Transcriptome Site Distribution Estimation And Sequential MeRIP-Seq Temporal Transcription Modules Mining

m~6A methylation is the most common form of modification in RNA transcription,and it is currently a research hotspot in biology and bioinformatics.MeRIP-Seq is a Qualcomm sequencing technology combined with immunoprecipitation,which can accurately and effectively detect m~6A methylation sites,thereby providing strong data support for the study of m~6A methylation-related biological characteristics.However,in recent years,the lack of bioinformatics tools has been restricting the exploration of m~6A methylation related fields.To this end,this article has carried out research on the visualization of transcriptome-level site distribution and the exploration of the MeRIP-Seq data timing synergy module.First,this paper proposes a method for estimating the distribution of transcriptome sites to facilitate the analysis of the distribution of RNA methylation sites on the transcriptome.In order to overcome the structural inconsistency of different transcript regions,this paper uses the genome annotation database to construct the standard transcript coordinate system of the transcriptome complete set and the m RNA and nc RNA transcriptome subsets,and the set of sites to be observed is nonlinearized to the standard transcript Mapping to estimate the distribution characteristics and confidence of the sites on the transcriptome.At present,this method has been packaged into fully functional open source software and submitted to the R/Bioconductor platform,providing an effective visualization method for scientific research in related fields.Secondly,this paper designs a time series MeRIP-Seq data clustering algorithm to mine the time series collaboration modules in the data.The algorithm uses the constraint framework of the time window to extract the timing features in the methylation profile,and uses the ISA iterative idea to identify the time-dependent regulatory modules.By assigning credibility weights to site read data,noise caused by low methylation expression is suppressed,and the stability of clustering is enhanced.Through evaluation,this method can mine time-series coordination modules more accurately and effectively,and its calculation results are also bio-interpretable.This thesis has 40 figures,6 tables and 99 references.