With the development of next-generation sequencing technology and the reduction of sequencing costs, whole-genome sequencing, especially exome sequencing, has been widely used in pathogenic gene discovery and mutation detection. With the increase in sequencing samples and the increase in the scope and depth of sequencing, more and more nucleotide variations are detected. Among them, there are many mutations that affect the splicing of RNA. Gene mutations that affect RNA splicing mainly include: mutations leading to the loss of core regulatory elements, mutations to produce new linker sequences, and splicing regulatory sequence mutations that affect RNA splicing efficiency. Although mutation detection using genomic DNA can detect mutations in splicing core elements, mutations that lead to abnormal splicing may be missed. For example, many mutations (including nonsense mutations, missense mutations, and synonymous mutations) may cause abnormal RNA splicing by changing the regulatory sequence. These mutations may also exert pathogenic effects by affecting RNA splicing. Therefore, it is very important to quickly and effectively find and confirm abnormal splicing mutations.
With the rapid development of artificial intelligence, artificial intelligence method models have also been used in splicing related genetic variants analysis. So as to speeds up the identification and analysis of splicing mutations. For example, a computational model of splicing was learned using a Bayesian machine learning algorithm, with extreme care exercised to prevent overfitting.
Fig 1. The human splicing code. (Hui Y. X, et al. 2015)
With the development of splicing mutation research, various prediction software and algorithm models are used for mutation analysis. Because different software is based on different algorithms, there are certain limitations. In addition to providing artificial intelligence-related analysis methods, Protheragen will also provide comprehensive multi-software joint analysis based on customer data to maximize the accuracy of prediction results. Our data analysis service process is as follows:
CD ComputaBio provides splicing related genetic variants data analysis based on artificial intelligence methods. The application of artificial intelligence methods such as Bayesian confidence estimate has improved the prediction accuracy of splicing mutations to a certain extent. In addition, we also provides different algorithm model and software (such as Splice-Site Analyzer Tool, SpliceView, ESEfinder and LeafCutter, etc) for splicing related genetic variants data analysis according to customer data. For data analysis, we provide one-stop services, if you have any questions, please feel free to contact us, we will provide you with satisfactory data analysis reports.
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