Prediction Of Stage-specific Gene Clusters In Cell Reprogramming

Cell reprogramming is a process of reprogramming differentiated somatic cells into pluripotency or even totipotency cells under specific induction conditions.Understanding the specific changes of gene expression at different stages of cell reprogramming plays an important role in elucidating the reprogramming mechanism of induced pluripotent stem cells(iPSCs)and improving their induction efficiency.Currently,biological experiments have been successfully induced to differentiate into induced pluripotent stem cells by screening,combining,and over-expressing pluripotent transcription factors.However,there is no research on stage specific gene clusters from the perspective of binding peaks of transcription factors and histone modification.Therefore,based on the binding peak of transcription factors and other factors such as histone modification,this paper established a theoretical prediction model to predict the specific gene cluster in reprogramming stage.The research content of this paper includes three parts:(1)Prediction of stage-specific gene clusters based on transcription factor(TF).(2)Prediction of stage-specific gene clusters based on histone modification(HMs).(3)Prediction of stage-specific gene clusters based on TF combined with HMS.First of all,the Chip-seq data and microarray transcriptome data in this paper are from the Gene Expression Omnibus(GEO)database,and the login numbers are GSE67520 and GSE67462,respectively.The ChIP-seq data contains nine reprogramming time points from mouse fibroblasts into iPSCs.Using differential gene expression,we identified stage-specific gene clusters.Further,the peaks number of TF Oct4,three HMs(H3k4me3,H3k27me3,H3k27ac)and RNA polymerase(RNApol)in the promoters,enhancers and enhancer subdivided regions of these phase-specific gene clusters were counted.Secondly,the theoretical prediction models of TF Oct4?HMS ?Oct4 combined with HMS and stage specific gene clusters were established respectively.Finally,the performance of the classifier is evaluated by precision?recall?f1-score?the area under the receiver operating characteristic curve(Roc area)and the accuracy rate.Our results show that the prediction of stage-specific gene clusters in cell reprogramming can be effectively improved by using multi omics Chip-seq data and deep learning technology.