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  • 1
    Publication Date: 2009-01-27
    Description: Genome-wide pervasive transcription has been reported in many eukaryotic organisms, revealing a highly interleaved transcriptome organization that involves hundreds of previously unknown non-coding RNAs. These recently identified transcripts either exist stably in cells (stable unannotated transcripts, SUTs) or are rapidly degraded by the RNA surveillance pathway (cryptic unstable transcripts, CUTs). One characteristic of pervasive transcription is the extensive overlap of SUTs and CUTs with previously annotated features, which prompts questions regarding how these transcripts are generated, and whether they exert function. Single-gene studies have shown that transcription of SUTs and CUTs can be functional, through mechanisms involving the generated RNAs or their generation itself. So far, a complete transcriptome architecture including SUTs and CUTs has not been described in any organism. Knowledge about the position and genome-wide arrangement of these transcripts will be instrumental in understanding their function. Here we provide a comprehensive analysis of these transcripts in the context of multiple conditions, a mutant of the exosome machinery and different strain backgrounds of Saccharomyces cerevisiae. We show that both SUTs and CUTs display distinct patterns of distribution at specific locations. Most of the newly identified transcripts initiate from nucleosome-free regions (NFRs) associated with the promoters of other transcripts (mostly protein-coding genes), or from NFRs at the 3' ends of protein-coding genes. Likewise, about half of all coding transcripts initiate from NFRs associated with promoters of other transcripts. These data change our view of how a genome is transcribed, indicating that bidirectionality is an inherent feature of promoters. Such an arrangement of divergent and overlapping transcripts may provide a mechanism for local spreading of regulatory signals-that is, coupling the transcriptional regulation of neighbouring genes by means of transcriptional interference or histone modification.〈br /〉〈br /〉〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2766638/" target="_blank"〉〈img src="https://static.pubmed.gov/portal/portal3rc.fcgi/4089621/img/3977009" border="0"〉〈/a〉   〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2766638/" target="_blank"〉This paper as free author manuscript - peer-reviewed and accepted for publication〈/a〉〈br /〉〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Xu, Zhenyu -- Wei, Wu -- Gagneur, Julien -- Perocchi, Fabiana -- Clauder-Munster, Sandra -- Camblong, Jurgi -- Guffanti, Elisa -- Stutz, Francoise -- Huber, Wolfgang -- Steinmetz, Lars M -- P01 HG000205/HG/NHGRI NIH HHS/ -- P01 HG000205-19/HG/NHGRI NIH HHS/ -- R01 GM068717/GM/NIGMS NIH HHS/ -- R01 GM068717-06/GM/NIGMS NIH HHS/ -- England -- Nature. 2009 Feb 19;457(7232):1033-7. doi: 10.1038/nature07728. Epub 2009 Jan 25.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉European Molecular Biology Laboratory, Meyerhofstrasse 1, 69117 Heidelberg, Germany.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/19169243" target="_blank"〉PubMed〈/a〉
    Keywords: Gene Expression Profiling ; Gene Expression Regulation, Fungal/*genetics ; Genes, Fungal/genetics ; Genes, Overlapping/genetics ; Genome, Fungal/genetics ; Models, Genetic ; Nucleosomes ; Promoter Regions, Genetic/*genetics ; RNA Stability/genetics ; RNA, Fungal/*genetics ; RNA, Untranslated/genetics ; Saccharomyces cerevisiae/classification/*genetics ; Saccharomyces cerevisiae Proteins/genetics ; Transcription, Genetic/*genetics
    Print ISSN: 0028-0836
    Electronic ISSN: 1476-4687
    Topics: Biology , Chemistry and Pharmacology , Medicine , Natural Sciences in General , Physics
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  • 2
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    In:  CASI
    Publication Date: 2019-07-13
    Description: A long standing mystery in using Maximum Entropy (MaxEnt) is how to deal with constraints whose values are uncertain. This situation arises when constraint values are estimated from data, because of finite sample sizes. One approach to this problem, advocated by E.T. Jaynes [1], is to ignore this uncertainty, and treat the empirically observed values as exact. We refer to this as the classic MaxEnt approach. Classic MaxEnt gives point probabilities (subject to the given constraints), rather than probability densities. We develop an alternative approach that assumes that the uncertain constraint values are represented by a probability density {e.g: a Gaussian), and this uncertainty yields a MaxEnt posterior probability density. That is, the classic MaxEnt point probabilities are regarded as a multidimensional function of the given constraint values, and uncertainty on these values is transmitted through the MaxEnt function to give uncertainty over the MaXEnt probabilities. We illustrate this approach by explicitly calculating the generalized MaxEnt density for a simple but common case, then show how this can be extended numerically to the general case. This paper expands the generalized MaxEnt concept introduced in a previous paper [3].
    Keywords: Statistics and Probability
    Type: 25th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering; Aug 07, 2005 - Aug 12, 2005; San Jose, CA; United States
    Format: application/pdf
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