A Review of Variant Identification Algorithms and Optimization Methods

doi:10.11871/jfdc.issn.2096-742X.2026.05.011

Abstract

Abstract:

[Objective] This paper systematically reviews the application status and optimization methods of variant identification algorithms, summarizes the current research progress of the algorithms, and discusses the future research directions. [Methods] By conducting in-depth research on the variant identification tools and their optimization methods over the past five years, this study focuses on exploring the research progress of key technologies, such as the workflow, core functions, accuracy, and performance optimization methods of these tools. [Results] Current research has established relatively complete and efficient workflows for SNP and Indel detection, and performance optimization methods based on parallel computing and hardware acceleration have been developed and are still being improved. However, existing software has limitations in general applicability, and the accuracy of structural variant identification remains insufficient, making it difficult to detect all structural variants. [Conclusion] This paper systematically analyzes the key technologies and optimization methods of the variant identification algorithm tools over the past five years. In the future, variant identification should focus on structural variation detection, improving methods for multi-method collaborative work, and solve the problem of insufficient versatility of the variant identification tools.

Key words: variant identification, parallel computing, heterogeneous optimization, FPGA optimization, deep learning

RUAN Jiahui, FENG Yangde, LI Ruilin. A Review of Variant Identification Algorithms and Optimization Methods[J]. Frontiers of Data and Computing, 2026, 8(5): 134-152, https://cstr.cn/32002.14.jfdc.CN10-1649/TP.2026.05.011.

Figures/Tables 7

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Table 3

Comparison Table of Variant Identification Optimization Methods"

核心优化方案	适用场景	优点	缺点	对应文献
PE环结构	中等长度序列变异分析、需灵活扩展的计算场景	灵活性和可扩展性好，便于适配不同数据规模，时间复杂度O（N*L²/P）	对于极长序列，数据传输和存储开销较大	[28]
波前计算模式	GPU加速的并行计算场景、内存受限的分析任务	减少内核内存占用，实现高效并行计算，时间复杂度O(N*L/W)	性能存在波动，对GPU内存带宽要求较高	[54]
非合作结构	高计算密度的变异分析、可扩展性要求高的场景	提高计算效率，良好的可扩展性，时间复杂度为O（N*L²/P）	对内存带宽需求较高	[55]
动态内存交换方法	GPU内存资源紧张的大规模数据处理场景	充分利用GPU内存资源，提高内存利用率，时间复杂度为O（N*L）	实现复杂度高，内存交换带来额外开销	[56]
深度学习端到端判别	复杂区域变异检测、难例变异挖掘	可自动学习变异特征，对复杂测序信号处理能力强；能有效挖掘传统算法遗漏的难例变异，时间复杂度为O(NCk²)	需大量标注数据训练模型；成本较高	[11]
扩展Manta算法	染色体结构变异检测	结构变异检测灵敏度高；可精准识别传统方法难检测的染色体大片段变化，时间复杂度为O(RL+KM²)	对短读长数据的复杂结构变异解析能力仍有局限	[21]
GATK最佳实践流程兼容与加速	现有基因组分析流程、标准化分析场景	兼容成熟流程，降低用户迁移成本；保留标准分析结果的可比性	受限于GATK原有框架，部分自定义优化难以实现；对非标准流程适配性差	[8]
大规模队列联合调用可扩展性设计	生物银行级大规模样本分析、群体遗传学研究	支持高通量分析；大规模数据处理速度快	任务调度复杂，易出现资源竞争；对硬件集群规模要求较高	[9]

Table 3

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变异类型	准确性指标（HG002样本）	对比工具优势
SNV	F1值99.85%	错误数比DeepVariant+BWA减少58.7%，比GATK+BWA减少83%
indel（2-50 bp）	F1值99.80%	在CMRG区域F1值98.64%，比Giraffe+DeepVariant高0.54%
结构变异（插入）	F1值76.90%	是Manta（34.90%）的2.2倍，Delly（4.70%）的16.4倍
结构变异（缺失≥50 bp）	F1值82.60%	比Manta（70.80%）高11.8%，比Lumpy（66.80%）高15.8%
CNV（1-5 kb）	F1值92.60%	是CNVnator（39.20%）的2.4倍
STR（重叠区域）	F1值96.72%	比GangSTR（69.86%）高26.86%

工具名称	测序平台	覆盖度	使用样本	SNP（精度/召回率/F1分数）	Indel（精度/召回率/F1分数）	优势区域	参考文献
Clair3	ONT	50×	HG003样本	99.67%/ 99.60%/-	90.86% /64.73%/-	复杂区域，低覆盖、大规模检测	[39]
PEPPER-Margin-DeepVariant	ONT	50×	HG003样本	99.61%/ 99.63%/-	87.62%/ 57.42%/-	首次实现ONT-SNP精度超Illumina短读段，特定复杂区域，单倍型定相	[41]
DeepVariant	Illumina	35×	HG003样本	99.82%/ 99.38%/-	99.74%/ 99.39%/	Indel检测精度和召回率最优；碱基替换错误分布更随机，无明显偏向性	[44] [45]
DeepVariant	Illumina	30×	HG001样本	99.23%/ 96.89%/ 98.05%	96.74%/ 92.11%/ 94.37%	Indel检测精度和召回率最优；碱基替换错误分布更随机，无明显偏向性	[44] [45]
GATK 4.0	Illumina	30×	HG001样本	98.61%/ 97.04%/ 97.82%	94.83%/ 86.02%/ 90.21%	SNP召回率略高于DeepVariant；群体规模分析中常用的变异质量归一化支持更成熟	[44]