{'fraction_non_alphanumeric': 0.01698841698841699, 'fraction_numerical': 0.013899613899613899, 'mean_word_length': 5.447761194029851, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Integrative analysis using proteome and transcriptome data from yeast to unravel regulatory patterns at post‐transcriptional level

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. Biotechnol. Bioeng. 2010;107: 865–875. © 2010 Wiley Periodicals, Inc.