Greenbaum et al 13 and secondary structures in the vegetative yeast cell.  Other comparisons included looking at average biomasses, looking into subcellular localizations and a direct comparison of mRNA expression vs. protein abundance. Overall Transcriptome and Translatome Similarity: Outliers Against Trend   The overall similarity we find between transcriptome and translatome contrasts somewhat with the weak correlation between mRNA expression and gene abundance as shown in figure 2 and reported previously (Futcher et al. 1999; Gygi et al. 1999). This reflects the way our system of overall categories collects many proteins into robust averages. It shows that variation between proteins is not systematic with respect to the categories. For example, individual transcription factors might have higher or lower protein abundance than one expects from their mRNA expression, but the category “transcription factors” as a whole has a similar representation in the transcriptome and translatome. We used the reference data sets to compare mRNA expression and protein abundance for the 181 genes shared between the two sets -- the largest such comparison.  While we found an overall correlation between the two data sets, indicating that mRNA expression may be closely related to protein abundance, we found some genes that bucked the trend.  Possible explanations for the aberrant behavior of some of these outliers are presented. Those outliers that have higher levels of protein abundance than expected from their mRNA expression are dominated by alcohol dehydrogenases and Glyceraldehyde-3-phosphate (G3P) dehydrogenases.  It is known that G3P dehyderogenase forms a bienzyme complex with alcohol dehydrogenase, thus, the similar abundance pattern of these two enzymes can be rationalized (Batke et al. 1992). Alcohol dehydrogenase is also a stress induced protein in many organisms (Matton et al. 1990; An et al. 1991; Millar et al. 1994), induced into action when the cell undergoes trauma, thus perhaps translated to a higher degree prophylactically (although the expression pattern of another stress- induced protein (HSP70) shows that this is not always the case). Translation-related proteins are more prominent in the outliers, with lower protein abundance than expected from mRNA expression. While it is known that multiple features of an individual mRNA influence its expression and regulation, it is presently not clearly understood how.  There are many non-coding regions in each mRNA species that are responsible for this regulation.  These include upstream AUG codons (uAUGs), both 3’ and 5’ untranslated regions, upstream open reading frames (uORFs) and the overall secondary structure of mRNA.  Presently it is unclear how these act to exert their control (Morris & Geballe 2000).    One might conceive of using "outliers" with significantly different transcriptional and translational behavior to find consensus regulatory sequences.  One possible method would involve using predicted mRNA structures (Jaeger et al. 1990; Zuker 2000) to find consensus structural elements in these outliers.  In particular, it might be worthwhile to investigate the secondary mRNA structure, to which the yeast translational machinery is known to be sensitive (McCarthy 1998). The regulation of mRNA stability is certainly an additional factor causing strong disparities between gene expression and protein abundance.  Presently, there are many structures within