Fusion Gene Chip And DNA Sequencing Data To Research Differences In Gene Expression

With the advances in information science and technology, experimental method used toobtain biological information has been changed a lot. As the classic bioinformatics detectiontechnology, gene chip and high-throughput sequencing technology have entered in a maturestage. They have been successfully applied in genomics and many other areas and getamazing results. However, these results are obtained by a single sequencing technology. Asfor some researches, it will get better results if use a combination of two methods. In the paper,from the basic detection principle, to find each other’s strengths and weaknesses of these twoinformation technologies in detection of gene expression and then do the fusion analysis, themajor work are as follows:1. First outlined the background and significance integration of gene chips and DNAhigh-throughput sequencing technology. Then introduce the application and developmentstatus of two technologies.2. To analysis gene basic information and tell about the concept of the eukaryotic andprokaryotic gene transcription and expression. By understanding the nature of chip and DNAhigh-throughput sequencing to find its advantages and disadvantages and analysis itscomplementary, then to provide a basis for building the integration model.3. The characteristics of commonly used fusion method (serial and parallel integrationmethod fusion) for analysis. Since the ordinary feature fusion method can not suitable for thedata object that this paper has to deal with. Therefore, this paper presents the integrationmodel based on correlation analysis method. When this model combined the data of the twotechnologies, it can also correct high-throughput DNA sequencing data, by this way, it can getmore accurate data and better test results.4. Using the integration method proposed in this paper to generate the data, and doanalysis differences in gene expression. Firstly, to introduce the basic principles of theanalysis model and implementation steps, then for maximize the probability of the model, wemake a further analysis. After a brief introduction to particle swarm optimization algorithm,we select the particle swarm optimization algorithm for the following analysis. Because thisstep is difficult to strike a precise analytical solution and it is very complex to solve process.Through specific experimental data to validate the integration model that proposed by thispaper. By R language programming to realize the method, and the results verify thedifferences in gene expression analysis method that based on fusion data of gene chip and high-throughput sequencing is reasonable.