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Nucleic Acids Res. 2012 May;40(10):4298-305. doi: 10.1093/nar/gks043. Epub 2012 Jan 28.

Performance comparison and evaluation of software tools for microRNA deep-sequencing data analysis.

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Center for Systems Biology, Soochow University, Suzhou 215006, China.


With the development of next-generation sequencing (NGS) techniques, many software tools have emerged for the discovery of novel microRNAs (miRNAs) and for analyzing the miRNAs expression profiles. An overall evaluation of these diverse software tools is lacking. In this study, we evaluated eight software tools based on their common feature and key algorithms. Three deep-sequencing data sets were collected from different species and used to assess the computational time, sensitivity and accuracy of detecting known miRNAs as well as their capacity for predicting novel miRNAs. Our results provide useful information for researchers to facilitate their selection of the optimal software tools for miRNA analysis depending on their specific requirements, i.e. novel miRNAs discovery or miRNA expression profile analysis of sequencing data sets.

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