Classifying transcription factor targets and discovering relevant biological features

Dustin T Holloway,Charles Delisi,Mark Kon

doi:10.1186/1745-6150-3-22

Dustin T Holloway, Charles Delisi + Show 1 more

Open Access

https://doi.org/10.1186/1745-6150-3-22

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Journal: Biology Direct	Publication Date: May 30, 2008
Citations: 70	License type: cc-by

Affiliation: Boston University

Abstract

BackgroundAn important goal in post-genomic research is discovering the network of interactions between transcription factors (TFs) and the genes they regulate. We have previously reported the development of a supervised-learning approach to TF target identification, and used it to predict targets of 104 transcription factors in yeast. We now include a new sequence conservation measure, expand our predictions to include 59 new TFs, introduce a web-server, and implement an improved ranking method to reveal the biological features contributing to regulation. The classifiers combine 8 genomic datasets covering a broad range of measurements including sequence conservation, sequence overrepresentation, gene expression, and DNA structural properties.Principal Findings(1) Application of the method yields an amplification of information about yeast regulators. The ratio of total targets to previously known targets is greater than 2 for 11 TFs, with several having larger gains: Ash1(4), Ino2(2.6), Yaf1(2.4), and Yap6(2.4).(2) Many predicted targets for TFs match well with the known biology of their regulators. As a case study we discuss the regulator Swi6, presenting evidence that it may be important in the DNA damage response, and that the previously uncharacterized gene YMR279C plays a role in DNA damage response and perhaps in cell-cycle progression.(3) A procedure based on recursive-feature-elimination is able to uncover from the large initial data sets those features that best distinguish targets for any TF, providing clues relevant to its biology. An analysis of Swi6 suggests a possible role in lipid metabolism, and more specifically in metabolism of ceramide, a bioactive lipid currently being investigated for anti-cancer properties.(4) An analysis of global network properties highlights the transcriptional network hubs; the factors which control the most genes and the genes which are bound by the largest set of regulators. Cell-cycle and growth related regulators dominate the former; genes involved in carbon metabolism and energy generation dominate the latter.ConclusionPostprocessing of regulatory-classifier results can provide high quality predictions, and feature ranking strategies can deliver insight into the regulatory functions of TFs. Predictions are available at an online web-server, including the full transcriptional network, which can be analyzed using VisAnt network analysis suite.ReviewersThis article was reviewed by Igor Jouline, Todd Mockler(nominated by Valerian Dolja), and Sandor Pongor.

Highlights

Many factors influence the regulation of genes and their protein products within the cell
Some work has been published on supervised classification schemes for predicting transcription factors (TFs) binding targets, and we have briefly reviewed a few of these in our previous work [16,17], which focused on developing and applying a support vector machine [18,19] variant to predict transcription factor binding sites in Saccharomyces cerevisiae
Since 50 classifiers are trained for each TF each using a different randomly chosen negative set, the reported accuracy is an average over 50 trials

Summary

Introduction

Many factors influence the regulation of genes and their protein products within the cell. DNA methylation, and histone acetylation/methylation can affect the accessibility of a gene's cis-regulatory sites to trans-acting factors. The primary mode of regulatory control is the association of transcription factors with their target binding sites in DNA. These binding sites occur most often in promoter regions, the stretch of DNA upstream of the transcription start site. An important goal in post-genomic research is discovering the network of interactions between transcription factors (TFs) and the genes they regulate. We include a new sequence conservation measure, expand our predictions to include 59 new TFs, introduce a web-server, and implement an improved ranking method to reveal the biological features contributing to regulation. The classifiers combine 8 genomic datasets covering a broad range of measurements including sequence conservation, sequence overrepresentation, gene expression, and DNA structural properties

Methods

Results

Conclusion