终端限制片段分析工具在大规模基因组分析中的应用比较。

Comparative Application of Terminal Restriction Fragment Analysis Tools to Large-Scale Genomic Assays.

机构信息

Institute of Fundamental Medicine and Biology, Kazan Federal University, Kazan 420008, Republic of Tatarstan, Russia.

Department of Biological Sciences, College of Science, Marshall University, Huntington, WV 25701, USA.

出版信息

Int J Mol Sci. 2023 Dec 6;24(24):17194. doi: 10.3390/ijms242417194.

Abstract

The analysis of telomere length is an important component of many studies aiming to characterize the role of telomere maintenance mechanisms in cellular lifespan, disease, or in general chromosome protection and DNA replication pathways. Several powerful methods to accurately measure the telomere length from Southern blots have been developed, but their utility for large-scale genomic studies has not been previously evaluated. Here, we performed a comparative analysis of two recently developed programs, TeloTool and WALTER, for the extraction of mean telomere length values from Southern blots. Using both software packages, we measured the telomere length in two extensive experimental datasets for the model plant , consisting of 537 natural accessions and 65 T-DNA (transfer DNA for insertion mutagenesis) mutant lines in the reference Columbia (Col-0) genotype background. We report that TeloTool substantially overestimates the telomere length in comparison to WALTER, especially for values over 4500 bp. Importantly, the TeloTool- and WALTER-calculated telomere length values correlate the most in the 2100-3500 bp range, suggesting that telomeres in this size interval can be estimated by both programs equally well. We further show that genome-wide association studies using datasets from both telomere length analysis tools can detect the most significant SNP candidates equally well. However, GWAS analysis with the WALTER dataset consistently detects fewer significant SNPs than analysis with the TeloTool dataset, regardless of the GWAS method used. These results imply that the telomere length data generated by WALTER may represent a more stringent approach to GWAS and SNP selection for the downstream molecular screening of candidate genes. Overall, our work reveals the unanticipated impact of the telomere length analysis method on the outcomes of large-scale genomic screens.

摘要

端粒长度分析是许多旨在描述端粒维持机制在细胞寿命、疾病或一般染色体保护和 DNA 复制途径中的作用的研究的重要组成部分。已经开发了几种从 Southern 印迹中准确测量端粒长度的强大方法,但它们在大规模基因组研究中的实用性尚未得到评估。在这里,我们对最近开发的两种程序 TeloTool 和 WALTER 进行了比较分析,以从 Southern 印迹中提取平均端粒长度值。使用这两个软件包,我们测量了模式植物的两个广泛的实验数据集的端粒长度,该植物包含 537 个自然品系和 65 个 T-DNA(转座 DNA 用于插入诱变)突变体系,在参考哥伦比亚(Col-0)基因型背景下。我们报告说,与 WALTER 相比,TeloTool 大大高估了端粒长度,特别是对于超过 4500 bp 的值。重要的是,TeloTool 和 WALTER 计算的端粒长度值在 2100-3500 bp 范围内相关性最强,这表明这一大小范围内的端粒可以由这两个程序同样很好地估计。我们进一步表明,使用来自这两种端粒长度分析工具的数据集进行全基因组关联研究可以同样很好地检测到最显著的 SNP 候选物。然而,无论使用哪种 GWAS 方法,使用 WALTER 数据集进行的 GWAS 分析始终比使用 TeloTool 数据集检测到的显著 SNP 少。这些结果表明,WALTER 生成的端粒长度数据可能代表了一种更严格的 GWAS 和 SNP 选择方法,用于候选基因的下游分子筛选。总的来说,我们的工作揭示了端粒长度分析方法对大规模基因组筛选结果的意外影响。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4227/10742804/440760a662f4/ijms-24-17194-g001.jpg

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