Review of Genomic Microsatellite Status Detection Based on Machine Learning

doi:10.11871/jfdc.issn.2096-742X.2021.03.011

Abstract

Abstract:

[Objective] This paper discusses the application and future research direction of machine learning in microsatellite status detection. [Scope of the literature] We collected the related literature of microsatellite status detection methods.[Methods] Firstly, the significance of microsatellite status detection and common detection methods are briefly introduced. Secondly, the current mainstream detection methods based on machine learning are introduced in detail. Finally, perspective future research direction of machine learning in the field of microsatellite status detection is presented.[Results] The detection methods based on machine learning can iteratively learn from massive sequencing data and discern key features that affect microsatellite instability. They can achieve accurate prediction results. [Limitations] The data types used by the detection methods are different, so we cannot compare the detection methods within the same dataset. [Conclusions] Machine learning has been widely used in microsatellite status detection. Improving the applicability of detection methods and detecting microsatellite status from peripheral blood samples are the future research directions of machine learning in this field.

Key words: machine learning, genome, microsatellite instability, sequencing data, key features

ZHANG Shuying,HAN Xinyin,HE Xiaoyu,YUAN Danyang,LUAN Haijing,LI Ruilin,HE Jiayin,NIU Beifang. Review of Genomic Microsatellite Status Detection Based on Machine Learning[J]. Frontiers of Data and Computing, 2021, 3(3): 126-135.

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方法	测序方法	机器学习算法	准确率（%）	灵敏性（%）	特异性（%）
MSIseq^[38]	WES	决策树	98.8	-	-
MSIpred^[39]	WES	支持向量机	98.3	93.6	99.6
MOSAIC^[40]	WES	决策树	96.6	95.8	97.6
MIRMMR^[41]	-	逻辑回归	93.8	91.9	94.2
MIAmS^[42]	TS（amplicon panel）	支持向量机（默认）	100.0	-	-

序号	特征	含义
1	T.sns（SNP）	样本中SNS比率
2	S.sns（SNP_R）	MS序列中SNS比率
3	T.ind（INDEL）	样本中indel比率
4	S.ind（INDEL_R）	MS序列中indel比率
5	T（t_mutation）	样本中突变比率
6	S（t_mutation_R）	MS序列中突变比率
7	S.sns/T.sns（SNP_R/SNP）	-
8	S.ind/T.ind（INDEL_R/ INDEL）	-
9	S/T（t_mutation_R/t_ mutation）	-
10	Frame_Shift_Del	导致ORF偏移的删除比率
11	Frame_Shift_Ins	导致ORF偏移的插入比率
12	In_Frame_Del	ORF没有偏移的删除比率
13	In_Frame_Ins	ORF没有偏移的插入比率
14	Missense_Mutation	错义突变比率
15	Nonsense_Mutation	无义突变比率
16	Silent	沉默突变比率
17	Splice_Site	剪接位点的突变比率
18	3’UTR	3’UTR区域突变比率
19	3’Flank	3’Flank区域突变比率
20	5’UTR	5’UTR区域突变比率
21	5’Flank	5’Flank区域突变比率
22	Intron	内含子区域突变比率

序号	特征	含义
1	peak_avg	T相对于N出现的新等位基因个数的平均值
2	peak_var	T相对于N出现的新等位基因个数的方差
3	num_unstable	不稳定的MS位点个数
4	prop_unstable	不稳定的MS位点占比