title: Integrated Analysis of Transcriptomic and Proteomic Datasets Reveals Information on Protein Expressivity and Factors Affecting Translational Efficiency.
abstract: Integrated analysis of large-scale transcriptomic and proteomic data can provide important insights into the metabolic mechanisms underlying complex biological systems. In this chapter, we present methods to address two aspects of issues related to integrated transcriptomic and proteomic analysis. First, due to the fact that proteomic datasets are often incomplete, and integrated analysis of partial proteomic data may introduce significant bias. To address these issues, we describe a zero-inflated Poisson (ZIP)-based model to uncover the complicated relationships between protein abundances and mRNA expression levels, and then apply them to predict protein abundance for the proteins not experimentally detected. The ZIP model takes into consideration the undetected proteins by assuming that there is a probability mass at zero representing expressed proteins that were undetected owing to technical limitations. The model validity is demonstrated using biological information of operons, regulons, and pathways. Second, weak correlation between transcriptomic and proteomic datasets is often due to biological factors affecting translational processes. To quantify the effects of these factors, we describe a multiple regression-based statistical framework to quantitatively examine the effects of various translational efficiency-related sequence features on mRNA-protein correlation. Using the datasets from sulfate-reducing bacteria Desulfovibrio vulgaris, the analysis shows that translation-related sequence features can contribute up to 15.2-26.2% of the total variation of the correlation between transcriptomic and proteomic datasets, and also reveals the relative importance of various features in translation process.
authors.affiliation: Laboratory of Synthetic Microbiology, School of Chemical Engineering and Technology, Tianjin University, Tianjin, 300072, People's Republic of China.|Key Laboratory of Systems Bioengineering, Ministry of Education of China, Tianjin, 300072, People's Republic of China.|Collaborative Innovation Center of Chemical Science and Engineering, Tianjin, People's Republic of China.
authors.forename: Jiangxin
authors.initials: J
authors.lastname: Wang
authors.affiliation: University of Maryland at Baltimore Country, Baltimore County, MD, USA.
authors.forename: Gang
authors.initials: G
authors.lastname: Wu
authors.affiliation: Laboratory of Synthetic Microbiology, School of Chemical Engineering and Technology, Tianjin University, Tianjin, 300072, People's Republic of China.|Key Laboratory of Systems Bioengineering, Ministry of Education of China, Tianjin, 300072, People's Republic of China.|Collaborative Innovation Center of Chemical Science and Engineering, Tianjin, People's Republic of China.
authors.forename: Lei
authors.initials: L
authors.lastname: Chen
authors.affiliation: Laboratory of Synthetic Microbiology, School of Chemical Engineering and Technology, Tianjin University, Tianjin, 300072, People's Republic of China. wwzhang8@tju.edu.cn.|Key Laboratory of Systems Bioengineering, Ministry of Education of China, Tianjin, 300072, People's Republic of China. wwzhang8@tju.edu.cn.|Collaborative Innovation Center of Chemical Science and Engineering, Tianjin, People's Republic of China. wwzhang8@tju.edu.cn.
authors.forename: Weiwen
authors.initials: W
authors.lastname: Zhang
mesh_terms: D000596:Amino Acids; D003062:Codon; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation; D015233:Models, Statistical; D014176:Protein Biosynthesis; D040901:Proteomics / Q000379:methods*; D012044:Regression Analysis; D015203:Reproducibility of Results
chemical_list: D000596:Amino Acids; D003062:Codon
keywords: Correlation; Prediction; Proteome; Sequence features; Transcriptome; Translation; Undetected proteins; Zero-inflated Poisson regression