技术与方法

基于全局最小残差法快速分析混合STR图谱

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  • 1.陕西师范大学计算机科学学院,西安 710119
    2.公安部鉴定中心,北京 100038
    3.波士顿大学生物系,波士顿 02215,美国
李鑫,硕士研究生,专业方向:生物信息。E-mail: 15514872144@163.com

收稿日期: 2023-06-15

  修回日期: 2023-08-14

  网络出版日期: 2023-08-30

基金资助

国家自然科学基金项目(12271324);陕西省自然科学基金重点项目(2022ZJ-39);法医遗传学公安部重点实验室开放课题(2021FGKFKT07)

Rapid analyzing mixed STR profiles based on the global minimum residual method

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  • 1. School of Computer Science, Shaanxi Normal University, Xi'an 710119, China
    2. The institute of Forensic Science, Ministry of Public Security, Beijing 100038, China
    3. Department of Biology, Boston University, Boston 02215, USA

Received date: 2023-06-15

  Revised date: 2023-08-14

  Online published: 2023-08-30

Supported by

National Natural Science Foundation of China(12271324);Key Program of Shaanxi Natural Science Foundation(2022ZJ-39);Open Projects of the Key Laboratory of Forensic Genetics of the Ministry of Public Security(2021FGKFKT07)

摘要

在法医DNA分析领域,混合短串联重复序列(short tandem repeats,STR)图谱的分析一直是研究难点。当前,国内主要依靠法医进行人工分析,不仅效率低下,分析结果还存在着主观性偏好,难以满足日益增长的STR图谱分析的需求。本文提出一种新的混合STR图谱分析方法——全局最小残差法,不仅可以计算出分析结果,还可以预测出每个组分的混合比例。该方法首先给混合比例赋予了新的定义,然后对等位基因模型进行优化,进而综合考虑STR图谱中的所有基因座,将每个基因座的残差值进行累加求和,选择累加和最小的混合比例作为推断结果,并使用灰狼优化算法快速寻找混合比例的最优值。对于二组分STR图谱,全局最小残差法能够兼顾分析的准确性和分析速度,有利于实现大量的图谱分析。本文提出的算法在实际应用中取得了不错的效果,具有较高的应用价值,可为混合STR图谱分析领域的研究提供新的解决方案。

本文引用格式

李鑫, 范虹, 赵兴春, 范晓诺, 姚若侠 . 基于全局最小残差法快速分析混合STR图谱[J]. 遗传, 2023 , 45(10) : 933 -944 . DOI: 10.16288/j.yczz.23-101

Abstract

The analysis of mixed short tandem repeat (STR) profiles has been long considered as a difficult challenge in the forensic DNA analysis. In the context of China, the current approach to analyze mixed STR profiles depends mostly on forensic manual method. However, besides the inefficiency, this technique is also susceptible to subjective biases in interpreting analysis results, which can hardly meet up with the growing demand for STR profiles analysis. In response, this study introduces an innovative method known as the global minimum residual method, which not only predicts the proportion of each contributor within a mixture, but also delivers accurate analysis results. The global minimum residual method first gives new definitions to the mixture proportion, then optimizes the allele model. After that, it comprehensively considers all loci present in the STR profile, accumulates and sums the residual values of each locus and selects the mixture proportion with the minimum accumulative sum as the inference result. Furthermore, the grey wolf optimizer is also employed to expedite the search for the optimal value. Notably, for two-person STR profiles, the high accuracy and remarkable efficiency of the global minimum residual method can bring convenience to realize extensive STR profile analysis. The optimization scheme established in this research has exhibited exceptional outcomes in practical applications, boasting significant utility and offering an innovative avenue in the realm of mixed STR profile analysis.

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