因子，因此通常认为sRNA的表达谱及其靶点是负相关的。最近，已经发现sRNA的表达与其靶点之间正相关的实例。目前，尚不清楚有多少sRNA-靶标对是正相关和负相关的，也不清楚在什么情况下（例如，在该处理下）可以观察到这些相关性中的任何一种。为了确定这一点，第一步之一是开发工具来进行sRNA和基因表达水平共变的全基因组表征。我们提出了FiRePat-Finding Regulatory Patterns-一种适用于大型数据集的无监督数据挖掘工具，通常由sRNA和mRNAs的高通量测序或微阵列实验产生，检测具有相关表达水平的sRNA基因对。该方法包括三个步骤：首先，我们选择差异表达的sRNA和基因;第二，我们计算所有可能的sRNA-基因对的sRNA和基因序列之间的相关性;第三，我们聚类sRNA或基因表达序列，同时在其他序列中诱导聚类。FiRePat的潜在用途是使用公开的植物和动物的sRNA和mRNA数据集。FiRePat的标准输出，sRNA和mRNA形成的相关对的列表，可用于研究各自表达模式的原因和后果。© 2012 Wiley Periodicals，Inc.这篇文章分类下：生物学发展>生物数据挖掘
Small RNAs are regulatory RNA fragments which, through RNA silencing, can regulate the expression of genes. Because sRNAs are negative regulators it is generally assumed that expression profiles of sRNAs and their targets are negatively correlated. Recently, examples of positive correlation between the expression of sRNAs and their targets have been discovered. At the moment, it is not known how many sRNA‐target pairs are positively and negatively correlated, and it is also not clear in what situations (e.g., under which treatments) any of these correlations can be observed. To determine this, one of the first steps is to develop tools to carry out a genome wide characterization of covariation of expression levels of sRNAs and genes. We present FiRePat—Finding Regulatory Patterns—an unsupervised data mining tool applicable to large datasets, typically produced by high throughput sequencing of sRNAs and mRNAs or microarray experiments, that detects sRNA‐gene pairs with correlated expression levels. The method consists of three steps: first, we select differentially expressed sRNAs and genes; second, we compute the correlation between sRNA and gene series for all possible sRNA–gene pairs; and third, we cluster the sRNA or gene expression series, simultaneously inducing clusters in the other series. Potential uses of FiRePat are presented using publicly available sRNA and mRNA datasets for both plants and animals. The standard output of FiRePat, a list of correlated pairs formed with sRNAs and mRNAs, can be used to investigate the cause and consequences of the respective expression patterns. © 2012 Wiley Periodicals, Inc.This article is categorized under:Algorithmic Development > Biological Data Mining