Dataset Information

MiRdentify: high stringency miRNA predictor identifies several novel animal miRNAs.

ABSTRACT: During recent years, miRNAs have been shown to play important roles in the regulation of gene expression. Accordingly, much effort has been put into the discovery of novel uncharacterized miRNAs in various organisms. miRNAs are structurally defined by a hairpin-loop structure recognized by the two-step processing apparatus, Drosha and Dicer, necessary for the production of mature ? 22-nucleotide miRNA guide strands. With the emergence of high-throughput sequencing applications, tools have been developed to identify miRNAs and profile their expression based on sequencing reads. However, as the read depth increases, false-positive predictions increase using established algorithms, underscoring the need for more stringent approaches. Here we describe a transparent pipeline for confident miRNA identification in animals, termed miRdentify. We show that miRdentify confidently discloses more than 400 novel miRNAs in humans, including the first male-specific miRNA, which we successfully validate. Moreover, novel miRNAs are predicted in the mouse, the fruit fly and nematodes, suggesting that the pipeline applies to all animals. The entire software package is available at www.ncrnalab.dk/mirdentify.

SUBMITTER: Hansen TB

PROVIDER: S-EPMC4176371 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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miRdentify: high stringency miRNA predictor identifies several novel animal miRNAs.

Hansen Thomas B TB Venø Morten T MT Kjems Jørgen J Damgaard Christian K CK

Nucleic acids research 20140722 16

During recent years, miRNAs have been shown to play important roles in the regulation of gene expression. Accordingly, much effort has been put into the discovery of novel uncharacterized miRNAs in various organisms. miRNAs are structurally defined by a hairpin-loop structure recognized by the two-step processing apparatus, Drosha and Dicer, necessary for the production of mature ∼ 22-nucleotide miRNA guide strands. With the emergence of high-throughput sequencing applications, tools have been d ...[more]

PMID: 25053842

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Project description:BackgroundMicroRNAs are small molecules which regulate gene expression post-transcriptionally and aberrant expression of several miRNAs is associated with neuroblastoma, a childhood cancer arising from precursor cells of the sympathetic nervous system. Amplification of the MYCN transcription factor characterizes the most clinically aggressive subtype of this disease, and although alteration of p53 signaling is not commonly found in primary tumors, deregulation of proteins involved in this pathway frequently arise in recurrent disease after pharmacological treatment. TH-MYCN is a well-characterized transgenic model of MYCN-driven neuroblastoma which recapitulates many clinicopathologic features of the human disease. Here, we evaluate the dysregulation of miRNAs in tumors from TH-MYCN mice that are either wild-type (TH-MYCN) or deficient (TH-MYCN/p53ER(TAM)) for the p53 tumor suppressor gene.Principal findingsWe analyzed the expression of 591 miRNAs in control (adrenal) and neuroblastoma tumor tissues derived from either TH-MYCN or TH-MYCN/p53ER(TAM) mice, respectively wild-type or deficient in p53. Comparing miRNA expression in tumor and control samples, we identified 159 differentially expressed miRNAs. Using data previously obtained from human neuroblastoma samples, we performed a comparison of miRNA expression between murine and human tumors to assess the concordance between murine and human expression data. Notably, the miR-17-5p-92 oncogenic polycistronic cluster, which is over-expressed in human MYCN amplified tumors, was over-expressed in mouse tumors. Moreover, analyzing miRNAs expression in a mouse model (TH-MYCN/p53ER(TAM)) possessing a transgenic p53 allele that drives the expression of an inactive protein, we identified miR-125b-3p and miR-676 as directly or indirectly regulated by the level of functional p53.SignificanceOur study represents the first miRNA profiling of an important mouse model of neuroblastoma. Similarities and differences in miRNAs expression between human and murine neuroblastoma were identified, providing important insight into the efficacy of this mouse model for assessing miRNA involvement in neuroblastoma and their potential effectiveness as therapeutic targets.

Project description:BACKGROUND:Several methods exist for the prediction of precursor miRNAs (pre-miRNAs) in genomic or sRNA-seq (small RNA sequences) data produced by NGS (Next Generation Sequencing). One key information used for this task is the characteristic hairpin structure adopted by pre-miRNAs, that in general are identified using RNA folders whose complexity is cubic in the size of the input. The vast majority of pre-miRNA predictors then rely on further information learned from previously validated miRNAs from the same or a closely related genome for the final prediction of new miRNAs. With this paper, we wished to address three main issues. The first was methodological and aimed at obtaining a more time-efficient predictor, however without losing in accuracy which represented a second issue. We indeed aimed at better predicting miRNAs at a genome scale, but also from sRNAseq data where in some cases, notably of plants, the current folding methods often infer the wrong structure. The third issue is related to the fact that it is important to rely as little as possible on previously recorded examples of miRNAs. We therefore also sought a method that is less dependent on previous miRNA records. RESULTS:As concerns the first and second issues, we present a novel alternative to a classical folder based on a thermodynamic Nearest-Neighbour (NN) model for computing the free energy and predicting the classical hairpin structure of a pre-miRNA. We show that the free energies thus computed correlate well with those of RNAFOLD. This novel method, called MIRINHO, has quadratic instead of cubic complexity and is much more efficient also in practice. When applied to sRNAseq data of plants, it gives in general better results than classical folders. On the third issue, we show that MIRINHO, which uses as only knowledge the length of the loops and stem-arms and the free energy of the pre-miRNA hairpin, compares well with algorithms that require more information. The results, obtained with different datasets, are indeed similar to those of other approaches with which such a comparison was possible. These needed to be publicly available softwares that could be used on a large input. In some cases, MIRINHO is even better in terms of sensitivity or precision. CONCLUSION:We provide a simpler and much faster method with very reasonable sensitivity and precision, which can be applied without special adaptation to the prediction of both animal and plant pre-miRNAs, using as input either genomic sequences or sRNA-seq data.

Dataset Information

MiRdentify: high stringency miRNA predictor identifies several novel animal miRNAs.

Publications

miRdentify: high stringency miRNA predictor identifies several novel animal miRNAs.

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