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.
|Number of pages||12|
|Journal||Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery|
|Publication status||Published - 2012|