28 research outputs found

    RNA deep sequencing reveals differential microRNA expression during development of sea urchin and sea star.

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    microRNAs (miRNAs) are small (20-23 nt), non-coding single stranded RNA molecules that act as post-transcriptional regulators of mRNA gene expression. They have been implicated in regulation of developmental processes in diverse organisms. The echinoderms, Strongylocentrotus purpuratus (sea urchin) and Patiria miniata (sea star) are excellent model organisms for studying development with well-characterized transcriptional networks. However, to date, nothing is known about the role of miRNAs during development in these organisms, except that the genes that are involved in the miRNA biogenesis pathway are expressed during their developmental stages. In this paper, we used Illumina Genome Analyzer (Illumina, Inc.) to sequence small RNA libraries in mixed stage population of embryos from one to three days after fertilization of sea urchin and sea star (total of 22,670,000 reads). Analysis of these data revealed the miRNA populations in these two species. We found that 47 and 38 known miRNAs are expressed in sea urchin and sea star, respectively, during early development (32 in common). We also found 13 potentially novel miRNAs in the sea urchin embryonic library. miRNA expression is generally conserved between the two species during development, but 7 miRNAs are highly expressed in only one species. We expect that our two datasets will be a valuable resource for everyone working in the field of developmental biology and the regulatory networks that affect it. The computational pipeline to analyze Illumina reads is available at http://www.benoslab.pitt.edu/services.html.</p

    Identification of promoters of intergenic miRNA genes.

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    <p><i>miRNA</i>: miRNA gene symbol, multiple symbols designate cluster of co-expressed miRNAs; <i>Chromosomal location</i>: the chromosomal position and orientation of the miRNA gene; <i>ChIP-chip region</i>: the nearest region with a statistically significant peak; <i>CPPP model</i>: the CpG (CpG+) or non-CpG (CpG−) model used for the TSS prediction; <i>Predicted TSS</i>: TSS predicted by CPPP; <i>Distance</i>: the distance of the predicted TSS from the most 5′ pre-miRNA transcript. Bold letters designate previously verified TSSs.</p

    Pol II ChIP-chip results for miR-10a.

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    <p>The blue arrow represents the location and transcriptional direction of hsa-miR-10a. The red dashes represent the location and value of the ChIP-chip probes. <i>TSS</i> – transcription start site of this miRNA.</p

    Identification of promoters for <i>intragenic</i> miRNA genes.

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    <p>Column names as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0005279#pone-0005279-t001" target="_blank">Table 1</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0005279#pone-0005279-t002" target="_blank">2</a>. Bold letters designate genes whose expression was found to be anti-correlated with their host genes.</p

    Performance of the SVM models in predicting CpG+ and CpG− promoters.

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    <p>Two SVM models were evaluated in the prediction of the CpG+ promoters: one with random intergenic background (CpG+/Rnd_bg) and one with intergenic background with similar GC content (CpG+/GC_bg). <i>Sn</i> – sensitivity, <i>Sp</i> – specificity.</p

    Predicting efficiency of Drosophila-trained ComiR on various datasets.

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    <p>(A) Self-test on the Drosophila Ago1-IP dataset consist of Set I (positive examples) and equal number of negative examples (from Set IV). (B) Performance on an external Drosophila Ago1-IP dataset consisting of Set III (positive examples) and the remaining of Set IV (negative examples). This Drosophila dataset was not used in training ComiR. (C) SN <i>vs.</i> threshold on an external <i>C. elegans</i> AIN-IP dataset (not an ROC curve due to inability to define a negative dataset). (D) Performance on an external human PAR-CLIP dataset. In all cases, TargetScan was used without the evolutionary conservation feature resulting in a binary outcome. For the human dataset the reader can find a continuous TargetScan ROC curve in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002830#pcbi.1002830.s003" target="_blank">Figure S3B</a>, plotted using the <i>context score</i>.</p

    The effect of Fermi-Dirac model in miRNA target prediction.

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    <p>(A) Overlap of predicted targets from PITA and miRanda using a naïve combination of energy scores. (B) Target overlap between PITA and miRanda using the Fermi-Dirac energy score combination. (C) Receiver-operating Characteristic (ROC) curves of PITA and miRanda predictions with naïve (solid lines) and Fermi-Dirac (broken lines) energy score combination. AUC: area under the curve. Positive and negative sets were derived from the Ago1 IP data (Materials and Methods).</p
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