Literature DB >> 12016056

Analysis of mRNA expression and protein abundance data: an approach for the comparison of the enrichment of features in the cellular population of proteins and transcripts.

Dov Greenbaum1, Ronald Jansen, Mark Gerstein.   

Abstract

MOTIVATION: Protein abundance is related to mRNA expression through many different cellular processes. Up to now, there have been conflicting results on how correlated the levels of these two quantities are. Given that expression and abundance data are significantly more complex and noisy than the underlying genomic sequence information, it is reasonable to simplify and average them in terms of broad proteomic categories and features (e.g. functions or secondary structures), for understanding their relationship. Furthermore, it will be essential to integrate, within a common framework, the results of many varied experiments by different investigators. This will allow one to survey the characteristics of highly expressed genes and proteins.
RESULTS: To this end, we outline a formalism for merging and scaling many different gene expression and protein abundance data sets into a comprehensive reference set, and we develop an approach for analyzing this in terms of broad categories, such as composition, function, structure and localization. As the various experiments are not always done using the same set of genes, sampling bias becomes a central issue, and our formalism is designed to explicitly show this and correct for it. We apply our formalism to the currently available gene expression and protein abundance data for yeast. Overall, we found substantial agreement between gene expression and protein abundance, in terms of the enrichment of structural and functional categories. This agreement, which was considerably greater than the simple correlation between these quantities for individual genes, reflects the way broad categories collect many individual measurements into simple, robust averages. In particular, we found that in comparison to the population of genes in the yeast genome, the cellular populations of transcripts and proteins (weighted by their respective abundances, the transcriptome and what we dub the translatome) were both enriched in: (i) the small amino acids Val, Gly, and Ala; (ii) low molecular weight proteins; (iii) helices and sheets relative to coils; (iv) cytoplasmic proteins relative to nuclear ones; and (v) proteins involved in 'protein synthesis,' 'cell structure,' and 'energy production.' SUPPLEMENTARY INFORMATION: http://genecensus.org/expression/translatome

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Year:  2002        PMID: 12016056     DOI: 10.1093/bioinformatics/18.4.585

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  66 in total

1.  Relating whole-genome expression data with protein-protein interactions.

Authors:  Ronald Jansen; Dov Greenbaum; Mark Gerstein
Journal:  Genome Res       Date:  2002-01       Impact factor: 9.043

2.  Revisiting the codon adaptation index from a whole-genome perspective: analyzing the relationship between gene expression and codon occurrence in yeast using a variety of models.

Authors:  Ronald Jansen; Harmen J Bussemaker; Mark Gerstein
Journal:  Nucleic Acids Res       Date:  2003-04-15       Impact factor: 16.971

3.  GeneCensus: genome comparisons in terms of metabolic pathway activity and protein family sharing.

Authors:  J Lin; J Qian; D Greenbaum; P Bertone; R Das; N Echols; A Senes; B Stenger; M Gerstein
Journal:  Nucleic Acids Res       Date:  2002-10-15       Impact factor: 16.971

4.  TopNet: a tool for comparing biological sub-networks, correlating protein properties with topological statistics.

Authors:  Haiyuan Yu; Xiaowei Zhu; Dov Greenbaum; John Karro; Mark Gerstein
Journal:  Nucleic Acids Res       Date:  2004-01-14       Impact factor: 16.971

5.  ExpressYourself: A modular platform for processing and visualizing microarray data.

Authors:  Nicholas M Luscombe; Thomas E Royce; Paul Bertone; Nathaniel Echols; Christine E Horak; Joseph T Chang; Michael Snyder; Mark Gerstein
Journal:  Nucleic Acids Res       Date:  2003-07-01       Impact factor: 16.971

6.  Assessing the limits of genomic data integration for predicting protein networks.

Authors:  Long J Lu; Yu Xia; Alberto Paccanaro; Haiyuan Yu; Mark Gerstein
Journal:  Genome Res       Date:  2005-07       Impact factor: 9.043

7.  Low contents of carbon and nitrogen in highly abundant proteins: evidence of selection for the economy of atomic composition.

Authors:  Ning Li; Jie Lv; Deng-Ke Niu
Journal:  J Mol Evol       Date:  2009-02-10       Impact factor: 2.395

8.  Regulatory changes contribute to the adaptive enhancement of thermogenic capacity in high-altitude deer mice.

Authors:  Zachary A Cheviron; Gwendolyn C Bachman; Alex D Connaty; Grant B McClelland; Jay F Storz
Journal:  Proc Natl Acad Sci U S A       Date:  2012-05-14       Impact factor: 11.205

9.  Screening key genes and pathways in glioma based on gene set enrichment analysis and meta-analysis.

Authors:  Yanyan Tang; Wenwu He; Yunfei Wei; Zhanli Qu; Jinming Zeng; Chao Qin
Journal:  J Mol Neurosci       Date:  2013-03-15       Impact factor: 3.444

10.  Integrative analysis of transcriptomic and proteomic data of Desulfovibrio vulgaris: a non-linear model to predict abundance of undetected proteins.

Authors:  Wandaliz Torres-García; Weiwen Zhang; George C Runger; Roger H Johnson; Deirdre R Meldrum
Journal:  Bioinformatics       Date:  2009-05-15       Impact factor: 6.937

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