Abstract
Cell cycle is a complex and highly supervised process that must proceed with regulatory precision to achieve successful cellular division. Despite the wide application, microarray time course experiments have several limitations in identifying cell cycle genes. We thus propose a computational model to predict human cell cycle genes based on transcription factor (TF) binding and regulatory motif information in their promoters. We utilize ENCODE ChIP-seq data and motif information as predictors to discriminate cell cycle against non-cell cycle genes. Our results show that both the trans- TF features and the cis- motif features are predictive of cell cycle genes, and a combination of the two types of features can further improve prediction accuracy. We apply our model to a complete list of GENCODE promoters to predict novel cell cycle driving promoters for both protein-coding genes and non-coding RNAs such as lincRNAs. We find that a similar percentage of lincRNAs are cell cycle regulated as protein-coding genes, suggesting the importance of non-coding RNAs in cell cycle division. The model we propose here provides not only a practical tool for identifying novel cell cycle genes with high accuracy, but also new insights on cell cycle regulation by TFs and cis-regulatory elements.
Highlights
As one of the most important cellular processes, the cell division cycle is under precise regulation in all organisms
We propose a computational approach to predict cell cycle genes based on transcription factor (TF) binding data and motif information in their promoters
We take advantage of ChIPseq TF binding data generated by the ENCODE project and the TF binding motif information available from public databases
Summary
As one of the most important cellular processes, the cell division cycle is under precise regulation in all organisms. The genes, the transcription factors (TFs) that modulate cell cycle have been investigated, e.g. identifying their genomic occupation using chromatin immunoprecipitation followed by microarray hybridization (ChIP-chip) or massively parallel sequencing (ChIP-seq) [5,6]. These studies have provided many insights into cell cycle regulation during normal biological processes and in cancers
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
