Study On Motif Prediction Of Monocotyledonous Promoters Based On Phylogenetic Footprinting Technology

As a new interdisciplinary subject,bioinformatics integrates multidisciplinary knowledge such as biology,computer science and mathematical science to study the biological mysteries contained in biological data.The sequence analysis of nucleic acid and protein is the focus of bioinformatics,and the prediction of DNA sequence motif in sequence analysis is always an important problem in bioinformatics research,especially the motif prediction of transcription factor binding sites.This is due to the rapid development of a next-generation of sequencing technology and its low cost and high efficiency,allowing more genomes to be sequenced,but the researchers did not fully grasp the genome function of sequenced organisms.As a result,researchers focused on studying the functional sequence elements of these genomes,and the cis-regulatory elements(also called motifs)identified by transcriptional factors are precisely the functional elements that play key roles in gene expression.Therefore,the motif discovery of transcription factor binding sites is a hot issue in bioinformatics.With the development of a new generation of sequencing technologies,a large number of species are sequenced completely,providing the possibility of using comparative genomics.Since genome-wide sequencing and functional annotations of the monocots such as Panicum virgatum,Panicum hallii,Setaria italica and Brachypodium stacei have been completed in recent years,the complete genome of Oryza sativa,Sorghum bicolor,Zea mays,Setaria viridis,Brachypodium distachyo has been sequenced,and the evolutionary distances of these species are close to each other.Therefore,we selected the whole genome sequence of these plants as the research data to study the biological characteristics of monocotyledons.The development of a next-generation of sequencing technology has led to the complete sequencing of a large number of species,which provides the possibility of using comparative genomics.In studying plants,due to the lack of reference genomes,this technique is currently applied only to the regulation of some types of transcription factors.In order to improve the accuracy of phantom prediction and utilize the complementarity of the algorithms,a new research idea and method is proposed in this paper,which uses the evolutionary footprint technology integrated with multiple phantom discovery algorithms and the latest cluster-only clustering Algorithms combine to perform genome-wide promoter predictions.The successful prediction of a large number of promoter motifs,and the application of this method to improve the accuracy of motif prediction.In order to facilitate the use of our algorithms and tools by biologists,we have constructed the above-mentioned series of research methods into the model predictive analysis toolkit P3 CRE,and the corresponding software can be downloaded free of charge through https://github.com/shaoqiangzhang.