title: Integrative analysis using proteome and transcriptome data from yeast to unravel regulatory patterns at post-transcriptional level.
abstract: Exist several studies on the correlation between proteome and transcriptome and these studies have shown that generally there is only a weak positive correlation between these two omes, which means that post-transcriptional events play an important role in determining the protein levels in the cell. In this study we combined proteome and transcriptome data from six different published dataset to identify patterns that can provide new insight into the reasons for these deviations. By using a categorization method and integrating genome-scale information we found that the relation between protein and mRNA is related to the gene function. We could further identify that for genes belonging to amino acid biosynthetic pathways there is no translational regulation, meaning that there is generally a good correlation between mRNA and protein levels. We also found that there is generally translational control for large proteins and there also evidence for a role of conserved motifs in the 3' untranslated regions in the mRNA-protein correlation, probably by controlling the level of mRNA.
authors.affiliation: Systems Biology, Department of Chemical and Biological Engineering, Chalmers University of Technology, Kemivâgen 10, Gothenburg SE-41296, Sweden.
authors.forename: Roberto
authors.initials: R
authors.lastname: Olivares-Hernández
authors.forename: Renata
authors.initials: R
authors.lastname: Usaite
authors.forename: Jens
authors.initials: J
authors.lastname: Nielsen
mesh_terms: D020869:Gene Expression Profiling; D015966:Gene Expression Regulation, Fungal; D020543:Proteome; D012441:Saccharomyces cerevisiae / Q000502:physiology*; D029701:Saccharomyces cerevisiae Proteins / Q000096:biosynthesis* / Q000235:genetics*
chemical_list: D020543:Proteome; D029701:Saccharomyces cerevisiae Proteins