Investigating the correspondence between transcriptomic and proteomic expression profiles using coupled cluster models

Rogers, S., Girolami, M., Kolch, W., Waters, K. M., Liu, T., Thrall, B. and Wiley, H. S. (2008) Investigating the correspondence between transcriptomic and proteomic expression profiles using coupled cluster models. Bioinformatics, 24(24), pp. 2894-2900. (doi:10.1093/bioinformatics/btn553)

Full text not currently available from Enlighten.

Publisher's URL: http://dx.doi.org/10.1093/bioinformatics/btn553

Abstract

<b>Motivation:</b> Modern transcriptomics and proteomics enable us to survey the expression of RNAs and proteins at large scales. While these data are usually generated and analysed separately, there is an increasing interest in comparing and co-analysing transcriptome and proteome expression data. A major open question is whether transcriptome and proteome expression is linked and how it is coordinated.<p></p> <b>Results:</b> Here we have developed a probabilistic clustering model that permits analysis of the links between transcriptomic and proteomic profiles in a sensible and flexible manner. Our coupled mixture model defines a prior probability distribution over the component to which a protein profile should be assigned conditioned on which component the associated mRNA profile belongs to. We apply this approach to a large dataset of quantitative transcriptomic and proteomic expression data obtained from a human breast epithelial cell line (HMEC). The results reveal a complex relationship between transcriptome and proteome with most mRNA clusters linked to at least two protein clusters, and vice versa. A more detailed analysis incorporating information on gene function from the gene ontology database shows that a high correlation of mRNA and protein expression is limited to the components of some molecular machines, such as the ribosome, cell adhesion complexes and the TCP-1 chaperonin involved in protein folding.<p></p>

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Rogers, Dr Simon and Girolami, Prof Mark
Authors: Rogers, S., Girolami, M., Kolch, W., Waters, K. M., Liu, T., Thrall, B., and Wiley, H. S.
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics
H Social Sciences > HA Statistics
College/School:College of Science and Engineering > School of Computing Science
Research Group:Inference
Journal Name:Bioinformatics
Publisher:Oxford University Press
ISSN:1367-4803
ISSN (Online):1460-2059
Published Online:30 October 2008
Related URLs:

University Staff: Request a correction | Enlighten Editors: Update this record

Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
399341Stochastic modelling and statistical inference of gene regulatory pathways - integrating multiple sources of dataErnst WitEngineering & Physical Sciences Research Council (EPSRC)EP/C010620/1Statistics
396841Probabilistic Reconstruction of Signalling Pathways & Identification of Novel Transcription Factors Employing Heterogeneous Genome-Wide dataMark GirolamiMedical Research Council (MRC)G0401466Computing Science