The stochastic nature of gene expression promotes cell-to-cell differences in protein level, usually referred to as noise
. Recent studies, both experimental and computational, have revealed that such cell-to-cell variability can be both disadvantageous 
, as variations in protein level might negatively affect the precision of signaling and regulation, and advantageous 
, by enabling heterogeneous stress-response programs to environmental changes 
. Expression noise has also been proposed to have an important impact on gene evolution 
. These diverse roles are expected to be accompanied by complex and heterogeneous modes of noise regulation. In addition, feedback loops and other network motifs might be utilized to regulate noise 
or propagate it through regulatory networks 
, adding to the overall complexity.
The sources of variation in gene expression in an isogenic cell population are typically divided into two basic groups: (i) the intrinsic noise attributed to the inherent stochasticity of expression processes, and (ii) the extrinsic noise resulting from variation in cell state related to cell-cycle progression, cell size, subtle environmental differences, and other stochastic events that are external to the system – in this case external to the process of expression of an individual gene 
. Several stochastic processes including transcription, translation, and mRNA and protein degradation can contribute to the intrinsic noise 
. The relative contribution of these components is gene or gene-module specific. Basic factors can be gleaned from correlations between noise level and gene characteristics such as promoter structure, gene function, essentiality, chromatin density, and similar features 
. In the context of the prokaryote B. subtilis
, it has been observed that the predominant source of phenotypic noise strength is translational efficiency 
. It has been proposed that in prokaryotes, low transcription but high translation rates produce protein bursts leading to strong fluctuations in the protein level 
. In contrast, noise in eukaryotic gene expression is assumed to be predominantly influenced by the dynamics of transcription 
, in particular transcription bursts 
. Transcription bursts are not unique to eukaryotes and also have a clear impact on prokaryotic noise 
. Similarly, translation dynamics is expected to have an impact on eukaryotic gene expression. Along this line, Blake et al.
demonstrated experimentally that codon usage can impact noise strength in eukaryotic gene expression and proposed that increased translational efficiency might have a substantial effect when coupled with a noisy transcriptional state 
. Furthermore, a recent analysis of data collected by Bar-Even et al
. showed some tendency for efficiently translated genes to have increased noise 
Single-cell analyses of gene expression in yeast provided an important step towards understanding noise etiology and demonstrated a general trend where expression noise scales with protein abundance 
. This trend suggests that expression of most genes follows roughly a random stochastic process. Importantly, there are some deviations from this basic trend, indicating that gene specific factors might be altering this general behavior. Newman et al
. measured these deviations with the DM measure, defined as the difference of the gene specific noise and the median noise for proteins with the same abundance, as estimated from the trend line for the relation between noise and abundance. We use the term noise differential
to denote such deviation of the noise of an individual gene from the average trend, and thus DM is a measure of noise differential. Studies by Newman et al
have uncovered a highly significant correlation of noise differential with several transcription regulation features, including the presence of a TATA box, but did not reveal such highly significant correlation with codon usage, the hallmark of translation efficiency 
. However, untangling different components of expression noise is highly nontrivial. Intrinsic and extrinsic fluctuations can be separated experimentally by utilizing dual reporter measurements 
, but experimental separation of transcriptional and translational components would additionally require single-cell measurements of both mRNA and protein copy numbers simultaneously 
To complement these studies, we used computational means to investigate the question of to what extent sequence features known or postulated to accompany translation efficiency can also be associated with noise differential. Specifically, we considered codon usage, as measured by the tRNA adaptation index (tAI), and 5′ UTR structure. High tAI is postulated to contribute to efficient translation elongation, while low secondary structure at the 5′ UTR has been shown to negatively correlate with ribosomal density 
. Thus, these two features may potentially correlate with amplification of the strength of transcription noise and noise differential.
We observed that ribosomal proteins display a different relationship between expression noise and codon usage as compared to other proteins. Focusing on nonribosomal proteins, we found that the above-mentioned features indeed have significant associations with noise differential. Among these features, the statistical significance of the association with tRNA adaptation index is the highest. We then used a theoretical noise expression model to decompose the protein abundance associated noise strength into two components: noise associated with transcription (represented by the presence of a TATA box) and noise putatively associated with translation (represented by high tAI), while controlling for the protein abundance. Strikingly, we found that the amplification of noise strength associated with high tRNA adaptation index is comparable to the amplification of noise strength associated with the presence of a TATA box. The noise factoring strategy that we introduced here for the purpose of uncovering relative interplay between these two factors is general and can be readily applied to tease apart other contributions of interest.