Discovering Regulatory Overlapping RNA Transcripts

Danford, Timothy; Dowell, Robin D.; Agarwala, Sudeep D.; Grisafi, Paula; Fink, Gerald R.; Gifford, David K.

doi:10.1089/cmb.2010.0267

Cited by 2 publications

(4 citation statements)

References 24 publications

(25 reference statements)

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“…A visual examination of the read mappings relative to available tiled expression data (Danford et al 2010) indicates that reads are strand specific and show perfect correspondence with expressed segments, indicating the background of possible RNA binding sites is the transcriptome, not the genome. A weak correlation is observed between the expression levels of a transcript and the number of observed reads.…”

Section: Methodsmentioning

confidence: 99%

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Feed-Forward Regulation of a Cell Fate Determinant by an RNA-Binding Protein Generates Asymmetry in Yeast

et al. 2010

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Saccharomyces cerevisiae can divide asymmetrically so that the mother and daughter cells have different fates. We show that the RNA-binding protein Khd1 regulates asymmetric expression of FLO11 to determine daughter cell fate during filamentous growth. Khd1 represses transcription of FLO11 indirectly through its regulation of ASH1 mRNA. Khd1 also represses FLO11 through a post-transcriptional mechanism independent of ASH1. Cross-linking immunoprecipitation (CLIP) coupled with high-throughput sequencing shows that Khd1 directly binds repetitive sequences in FLO11 mRNA. Khd1 inhibits translation through this interaction, establishing feed-forward repression of FLO11. This regulation enables changes in FLO11 expression between mother and daughter cells, which establishes the asymmetry required for the developmental transition between yeast form and filamentous growth.

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Section: Methodsmentioning

confidence: 99%

“…The peaks are weighted by the corresponding expression level of each transcript, as determined from tiled expression data (Danford et al 2010). Only peaks containing at least 50% of the reads of the transcript's maximal peak size are considered.…”

Section: Methodsmentioning

confidence: 99%

Feed-Forward Regulation of a Cell Fate Determinant by an RNA-Binding Protein Generates Asymmetry in Yeast

et al. 2010

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“…We note, furthermore, that the recursion (8) is the same as for segmentation problems in general [28]. It appears, e.g., in Reference [29] for financial time series, in Reference [30] in context of text segmentation, in Reference [31] for the analysis of array CGH data, and in Reference [5,7,32] for the identification of transcripts in tiling array and RNA-seq data. It is discussed in the setting of very general similarity measures in Reference [11].…”

Section: Dynamic Programming Algorithmmentioning

confidence: 97%

“…The identification of transcriptional units is also a segmentation problem, consisting in the distinction of expressed and non-expressed loci [3,4] or operons [5] or, more generally, in the distinction of adjacent or even overlapping transcripts without the benefit of non-expressed spacers between them. The latter task is particularly relevant in organisms with "compact" genomes, such as bacteria [5] or yeast [6,7], where transcribed loci are rarely separated non-expressed regions. Boundaries between transcriptional units are detectable by differences in RNA levels [6], see, e.g., the SRG1 ncRNA in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

Weighted Consensus Segmentations

et al. 2021

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The problem of segmenting linearly ordered data is frequently encountered in time-series analysis, computational biology, and natural language processing. Segmentations obtained independently from replicate data sets or from the same data with different methods or parameter settings pose the problem of computing an aggregate or consensus segmentation. This Segmentation Aggregation problem amounts to finding a segmentation that minimizes the sum of distances to the input segmentations. It is again a segmentation problem and can be solved by dynamic programming. The aim of this contribution is (1) to gain a better mathematical understanding of the Segmentation Aggregation problem and its solutions and (2) to demonstrate that consensus segmentations have useful applications. Extending previously known results we show that for a large class of distance functions only breakpoints present in at least one input segmentation appear in the consensus segmentation. Furthermore, we derive a bound on the size of consensus segments. As show-case applications, we investigate a yeast transcriptome and show that consensus segments provide a robust means of identifying transcriptomic units. This approach is particularly suited for dense transcriptomes with polycistronic transcripts, operons, or a lack of separation between transcripts. As a second application, we demonstrate that consensus segmentations can be used to robustly identify growth regimes from sets of replicate growth curves.

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Discovering Regulatory Overlapping RNA Transcripts

Cited by 2 publications

References 24 publications

Feed-Forward Regulation of a Cell Fate Determinant by an RNA-Binding Protein Generates Asymmetry in Yeast

Feed-Forward Regulation of a Cell Fate Determinant by an RNA-Binding Protein Generates Asymmetry in Yeast

Weighted Consensus Segmentations

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