Du Yan, Luo Shanwei, Yu Lixia, Cui Tao, Chen Xia, Yang Jiangyan, Li Xin, Li Wenjian, Wang Jufang, Zhou Libin
Biophysics Group, Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou 730000, PR China.
Biophysics Group, Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou 730000, PR China; College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, PR China.
Mutat Res. 2018 Jan;807:21-30. doi: 10.1016/j.mrfmmm.2017.12.001. Epub 2017 Dec 9.
Heavy-ion beam irradiation is a powerful physical mutagen that has been used to create numerous mutant materials in plants. These materials are an essential resource for functional genomics research in the post-genome era. The advent of Next-Generation Sequencing (NGS) technology has promoted the study of functional genomics and molecular breeding. A wealth of information can be gathered from whole genome re-sequencing; however, understanding the molecular mutation profile at genome wide, as well as identifying causal genes for a given phenotype are big challenging issues for researchers. The huge outputs created by NGS make it difficult to capture key information. It is worthy to explore an effective and efficient data-sieving strategy for mutation scanning at whole genome scale. Re-sequencing data from one laboratory wild type (Columbia) and eleven MArabidopsis thaliana lines derived from carbon-ion beam irradiation were used in present study. Both the number and different combinations of samples used for analysis affected the sieving results. The result indicated that using six samples was sufficient to filter out the shared mutation (background interference) sites as well as to identify the true mutation sites in the whole genome. The final number of candidate mutation sites could be further narrowed down by combining traditional rough map-based cloning. Our results demonstrated the feasibility of a parallel sequencing analysis as an efficient tool for the identification of mutations induced by carbon-ion beam irradiation. For the first time, we presented different analysis strategies for handling massive parallel sequencing data sets to detect the mutations induced by carbon-ion beam irradiation in Arabidopsis thaliana with low false-positive rate, as well as to identify the causative nucleotide changes responsible for a mutant phenotype.
重离子束辐照是一种强大的物理诱变剂，已被用于在植物中创建大量突变材料。这些材料是后基因组时代功能基因组学研究的重要资源。新一代测序（NGS）技术的出现推动了功能基因组学和分子育种的研究。通过全基因组重测序可以收集大量信息；然而，在全基因组范围内了解分子突变图谱以及识别给定表型的因果基因，对研究人员来说是重大挑战。NGS产生的海量数据使得难以捕捉关键信息。探索一种有效且高效的全基因组规模突变扫描数据筛选策略是值得的。本研究使用了来自一个实验室野生型（哥伦比亚）和11个源自碳离子束辐照的拟南芥品系的重测序数据。用于分析的样本数量和不同组合都会影响筛选结果。结果表明，使用六个样本足以滤除共享突变（背景干扰）位点，并识别全基因组中的真正突变位点。通过结合传统的基于粗略图谱的克隆，候选突变位点的最终数量可以进一步缩小。我们的结果证明了平行测序分析作为鉴定碳离子束辐照诱导突变的有效工具的可行性。我们首次提出了不同的分析策略，用于处理大规模平行测序数据集，以检测拟南芥中碳离子束辐照诱导的低假阳性率突变，并识别导致突变表型的因果核苷酸变化。
