Context Specific Transcription Factor Prediction

Abstract

Abstract-One of the goals of systems biology is the identification of regulatory mechanisms that govern an organism's response to external stimuli. Transcription factors have been hypothesized as a major contributor to an organism's response to various outside stimuli, and a great deal of work has been done to predict the set of transcription factors which regulate a given gene. Most of the current methods seek to identify possible binding sites from genomic sequence. Initial attempts at predicting transcription factors from genomic sequences suffered from the problem of false positives. Making the problem more difficult, it has also been shown that while predicted binding sites might be false positives, they can be shown to bind to their corresponding sequences in vitro. One method for rectifying this is through the use of phylogenetic analysis in which only regions which show high evolutionary conservation are analyzed. However such an approach may be too stringent because of the level of degeneracy shown in transcription factor binding site position weight matrices. Due to the degeneracy, there may be only a few bases that need to be conserved across species. Therefore, while a sequence may not show a high level of evolutionary conservation, these sequences may still show high affinity for the same transcription factor. In predicting transcription factor binding we explore the notion that ''Coexpression implies co-regulation'' [Allocco et al. BMC Bioinformatics 5:18, 2004]. With multiple genes requiring similar transcription factors binding sites, there exists a basis for eliminating false positives. This method allows for the selection of transcription factors binding sites that are active under a given experimental paradigm, thereby allowing us to indirectly incorporate the effects of chromosome and recognition site presentation upon transcription factor binding prediction. Rather than having to rationalize that a few transcription factors binding sites are over-represented in a cluster of genes, one can show that a few transcription factors are active in the cluster of genes that have been grouped together. Although the method focuses on predicting experiment-specific transcription factor binding sites, it is possible that if such a methodology were used in an iterative process where different experiments were analyzed, one could obtain a comprehensive set of transcription factors binding sites which regulate the various dynamic responses shown by biological systems under a variety of conditions hence building a more comprehensive model of transcriptional regulation.

Gene Regulatory Network Inference of Immunoresponsive Gene 1 (IRG1) Identifies Interferon Regulatory Factor 1 (IRF1) as Its Transcriptional Regulator in Mammalian Macrophages

“…For this, we designed an integrative approach of computational and experimental methods to identify the transcriptional regulators of IRG1 in mammalian macrophages, where the notion “co-expression implies co-regulation” is satisfied [ 37 ]. Transcription factors play a pivotal role in modulating gene expression and thus contribute to the overall regulation of biological processes.…”

Understanding Physiology in the Continuum: Integration of Information from Multiple -Omics Levels

“…(Note: in the analysis that follows, we assume that the base line expression levels, i.e., in the absence of the drug, are represented by mRNA and protein abundance levels prior to drug administration). In a number of previous publications, we have illustrated the analysis of longitudinal data with a time-varying base line ( Androulakis et al, 2007 ; Yang et al, 2007b , 2008a , 2009b , 2012a , b ; Yang E.H. et al, 2009 ; Almon et al, 2008a , b ; Nguyen et al, 2009 , 2010a , b , 2011 , 2014a ; Ovacik et al, 2010 ; Scheff et al, 2010a ; Swiss et al, 2011 ). However, temporal relations among time-varying quantities are known to be non-trivial, extending far beyond the classic view of correlation ( Qian et al, 2001 ).…”

“…The diversity of the available models describing gene induction by MPL have been expanded to several pharmacogenomics models that explain the response of numerous genes with various dynamic patterns ( Almon et al, 2003 , 2005 , 2007a , b ; Jin et al, 2003 ; Nguyen et al, 2014a ). Our earlier studies in collaboration with Prof. Jusko, characterized global dynamics of the systems that are regulated by CS at the transcriptional level across multiple tissues in adrenalectomized (ADX) and intact male rats, following single and chronic dosing of MPL enabling us to: (a) develop transcription-level understanding of MPL (acute vs. chronic) effects; (b) elaborate on tissue-specific transcriptional differences; (c) assess MPL-induced dose- and tissue-specific transcriptional regulation; and (d) assess circadian dynamics and regulation of intact and MPL-dosed animals ( Yang et al, 2007b , 2008a ; Yang E.H. et al, 2009 ; Almon et al, 2008a ; Nguyen et al, 2010a , b , 2014a ; Ovacik et al, 2010 ; Scheff et al, 2010a , 2011 ).…”

“…Once the sub-set of genes and proteins of interest has been identified, we then need to establish a network structure expressing putative regulatory relations. A variety of computational methodologies for further refining regulatory network structures are discussed elsewhere ( Yang et al, 2007b ; Foteinou et al, 2009d ; Nguyen and Androulakis, 2009 ; Nguyen et al, 2011 ; Saharidis et al, 2011 ).…”

A computational approach for genome-wide mapping of splicing factor binding sites

“…Clustering of evolutionarily conserved cis -regulatory elements has been previously demonstrated for transcription factors binding sites. Recent transcription factors binding site prediction tools have demonstrated that consideration of neighboring effects dramatically improves prediction performance compared to strategies that consider only a single site [ 22 - 25 ].…”