Sentence examples for between sequence and expression from inspiring English sources

Generally, there is a moderate, but nevertheless significant, positive correlation between sequence and expression profile similarities.

In addition, the results indicate the presence of a correlation between sequence and expression conservation within the Triticeae.

Interestingly, the correlation between sequence and expression similarity is highest for the six 8q24 KRAB-ZNF genes.

Collectively, these observations reveal the complexity of the dependencies between sequence and expression divergence, and further imply that different expression similarity measures capture distinct aspects of the functions of orthologous and paralogous genes.

First, we developed a scalable metric for expression divergence that enabled us to detect a significant correlation between sequence and expression conservation on the global level, which previous smaller-scale expression studies failed to detect.

However, Tirosh and Barkai (2008) observed no correlation in yeast [ 33], and in contrast to Yang and Wang [ 32], Movahedi et al. observed no correlation between sequence and expression evolution in Arabidopsis and rice [ 34].

This correlation between protein sequence and expression similarity is consistent with the majority of results in mammalian systems [ 45- 47], Xenopus[ 48] and Drosophila[ 49, 50].

Being a part of such modules would require a coordinated evolution between their sequence and expression level to participate in a collective cell function.

A trend that we seem to observe is that the correlation between sequence evolution and expression context evolution reflects the predictive span of the expression data.

For example, duplicate genes, which are usually associated with highly consistent coding sequences but diverse biological functions, have only a weak correlation between rates of sequence and expression divergences [1].

Elemento et al. [ 9, 10] propose an algorithm called Finding Informative Regulatory Elements (FIRE) that measures the mutual information between sequences and gene expression profiles, which is used to select the most statistically significant motifs.