Predicting gene expression in T cell differentiation from histone modifications and transcription factor binding affinities by linear mixture models

BMC Bioinformatics

Research

Ivan G Costa¹  Francisco de AT de Carvalho¹  Thais G do Rego¹  Helge G Roider² 

[1] Center of Informatics, Federal University of Pernambuco, Recife, Brazil;Dept. of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Berlin, Germany;

关键词: Mean Square Error;    Linear Regression Model;    Histone Modification;    Regulatory Signal;    Histone Modification;   

DOI  :  10.1186/1471-2105-12-S1-S29

来源: Springer

【 摘 要 】

BackgroundThe differentiation process from stem cells to fully differentiated cell types is controlled by the interplay of chromatin modifications and transcription factor activity. Histone modifications or transcription factors frequently act in a multi-functional manner, with a given DNA motif or histone modification conveying both transcriptional repression and activation depending on its location in the promoter and other regulatory signals surrounding it.ResultsTo account for the possible multi functionality of regulatory signals, we model the observed gene expression patterns by a mixture of linear regression models. We apply the approach to identify the underlying histone modifications and transcription factors guiding gene expression of differentiated CD4+ T cells. The method improves the gene expression prediction in relation to the use of a single linear model, as often used by previous approaches. Moreover, it recovered the known role of the modifications H3K4me3 and H3K27me3 in activating cell specific genes and of some transcription factors related to CD4+ T differentiation.

【 授权许可】

CC BY   
© Costa et al; licensee BioMed Central Ltd. 2011