Abstract
MicroRNAs are crucial for post-transcriptional gene regulation, and their dysregulation has been associated with diseases like cancer and, therefore, their analysis has become popular. The experimental discovery of miRNAs is cumbersome and, thus, many computational tools have been proposed. Here we assess 13 ab initio pre-miRNA detection approaches using all relevant, published, and novel data sets while judging algorithm performance based on ten intrinsic performance measures. We present an extensible framework, izMiR, which allows for the unbiased comparison of existing algorithms, adding new ones, and combining multiple approaches into ensemble methods. In an exhaustive attempt, we condense the results of millions of computations and show that no method is clearly superior; however, we provide a guideline for biomedical researchers to select a tool. Finally, we demonstrate that combining all of the methods into one ensemble approach, for the first time, allows reliable purely computational pre-miRNA detection in large eukaryotic genomes.
Highlights
MicroRNAs are crucial for post-transcriptional gene regulation, and their dysregulation has been associated with diseases like cancer and, their analysis has become popular
Performance comparison among tools cannot be done based on their published metrics since they are based on different data sets, used different approaches to establishing pre-miRNA detection models, and present diverse performance measures
All available tools for pre-miRNA detection discussed here (Table 1) employ machine learning for which training data quality is of crucial importance
Summary
MicroRNAs are crucial for post-transcriptional gene regulation, and their dysregulation has been associated with diseases like cancer and, their analysis has become popular. We demonstrate that combining all of the methods into one ensemble approach, for the first time, allows reliable purely computational pre-miRNA detection in large eukaryotic genomes. Since their discovery, about two decades ago, microRNAs (miRNAs) have been detected in a large number of organisms including microbes[1], sponges[2], metazoan[3], plants[4], and viruses[5]. Considering the massive impact of miRNAs especially in human disease and plant breeding (i.e., human nutrition) and taking into account our inability to experimentally determine all pre-miRNAs, it is crucial for the community to be able to rely on computational methods for pre-miRNA detection. Previous evaluations have been performed for a subset of the state of the art, but only when a new tool was to be published and using different data as well as varying parameters, which obfuscates comparative evaluations
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