記述言語：日本語   掲載種別：記事・総説・解説・論説等（大学・研究所紀要）

DOI： 10.0006/j.molcel.2004.06.003

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記述言語：英語   掲載種別：研究論文（学術雑誌）

microRNAs (miRNAs) are tiny endogenous RNAs that have been discovered in animals and plants, and direct the post-transcriptional regulation of target mRNAs for degradation or translational repression via binding to the 3'UTRs and the coding exons. To gain insight into the biological role of miRNAs, it is essential to identify the full repertoire of mRNA targets (target genes). A number of computer programs have been developed for miRNA-target prediction. These programs essentially focus on potential binding sites in 3'UTRs, which are recognized by miRNAs according to specific base-pairing rules. Here, we introduce a novel method for miRNA-target prediction that is entirely independent of existing approaches. The method is based on the hypothesis that transcription of a miRNA and its target genes tend to be co-regulated by common transcription factors. This hypothesis predicts the frequent occurrence of common cis-elements between promoters of a miRNA and its target genes. That is, our proposed method first identifies putative cis-elements in a promoter of a given miRNA, and then identifies genes that contain common putative cis-elements in their promoters. In this paper, we show that a significant number of common cis-elements occur in ~28% of experimentally supported human miRNA-target data. Moreover, we show that the prediction of human miRNA-targets based on our method is statistically significant. Further, we discuss the random incidence of common cis-elements, their consensus sequences, and the advantages and disadvantages of our method. This is the first report indicating prevalence of transcriptional regulation of a miRNA and its target genes by common transcription factors and the predictive ability of miRNA-targets based on this property.

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記述言語：英語   掲載種別：研究論文（学術雑誌）

Motivation: How to find motifs from genome-scale functional sequences, such as all the promoters in a genome, is a challenging problem. Word-based methods count the occurrences of oligomers to detect excessively represented ones. This approach is known to be fast and accurate compared with other methods. However, two problems have hampered the application of such methods to largescale data. One is the computational cost necessary for clustering similar oligomers, and the other is the bias in the frequency of fixedlength oligomers, which complicates the detection of significant words. Results: We introduce a method that uses a DNA Gray code and equiprobable oligomers, which solve the clustering problem and the oligomer bias, respectively. Our method can analyze 18 000 sequences of ~1 kbp long in 30 s. We also show that the accuracy of our method is superior to that of a leading method, especially for large-scale data and small fractions of motif-containing sequences. © The Author(s) 2011. Published by Oxford University Press. All rights reserved.

DOI： 10.1093/bioinformatics/btr606

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記述言語：英語   掲載種別：研究論文（学術雑誌）

UPF1 eliminates aberrant mRNAs harboring premature termination codons, and regulates the steady-state levels of normal physiological mRNAs. Although genome-wide studies of UPF1 targets performed, previous studies did not distinguish indirect UPF1 targets because they could not determine UPF1-dependent altered RNA stabilities. Here, we measured the decay rates of the whole transcriptome in UPF1-depleted HeLa cells using BRIC-seq, an inhibitor-free method for directly measuring RNA stability. We determined the half-lives and expression levels of 9,229 transcripts. An amount of 785 transcripts were stabilized in UPF1-depleted cells. Among these, the expression levels of 76 transcripts were increased, but those of the other 709 transcripts were not altered. RNA immunoprecipitation showed UPF1 bound to the stabilized transcripts, suggesting that UPF1 directly degrades the 709 transcripts. Many UPF1 targets in this study were newly identified. This study clearly demonstrates that direct determination of RNA stability is a powerful approach for identifying targets of RNA degradation factors. © 2012 Landes Bioscience.

DOI： 10.4161/rna.22360

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記述言語：英語   掲載種別：研究論文（学術雑誌）

We developed linear regression models which predict strength of transcriptional activity of promoters from their sequences. Intrinsic transcriptional strength data of 451 human promoter sequences in three cell lines (HEK293, MCF7 and 3T3), which were measured by systematic luciferase reporter gene assays, were used to build the models. The models sum up contributions of CG dinucleotide content and transcription factor binding sites (TFBSs) to transcriptional strength. We evaluated prediction accuracies of the models by cross validation tests and found that they have adequate ability for predicting transcriptional strength of promoters in spite of their simple formalization. We also evaluated statistical significance of the contributions and proposed a picture of regulatory code hidden in promoter sequences. That is, CG dinucleotide content and TFBSs mainly determine strength of transcriptional activity under ubiquitous and specific environments, respectively.

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記述言語：英語   掲載種別：研究論文（学術雑誌）

We developed a computer program that can predict the intrinsic promoter activities of primary human DNA sequences. We observed promoter activity using a quantitative luciferase assay and generated a prediction model using multiple linear regression. Our program achieved a prediction accuracy correlation coefficient of 0.87 between the predicted and observed promoter activities. We evaluated the prediction accuracy of the program using massive sequencing analysis of transcriptional start sites in vivo. We found that it is still difficult to predict transcript levels in a strictly quantitative manner in vivo; however, it was possible to select active promoters in a given cell from the other silent promoters. Using this program, we analyzed the transcriptional landscape of the entire human genome. We demonstrate that many human genomic regions have potential promoter activity, and the expression of some previously uncharacterized putatively non-protein-coding transcripts can be explained by our prediction model. Furthermore, we found that nucleosomes occasionally formed open chromatin structures with RNA polymerase II recruitment where the program predicted significant promoter activities, although no transcripts were observed. © 2011 The Author(s).

DOI： 10.1093/nar/gkr173

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