Improving microRNA target prediction with gene expression profiles

BMC Genomics

Methodology Article

Daniel Lepe-Soltero¹  Cei Abreu-Goodger¹  Cesaré Ovando-Vázquez¹ 

[1] Unidad de Genómica Avanzada (Langebio), Centro de Investigación y de Estudios Avanzados del IPN, 36821, Irapuato, Guanajuato, México;

关键词: microRNA target prediction;    Support Vector Machine;    Gene expression profiles;    Biological context;    microRNA perturbation experiments;   

DOI  :  10.1186/s12864-016-2695-1

 received in 2016-01-16, accepted in 2016-05-04,  发布年份 2016

来源: Springer

【 摘 要 】

BackgroundMammalian genomes encode for thousands of microRNAs, which can potentially regulate the majority of protein-coding genes. They have been implicated in development and disease, leading to great interest in understanding their function, with computational methods being widely used to predict their targets. Most computational methods rely on sequence features, thermodynamics, and conservation filters; essentially scanning the whole transcriptome to predict one set of targets for each microRNA. This has the limitation of not considering that the same microRNA could have different sets of targets, and thus different functions, when expressed in different types of cells.ResultsTo address this problem, we combine popular target prediction methods with expression profiles, via machine learning, to produce a new predictor: TargetExpress. Using independent data from microarrays and high-throughput sequencing, we show that TargetExpress outperforms existing methods, and that our predictions are enriched in functions that are coherent with the added expression profile and literature reports.ConclusionsOur method should be particularly useful for anyone studying the functions and targets of miRNAs in specific tissues or cells. TargetExpress is available at: http://targetexpress.ceiabreulab.org/.

【 授权许可】

CC BY   
© Ovando-Vázquez et al. 2016

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