Dataset Information

Genome-wide miRNA seeds prediction in Archaea.

ABSTRACT: Growing evidence indicates that miRNA genes exist in the archaeal genome, though the functional role of such noncoding RNA remains unclear. Here, we integrated the phylogenetic information of available archaeal genomes to predict miRNA seeds (typically defined as the 2-8 nucleotides of mature miRNAs) on the genomic scale. Finally, we found 2649 candidate seeds with significant conservation signal. Eleven of 29 unique seeds from previous study support our result (P value <0.01), which demonstrates that the pipeline is suitable to predict experimentally detectable miRNA seeds. The statistical significance of the overlap between the detected archaeal seeds and known eukaryotic seeds shows that the miRNA may evolve before the divergence of these two domains of cellular life. In addition, miRNA targets are enriched for genes involved in transcriptional regulation, which is consistent with the situation in eukaryote. Our research will enhance the regulatory network analysis in Archaea.

SUBMITTER: Wang S

PROVIDER: S-EPMC4053300 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Genome-wide miRNA seeds prediction in Archaea.

Wang Shengqin S Xu Yuming Y Lu Zuhong Z

Archaea (Vancouver, B.C.) 20140514

Growing evidence indicates that miRNA genes exist in the archaeal genome, though the functional role of such noncoding RNA remains unclear. Here, we integrated the phylogenetic information of available archaeal genomes to predict miRNA seeds (typically defined as the 2-8 nucleotides of mature miRNAs) on the genomic scale. Finally, we found 2649 candidate seeds with significant conservation signal. Eleven of 29 unique seeds from previous study support our result (P value <0.01), which demonstrate ...[more]

PMID: 24948879

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Project description:BackgroundDeep-sequencing has enabled the identification of large numbers of miRNAs and siRNAs, making the high-throughput target identification a main limiting factor in defining their function. In plants, several tools have been developed to predict targets, majority of them being trained on Arabidopsis datasets. An extensive and systematic evaluation has not been made for their suitability for predicting targets in species other than Arabidopsis. Nor, these have not been evaluated for their suitability for high-throughput target prediction at genome level.ResultsWe evaluated the performance of 11 computational tools in identifying genome-wide targets in Arabidopsis and other plants with procedures that optimized score-cutoffs for estimating targets. Targetfinder was most efficient [89% 'precision' (accuracy of prediction), 97% 'recall' (sensitivity)] in predicting 'true-positive' targets in Arabidopsis miRNA-mRNA interactions. In contrast, only 46% of true positive interactions from non-Arabidopsis species were detected, indicating low 'recall' values. Score optimizations increased the 'recall' to only 70% (corresponding 'precision': 65%) for datasets of true miRNA-mRNA interactions in species other than Arabidopsis. Combining the results of Targetfinder and psRNATarget delivers high true positive coverage, whereas the intersection of psRNATarget and Tapirhybrid outputs deliver highly 'precise' predictions. The large number of 'false negative' predictions delivered from non-Arabidopsis datasets by all the available tools indicate the diversity in miRNAs-mRNA interaction features between Arabidopsis and other species. A subset of miRNA-mRNA interactions differed significantly for features in seed regions as well as the total number of matches/mismatches.ConclusionAlthough, many plant miRNA target prediction tools may be optimized to predict targets with high specificity in Arabidopsis, such optimized thresholds may not be suitable for many targets in non-Arabidopsis species. More importantly, non-conventional features of miRNA-mRNA interaction may exist in plants indicating alternate mode of miRNA target recognition. Incorporation of these divergent features would enable next-generation of algorithms to better identify target interactions.

Dataset Information

Genome-wide miRNA seeds prediction in Archaea.

Publications

Genome-wide miRNA seeds prediction in Archaea.

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