Biochemical and Biophysical Research Communications
Correlation between mRNA and protein abundance in Desulfovibrio vulgaris: A multiple regression to identify sources of variations
Section snippets
Materials and methods
Microarray analysis. Oligonucleotide microarrays containing 3507 ORFs of the D. vulgaris genome were designed by NimbleGen Systems (Madison, WI) [13], [16]. The raw intensity data were normalized using tools available through the Bioconductor project (http://www.bioconductor.org). For each experimental condition, there were four measurements for each gene: two replicates (each containing a pool of three biological replicates) that were each hybridized to duplicate microarrays [14]. Four
Quality and distribution pattern analysis of mRNA and protein abundance data
Four measurements of mRNA levels were obtained for all genes in the D. vulgaris genome under three different growth conditions. The quality of the microarray data was evaluated with Pearson’s correlation coefficient analysis among four measurements. Pearson’s correlation coefficients indicated excellent reproducibility of the microarray experiments, with variations from 0.96 to 0.99 for samples collected from lactate-based medium at exponential phase (LE), from 0.97 to 0.99 for samples
Discussion
High-throughput experimentation measuring mRNA and protein expression provides rich sources of genomic information [30]. Integrative analysis of these two data types could provide additional insights into the metabolic mechanisms underlying complex biological systems. However, a challenging question facing the integrative analysis of these large-scale datasets is to identify the correlation patterns between mRNA and protein abundance, and various factors affecting the relationship. Moreover,
Acknowledgments
The research described in this paper was conducted under the Laboratory Directed Research and Development, LDRD Program at the Pacific Northwest National Laboratory, a multi-program national laboratory operated by Battelle for the U.S. Department of Energy under Contract DE-AC056-76RLO1830. This study is also partially supported by the Award 0317349 from the National Science Foundation’s Division of Biological Infrastructure, Program in Biological Databases and Informatics.
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