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MiRTRAP, a computational method for the systematic identification of miRNAs from high throughput sequencing data.

ABSTRACT: MicroRNAs (miRs) have been broadly implicated in animal development and disease. We developed a novel computational strategy for the systematic, whole-genome identification of miRs from high throughput sequencing information. This method, miRTRAP, incorporates the mechanisms of miR biogenesis and includes additional criteria regarding the prevalence and quality of small RNAs arising from the antisense strand and neighboring loci. This program was applied to the simple chordate Ciona intestinalis and identified nearly 400 putative miR loci.

SUBMITTER: Hendrix D

PROVIDER: S-EPMC2884542 | biostudies-literature | 2010

REPOSITORIES: biostudies-literature

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miRTRAP, a computational method for the systematic identification of miRNAs from high throughput sequencing data.

Hendrix David D Levine Michael M Shi Weiyang W

Genome biology 20100406 4

MicroRNAs (miRs) have been broadly implicated in animal development and disease. We developed a novel computational strategy for the systematic, whole-genome identification of miRs from high throughput sequencing information. This method, miRTRAP, incorporates the mechanisms of miR biogenesis and includes additional criteria regarding the prevalence and quality of small RNAs arising from the antisense strand and neighboring loci. This program was applied to the simple chordate Ciona intestinalis ...[more]

PMID: 20370911

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Project description:BACKGROUND: Plant microRNAs are short (~21 nt) non-coding molecules that regulate gene expression by targeting the mRNA cleavage or protein translation inhibition. In this manner, they play many important roles in the cells of living organisms. One of the plant species in which the entire set of miRNAs has not been yet completely identified is Brassica oleracea var. capitata (cabbage). For this reason and for the economic and nutritional importance of this food crop, high-throughput small RNAs sequencing has been performed to discover the novel and conserved miRNAs in mature cabbage leaves. RESULTS: In this study, raw reads generated from three small RNA libraries were bioinformatically processed and further analyzed to select sequences homologous to known B. oleracea and other plant miRNAs. As a result of this analysis, 261 conserved miRNAs (belonging to 62 families) have been discovered. MIR169, MIR167 and MIR166 were the largest miRNA families, while the highest abundance molecules were miR167, miR166, miR168c and miR157a. Among the generated sequencing reads, miRNAs* were also found, such as the miR162c*, miR160a* and miR157a*. The unannotated tags were used in the prediction and evaluation of novel miRNAs, which resulted in the 26 potential miRNAs proposal. The expressions of 13 selected miRNAs were analyzed by northern blot hybridization. The target prediction and annotation for identified miRNAs were performed, according to which discovered molecules may target mRNAs encoding several potential proteins - e.g., transcription factors, polypeptides that regulate hormone stimuli and abiotic stress response, and molecules participating in transport and cell communication. Additionally, KEGG maps analysis suggested that the miRNAs in cabbage are involved in important processing pathways, including glycolysis, glycerolipid metabolism, flavonoid biosynthesis and oxidative phosphorylation. CONCLUSIONS: Conclusively, for the first time, the large set of miRNAs was identified in mature cabbage leaves. Potential targets designation for these miRNAs may suggest their essential role in many plants primary biological processes. Presented study not only supplements the knowledge about B. oleracea miRNAs, but additionally it may be used in other research concerning the improvement of the cabbage cultivation.

Dataset Information

MiRTRAP, a computational method for the systematic identification of miRNAs from high throughput sequencing data.

Publications

miRTRAP, a computational method for the systematic identification of miRNAs from high throughput sequencing data.

Similar Datasets

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