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. 2010;11(3):R24.
doi: 10.1186/gb-2010-11-3-r24. Epub 2010 Mar 1.

High-resolution transcription atlas of the mitotic cell cycle in budding yeast

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High-resolution transcription atlas of the mitotic cell cycle in budding yeast

Marina V Granovskaia et al. Genome Biol. 2010.

Abstract

Background: Extensive transcription of non-coding RNAs has been detected in eukaryotic genomes and is thought to constitute an additional layer in the regulation of gene expression. Despite this role, their transcription through the cell cycle has not been studied; genome-wide approaches have only focused on protein-coding genes. To explore the complex transcriptome architecture underlying the budding yeast cell cycle, we used 8 bp tiling arrays to generate a 5 minute-resolution, strand-specific expression atlas of the whole genome.

Results: We discovered 523 antisense transcripts, of which 80 cycle or are located opposite periodically expressed mRNAs, 135 unannotated intergenic non-coding RNAs, of which 11 cycle, and 109 cell-cycle-regulated protein-coding genes that had not previously been shown to cycle. We detected periodic expression coupling of sense and antisense transcript pairs, including antisense transcripts opposite of key cell-cycle regulators, like FAR1 and TAF2.

Conclusions: Our dataset presents the most comprehensive resource to date on gene expression during the budding yeast cell cycle. It reveals periodic expression of both protein-coding and non-coding RNA and profiles the expression of non-annotated RNAs throughout the cell cycle for the first time. This data enables hypothesis-driven mechanistic studies concerning the functions of non-coding RNAs.

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Figures

Figure 1
Figure 1
Gene expression profiles ordered by expression peak times. CDC28 and alpha-factor panels show the expression profiles for all identified cell cycle-regulated genes, including 598 protein-coding genes, 37 unannotated antisense transcripts and 11 intergenic transcripts, ordered by their peak times. Profiles for annotated ORFs are graded in blue; all non-coding RNAs are graded in red. Each column of the two time-course panels represents a single experimental 5-minute time-point. The scales on the left display the relative duration and number of transcripts expressed in each phase. Key cell-cycle-regulated genes are indicated on the right side. In each row, white and dark blue (or dark red for the non-coding RNAs) represent the minimum and maximum expression levels, respectively, of the corresponding transcript. Intermediate values are shown by colors that scale linearly over the range.
Figure 2
Figure 2
Gene expression profiles for all identified cell-cycle-regulated ncRNAs ordered by their expression peak times. Each column of the CDC28 and alpha-factor time-course panels represents a single experimental 5-minute time-point. The scales on the left display the relative duration and number of transcripts expressed in each phase. In each row, white and dark red represent the minimum and maximum expression levels, respectively, of the corresponding transcript. Intermediate values are shown by colors that scale linearly over the range.
Figure 3
Figure 3
Expression for sense and antisense transcripts. Heatmaps of expression for sense and antisense transcripts of (a) FAR1, (b) TAF2, (c) CTF4, (d) SPS100 and (e) YLR050C. Each horizontal line represents a single experimental time-point. The unit of the time axis (vertical) is minutes. The horizontal axis in the center of each panel represents genomic coordinates, and annotated coding genes are indicated by blue boxes. The heatmap in the upper half of each panel represents signal on the Watson strand, the one in the lower half signal on the Crick strand. The horizontal orange lines separate alpha-factor (above the line) and Cdc28 (below the line) experimental datasets. Vertical red lines show the segment boundaries.
Figure 4
Figure 4
Venn diagram displays the overlap of our list of identified cell cycle-regulated protein-coding genes with the lists determined by the previous studies of Gauthier et al. [37], Pramila et al. [29], and Spellman et al. [30]. The overlap shows that we find an additional 223 genes not identified by Gauthier et al., among which 109 are unique to our dataset and were not previously defined by the other studies.

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