In the detection of genome variation, the research on the internal correlation of reference genome is deepening; the detection of variation in genome sequence has become the focus of research, and it has also become an effective path to find new genes and new functional proteins. The targeted sequencing sequence is used to sequence the exon region of a specific gene in cancer gene detection, and the sequencing depth is relatively large. Traditional alignment algorithms will lose some sequences, which will lead to inaccurate mutation detection. This paper proposes a mutation detection algorithm based on feedback fast learning neural network position index. By establishing a position index relationship for ACGT in the DNA sequence, the subsequence is decomposed into the position relationship of different subsequences corresponding to the main sequence. The positional relationship of the subsequence in the main sequence is determined by the positional relationship. Analyzing SNP and InDel mutations, even structural mutations, through the position correlation of sequences has the advantages of high precision and easy implementation by personal computers. The feedback fast learning neural network is used to verify whether there is a linear relationship between two or more positions. Experimental results show that the mutation points detected by position index are more than those detected by Bcftools, Freebye, Vanscan2, and Gatk.

中文翻译：

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基于反馈快速学习神经网络的基因位置索引突变检测算法


在基因组变异检测中，参考基因组内部相关性研究不断深入；基因组序列变异的检测已成为研究的热点，也成为寻找新基因和新功能蛋白的有效途径。靶向测序序列用于癌症基因检测中特定基因的外显子区域的测序，测序深度比较大。传统的比对算法会丢失一些序列，从而导致突变检测不准确。本文提出一种基于反馈快速学习神经网络位置索引的突变检测算法。通过建立ACGT在DNA序列中的位置索引关系，将子序列分解为主序列对应的不同子序列的位置关系。由位置关系确定子序列在主序列中的位置关系。通过序列的位置相关性来分析SNP和InDel突变，甚至结构突变，具有精度高、易于通过个人计算机实现的优点。反馈快速学习神经网络用于验证两个或多个位置之间是否存在线性关系。实验结果表明，位置索引检测到的突变点比Bcftools、Freebye、Vanscan2、Gatk检测到的突变点要多。