Regulatory Networks Of LncRNA And MRNA Based On The MiRNA

Bioinformatics is popular interdisciplinary, and its main mechanisms include non-coding RNA, DNA methylation. Common Non-coding RNA includes common small RNA molecule RNA(mi RNA), long non-coding RNA(lnc RNA) and interfering RNA(si RNA). The length of micro RNA is between 21 ~ 25 nt, and the length of lnc RNA is generally between 200 ~ 100000 nt, but lnc RNA can play a regulatory role in the mechanism prior to transcription, transcriptional and post-transcriptional, so it regulats lnc RNA and mi RNA. There is another protein-coding genes, the messenger RNA(m RNA) is translated protein coding genes, and mi RNA can inhibit translation of the m RNA and affect its stability. With the development process of RNA coding and non-coding RNA, they influence eath other, restrict eath other. Therefore, people began to study the complex relationship between gene regulation, and then analyze the biological function of genes.In the past, people only study the functional role of individual genes, and gradually discovered that the organism is a complex system, there may be the mutual regulation relations between the various genes, thus forming a complex regulatory network. Thus, the mutual regulation between mi RNA and m RNA, mi RNA and lnc RNA become one of the hot bioinformatics research in recent years. With the development of regulatory networks Research, Lnc RNA can be a competitive endogenous RNA(Competing Endogenous RNA, ce RNA), and is involved in the regulation of mi RNA targets. Meanwhile, mi RNA can target the m RNA 3’UTR, inhibiting translation of the m RNA or degradation m RNA. In short, in the process of forecast the close regulation relationship between lnc RNA, mi RNA and m RNA exists a close relationship in the process of regulation. A variety of target gene prediction software is constantly updated, and propose a new encoding scheme based on the traditional target gene prediction software, the mechanism not only improve the time algorithm, at the same time on the target gene matching rules and lnc RNA length issues is also improved, at the same time also in favor of studying the complex regulatory relationships among the three.This article is to establish a regulation network of lnc RNA and m RNA based on mi RNA. First, collecte the human genome data of mi RNA, m RNA and lnc RNA. Using DIANA-micro T 4.0 target gene prediction software to predict and select the pecific target gene m RNA of the mi RNA, and make use of the rule of target gene select to secondary screening for target gene predictions of m RNA and mi RNA, Screening effectiveness, reliable target gene.Secondly, project the targets of lnc RNA and mi RNA base on put forward coding mechanism algorithm, and use DIANA-Lnc Base software to analysis the mutual regulate relationship between lnc RNA and m RNA. Finally, analysis the mutual regulate relationship of between m RNA and mi RNA, lnc RNA and m RNA about the results of experimental data. Selecting and verified lnc RNA, mi RNA and m RNA target gene data, screening the differential expression data and processingredundant data for data of data result set, then use the adjacency matrix and graph theory to established primary regulatory network model. Because of the complexity of the primary regulatory network model, In this paper, we use the distance similarity of K-means algorithm to excavate primary regulatory network model. Finally, analysis the enrichment of process and study the role of core regulatory network module between each gene.With the study of the regulatory networks base on m RNA,lnc RNA and mi RNA, find the possible regulation of tthe mutual regulate relationship between the m RNA, mi RNA, lnc RNA and mi RNA. Therefore, the regulatory network not only reveals the value of bioinformatics research, and reveals the significant impact on human physiology. The present study of regulatory networks may find new ways to study the drug resistance of gastric cancer in the future, and has a certain significance in genomics, you can also apply this method to test for cancer or other more complex disease research.