Accurate detections of the heterozygous SNPs with rice genomic data and prediction of de novo spontaneous mutation rate.

IF 4.4 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
Elias George Balimponya, Maria Stefanie Dwiyanti, Koichi Yamamori, Shuntaro Sakaguchi, Yoshitaka Kanaoka, Yohei Koide, Yuji Kishima
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引用次数: 0

Abstract

Background: The use of Illumina sequencing technologies has enabled the identification and removal of mutations in various plant species. However, the Illumina sequencing method requires a considerable amount of data to ensure its integrity and quality due to the enormous number of false positives. This study aimed to explore an effective genomic data analysis for the detection of heterozygous variant (HV) in rice varieties.

Results: We compared the accuracy of four combinations of mapping tools and variant calling pipelines and selected BWA-MEM2 with GATK4.3 HaplotypeCaller. To detect heterozygous de novo polymorphisms such as HVs in the three different rice varieties (Nipponbare, Kitaake, and Hinohikari), we adopted the following cost-saving procedures; secondary references were created in Nipponbare and Kitaake, and generation-based comparison was performed in Hinohikari. The similar HVs were estimated by the three varieties to range from 2.55814 × 10-8 to 4.41860 × 10-8, with an average of 3.10278 × 10-8 per nucleotide in a single rice plant, a rate consistent with observations in other organisms. Of 107 HVs identified in all eight plant samples, nine were found to be non-synonymous, resulting in an average of one non-synonymous HV per plant in a single generation.

Conclusions: We have developed a methodology for the detection of true positive HVs within Illumina sequencing techniques. This system removed false positive HVs, allowing for the estimation of true positive HVs and, consequently, the estimation of the mutation rate. The study outlines a clear, step-by-step procedure that can be employed to detect true HVs in different organisms.

杂合snp与水稻基因组数据的准确检测及新生自发突变率的预测。
背景:Illumina测序技术的使用已经能够识别和去除各种植物物种的突变。然而,由于大量的假阳性,Illumina测序方法需要大量的数据来确保其完整性和质量。本研究旨在探索一种检测水稻品种杂合变异(HV)的有效基因组数据分析方法。结果:我们比较了四种定位工具和变异调用管道组合的准确性,并选择了BWA-MEM2与GATK4.3 HaplotypeCaller。为了检测三种不同水稻品种(日本裸、北竹和日光)的杂合新生多态性(HVs),我们采用了以下节省成本的方法;在Nipponbare和Kitaake建立了二级参考文献,在日野光进行了基于代的比较。3个品种的相似HVs值为2.55814 × 10-8 ~ 4.41860 × 10-8,平均每个核苷酸的HVs值为3.10278 × 10-8,与其他生物的观察值一致。在所有8个植物样本中鉴定的107个HV中,9个被发现是非同义的,导致平均每一代植物中有一个非同义HV。结论:我们已经开发了一种在Illumina测序技术中检测真阳性HVs的方法。该系统去除假阳性HVs,允许估计真阳性HVs,因此,估计突变率。这项研究概述了一个清晰、循序渐进的程序,可以用来检测不同生物体中真正的艾滋病毒。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Plant Methods
Plant Methods 生物-植物科学
CiteScore
9.20
自引率
3.90%
发文量
121
审稿时长
2 months
期刊介绍: Plant Methods is an open access, peer-reviewed, online journal for the plant research community that encompasses all aspects of technological innovation in the plant sciences. There is no doubt that we have entered an exciting new era in plant biology. The completion of the Arabidopsis genome sequence, and the rapid progress being made in other plant genomics projects are providing unparalleled opportunities for progress in all areas of plant science. Nevertheless, enormous challenges lie ahead if we are to understand the function of every gene in the genome, and how the individual parts work together to make the whole organism. Achieving these goals will require an unprecedented collaborative effort, combining high-throughput, system-wide technologies with more focused approaches that integrate traditional disciplines such as cell biology, biochemistry and molecular genetics. Technological innovation is probably the most important catalyst for progress in any scientific discipline. Plant Methods’ goal is to stimulate the development and adoption of new and improved techniques and research tools and, where appropriate, to promote consistency of methodologies for better integration of data from different laboratories.
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