Many genes are regulated as an innate part of the eukaryotic cell cycle, and a complex transcriptional network helps enable the cyclic behavior of dividing cells. This transcriptional network has been studied in Saccharomyces cerevisiae (budding yeast) and elsewhere. To provide more perspective on these regulatory mechanisms, we have used microarrays to measure gene expression through the cell cycle of Schizosaccharomyces pombe (fission yeast). The 750 genes with the most significant oscillations were identified and analyzed. There were two broad waves of cell cycle transcription, one in early/mid G2 phase, and the other near the G2/M transition. The early/mid G2 wave included many genes involved in ribosome biogenesis, possibly explaining the cell cycle oscillation in protein synthesis in S. pombe. The G2/M wave included at least three distinctly regulated clusters of genes: one large cluster including mitosis, mitotic exit, and cell separation functions, one small cluster dedicated to DNA replication, and another small cluster dedicated to cytokinesis and division. S. pombe cell cycle genes have relatively long, complex promoters containing groups of multiple DNA sequence motifs, often of two, three, or more different kinds. Many of the genes, transcription factors, and regulatory mechanisms are conserved between S. pombe and S. cerevisiae. Finally, we found preliminary evidence for a nearly genome-wide oscillation in gene expression: 2,000 or more genes undergo slight oscillations in expression as a function of the cell cycle, although whether this is adaptive, or incidental to other events in the cell, such as chromatin condensation, we do not know.

Most amino acids can be encoded by several synonymous codons, which are used at unequal frequencies. The significance of unequal codon usage remains unclear. One hypothesis is that frequent codons are translated relatively rapidly. However, there is little direct, in vivo, evidence regarding codon-specific translation rates. In this study, we generate high-coverage data using ribosome profiling in yeast, analyze using a novel algorithm, and deduce events at the A- and P-sites of the ribosome. Different codons are decoded at different rates in the A-site. In general, frequent codons are decoded more quickly than rare codons, and AT-rich codons are decoded more quickly than GC-rich codons. At the P-site, proline is slow in forming peptide bonds. We also apply our algorithm to short footprints from a different conformation of the ribosome and find strong amino acid-specific (not codon-specific) effects that may reflect interactions with the exit tunnel of the ribosome.DOI: http://dx.doi.org/10.7554/eLife.03735.001

We present a novel method (ESP) to address the two requirements in an interdependent way. It generalizes the truncated product method of Zaykin et al. to combine only those significance values which clear their respective experiment-specific false discovery restrictive thresholds, thus allowing us to control the false discovery rate (FDR) for the final combined result. Further, we introduce several concepts that together offer FDR control, high power, quality control and speed-up in meta-analysis as done by our algorithm. Computational and statistical methods of research synthesis like the one described here will be increasingly important as additional genome-wide datasets accumulate in databases. We apply our method to combine three well-known ChIP-chip transcription factor binding datasets for budding yeast to identify significant intergenic regulatory sequences for nine cell cycle regulating transcription factors, both with high power and controlled FDR.

We examined 20,648 prokaryotic unique taxids with respect to the annotation of the 3’ end of the 16S rRNA, which contains the anti-Shine-Dalgarno sequence. We used the sequence of highly conserved helix 45 of the 16S rRNA as a guide. By this criterion, 8,153 annotated 3’ ends correctly included the anti-Shine-Dalgarno sequence, but 12,495 were foreshortened or otherwise mis-annotated, missing part or all of the anti-Shine-Dalgarno sequence, which immediately follows helix 45. We re-annotated, giving a total of 20,648 16S rRNA 3’ ends. The vast majority indeed contained a consensus anti-Shine-Dalgarno sequence, embedded in a highly conserved 13 base “tail”. However, 128 exceptional organisms had either a variant anti-Shine-Dalgarno, or no recognizable anti-Shine-Dalgarno, in their 16S rRNA(s). For organisms both with and without an anti-Shine-Dalgarno, we identified the Shine-Dalgarno motifs actually enriched in front of each organism’s open reading frames. This showed to what extent the Shine-Dalgarno motifs correlated with anti-Shine Dalgarno motifs. In general, organisms whose rRNAs lacked a perfect anti-Shine-Dalgarno motif also lacked a recognizable Shine-Dalgarno. For organisms whose 16S rRNAs contained a perfect anti-Shine-Dalgarno motif, a variety of results were obtained. We found one genus, Alteromonas, where several taxids apparently maintain two different types of 16S rRNA genes, with different, but conserved, antiSDs. The fact that some organisms do not seem to have or use Shine-Dalgarno motifs supports the idea that prokaryotes have other robust mechanisms for recognizing start codons for translation.

The Shine-Dalgarno motif occurs in front of prokaryotic start codons, and is complementary to the 3’ end of the 16S ribosomal RNA. Hybridization between the Shine-Dalgarno sequence and the anti-Shine-Dalgarno region of the16S rRNA () directs the ribosome to the start AUG of the mRNA for translation. Shine-Dalgarno-like motifs ( in E. coli) are depleted from open reading frames of most prokaryotes. This may be because hybridization of the 16S rRNA at Shine-Dalgarnos inside genes would slow translation or induce internal initiation. However, we analyzed 128 species from diverse phyla where the 16S rRNA gene(s) lack the anti-Shine-Dalgarno sequence, and so the 16S rRNA is incapable of interacting with Shine-Dalgarno-like sequences. Despite this lack of an anti-Shine-Dalgarno, half of these species still displayed depletion of Shine-Dalgarno-like sequences when analyzed by previous methods. Depletion of the same G-rich sequences was seen by these methods even in eukaryotes, which do not use the Shine-Dalgarno mechanism. We suggest previous methods are partly detecting a non-specific depletion of G-rich sequences. Alternative informatics approaches show that most prokaryotes have only slight, if any, specific depletion of Shine-Dalgarno-like sequences from open reading frames. Together with recent evidence that ribosomes do not pause at ORF-internal Shine-Dalgarno motifs, these results suggest the presence of ORF-internal Shine-Dalgarno-like motifs may be inconsequential, perhaps because internal regions of prokaryotic mRNAs may be structurally “shielded” from translation initiation.