Computational Analysis For Molecular Regulatory Mechanism Of Disease Variants

With the development of high-throughput sequencing technology,a large amount of biological data has been extracted,but most of the biological data contained in the biological information and biological knowledge is unknown.Bioinformatics is an interdisciplinary field of science,which combines statistics,computer science,mathematics,and engineering to analyze and interpret biological data.The analysis of gene expression has always been one of the hotspots in bioinformatics.Gene expression process can be divided into two stages:transcription and translation.In every stage there are a large number of regulatory elements,proteins and other molecules involved.Any stage of abnormalities may lead to the inactivation of gene function,influence the expression of gene,and eventually lead to the disease.Regulatory elements are widely distributed in genes and involved in many processes of gene expression,so changes in functional activity of regulatory elements play an important role in gene expression.Gene mutations change the sequence distribution of regulatory elements,which leads abnormal gene expression and gene mutations is the important molecular pathogenesis.It has important significance for further study of the disease molecular mechanism to determine the mutation of the different regulatory elements on the gene expression.In order to quantize the effect of mutations of different regulatory elements on gene expression,in this paper,we first analyze the four types of disease-related mutations,and then calculates the distribution of these mutations in different regulatory elements,finally determine disease-associated variants in different categories of disease located in distinct regulatory elements.After the theoretical analysis,we use sequence model mining method the promoter sequence and enhancer sequence,then further analyze the mutation pathogenic mechanism of promoter and enhancer.The main research work and innovation are as follows:(1)Combined with the existing database and various tools,we analyze four different disease-associated mutations and different regulatory regions in detail.We first extract nine different types of regulatory regions data from FANTOM5 and ENCODE projects.Combined with the human genome sequence data,the distribution of genes in different regulatory regions is statistically analyzed.For the four different disease-associated mutations,this paper firstly obtains the relevant mutation data from OMMI,GWAS,ClinVar and other databases,and then use some online tools to analyze the similarities and differences of the four types of mutations,finally the specific distribution of different mutations in different control areas are calculated.(2)By using sequence pattern mining model to study the pathogenic mechanism of mutations of the promoter and enhancer,this paper quantities the degree in which mutation influence the functional activity of promoter and enhancer.Gene sequence data contains abundant biological features.The reason why different gene sequences can perform the same function in the process of gene expression is that they share the same biological characteristics.Combining the differences and conservative characteristics of the sequence,this paper studies the promoter and enhancer uses sequential patterns mining algorithm which is based on the combination of frequent pattern mining and PSSM algorithm to study the promoter and enhancer.Using this method can quantitative the promoter and enhancer.The experimental result show that the model can effectively distinguish between true and false promoter and enhancer.Combined with the above conclusion,this paper further studies the promoter and enhancer mutation and finds that the reduction of promoter signal strength leads to the increasement of pathogenic probability.It shows that the promoter of single nucleotide mutation which reduces the promoter signal strength has positive correlation with disease conformation;this paper also shows that as the reduction of signal strength of enhancer,the probability of disease conformation is not changed greatly.