Identification of Mutation Combinations in Genome-Wide Association Studies: Application for Mycobacterium tuberculosis

IF 0.7 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS PATTERN RECOGNITION AND IMAGE ANALYSIS Pub Date : 2024-07-04 DOI:10.1134/s1054661824700044
Yu-Xiang Chen, A. M. Andrianov, A. V. Tuzikov
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Abstract

In genome-wide association studies, combinations of single nucleotide polymorphisms are considered to be more effective than individual mutations in linking genes to traits. Clearly, finding the most relevant combinations from tens of thousands of these mutations associated with a trait is a complicated combinatorial problem. To achieve the higher prediction performance, improve computational efficiency and results interpretation, we proposed three algorithms for searching combinations of individual mutations and applied these algorithms to 3178 samples of Mycobacterium tuberculosis strains for predicting their drug resistance to 20 drugs. The single nucleotide polymorphisms associated with drug resistance were identified in the Mycobacterium tuberculosis genome using the single-marker test, and the combinations of individual mutations were searched using the multimarker test. The data were compared with those predicted by the widely recognized Mykrobe and TB-profiler software. Comparative analysis of the results obtained showed that, excepting for ofloxacin, the combinations of individual mutations found by our algorithms for the second-line drugs have some advantages in prediction accuracy.

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识别全基因组关联研究中的突变组合:结核分枝杆菌的应用
摘要 在全基因组关联研究中,单核苷酸多态性的组合被认为比单个突变更能有效地将基因与性状联系起来。显然,从数以万计与性状相关的突变中找出最相关的组合是一个复杂的组合问题。为了实现更高的预测性能,提高计算效率和结果解释能力,我们提出了三种搜索单个突变组合的算法,并将这些算法应用于 3178 个结核分枝杆菌菌株样本,预测它们对 20 种药物的耐药性。利用单标记检验在结核分枝杆菌基因组中鉴定了与耐药性相关的单核苷酸多态性,并利用多标记检验搜索了单个突变的组合。这些数据与广泛认可的 Mykrobe 和 TB-profiler 软件预测的数据进行了比较。对所得结果的比较分析表明,除氧氟沙星外,我们的算法为二线药物找到的单个突变组合在预测准确性方面具有一定优势。
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来源期刊
PATTERN RECOGNITION AND IMAGE ANALYSIS
PATTERN RECOGNITION AND IMAGE ANALYSIS Computer Science-Computer Graphics and Computer-Aided Design
CiteScore
1.80
自引率
20.00%
发文量
80
期刊介绍: The purpose of the journal is to publish high-quality peer-reviewed scientific and technical materials that present the results of fundamental and applied scientific research in the field of image processing, recognition, analysis and understanding, pattern recognition, artificial intelligence, and related fields of theoretical and applied computer science and applied mathematics. The policy of the journal provides for the rapid publication of original scientific articles, analytical reviews, articles of the world''s leading scientists and specialists on the subject of the journal solicited by the editorial board, special thematic issues, proceedings of the world''s leading scientific conferences and seminars, as well as short reports containing new results of fundamental and applied research in the field of mathematical theory and methodology of image analysis, mathematical theory and methodology of image recognition, and mathematical foundations and methodology of artificial intelligence. The journal also publishes articles on the use of the apparatus and methods of the mathematical theory of image analysis and the mathematical theory of image recognition for the development of new information technologies and their supporting software and algorithmic complexes and systems for solving complex and particularly important applied problems. The main scientific areas are the mathematical theory of image analysis and the mathematical theory of pattern recognition. The journal also embraces the problems of analyzing and evaluating poorly formalized, poorly structured, incomplete, contradictory and noisy information, including artificial intelligence, bioinformatics, medical informatics, data mining, big data analysis, machine vision, data representation and modeling, data and knowledge extraction from images, machine learning, forecasting, machine graphics, databases, knowledge bases, medical and technical diagnostics, neural networks, specialized software, specialized computational architectures for information analysis and evaluation, linguistic, psychological, psychophysical, and physiological aspects of image analysis and pattern recognition, applied problems, and related problems. Articles can be submitted either in English or Russian. The English language is preferable. Pattern Recognition and Image Analysis is a hybrid journal that publishes mostly subscription articles that are free of charge for the authors, but also accepts Open Access articles with article processing charges. The journal is one of the top 10 global periodicals on image analysis and pattern recognition and is the only publication on this topic in the Russian Federation, Central and Eastern Europe.
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