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系统分析暗基因和伪装基因揭示了隐藏在明处的与疾病相关的基因。

Systematic analysis of dark and camouflaged genes reveals disease-relevant genes hiding in plain sight.

机构信息

Department of Neuroscience, Mayo Clinic, Jacksonville, FL, 32224, USA.

Mayo Clinic Graduate School of Biomedical Sciences, Jacksonville, FL, 32224, USA.

出版信息

Genome Biol. 2019 May 20;20(1):97. doi: 10.1186/s13059-019-1707-2.

Abstract

BACKGROUND

The human genome contains "dark" gene regions that cannot be adequately assembled or aligned using standard short-read sequencing technologies, preventing researchers from identifying mutations within these gene regions that may be relevant to human disease. Here, we identify regions with few mappable reads that we call dark by depth, and others that have ambiguous alignment, called camouflaged. We assess how well long-read or linked-read technologies resolve these regions.

RESULTS

Based on standard whole-genome Illumina sequencing data, we identify 36,794 dark regions in 6054 gene bodies from pathways important to human health, development, and reproduction. Of these gene bodies, 8.7% are completely dark and 35.2% are ≥ 5% dark. We identify dark regions that are present in protein-coding exons across 748 genes. Linked-read or long-read sequencing technologies from 10x Genomics, PacBio, and Oxford Nanopore Technologies reduce dark protein-coding regions to approximately 50.5%, 35.6%, and 9.6%, respectively. We present an algorithm to resolve most camouflaged regions and apply it to the Alzheimer's Disease Sequencing Project. We rescue a rare ten-nucleotide frameshift deletion in CR1, a top Alzheimer's disease gene, found in disease cases but not in controls.

CONCLUSIONS

While we could not formally assess the association of the CR1 frameshift mutation with Alzheimer's disease due to insufficient sample-size, we believe it merits investigating in a larger cohort. There remain thousands of potentially important genomic regions overlooked by short-read sequencing that are largely resolved by long-read technologies.

摘要

背景

人类基因组包含“暗区”,即使用标准短读测序技术无法充分组装或比对的基因区域,这使得研究人员无法在这些可能与人类疾病相关的基因区域中识别突变。在这里,我们确定了那些可读取reads 数量较少的区域,将其称为暗区,以及那些对齐不明确的区域,称为伪装区。我们评估了长读长或连接读取技术在这些区域的解析能力。

结果

基于标准的全基因组 Illumina 测序数据,我们在 6054 个与人类健康、发育和生殖相关的重要途径的基因体中,鉴定出 36794 个暗区。在这些基因体中,8.7%完全为暗区,35.2%的暗区比例大于等于 5%。我们在 748 个基因的蛋白质编码外显子中发现了暗区。10x Genomics、PacBio 和 Oxford Nanopore Technologies 的连接读取或长读测序技术分别将暗的蛋白质编码区域减少到约 50.5%、35.6%和 9.6%。我们提出了一种算法来解决大多数伪装区的问题,并将其应用于阿尔茨海默病测序计划。我们在阿尔茨海默病基因 CR1 中发现了一个罕见的十核苷酸移码缺失,该缺失在疾病病例中存在,但在对照组中不存在。

结论

虽然由于样本量不足,我们无法正式评估 CR1 移码突变与阿尔茨海默病的关联,但我们认为值得在更大的队列中进行研究。仍有数千个可能重要的基因组区域被短读测序忽略,而这些区域在很大程度上可以通过长读技术解决。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cbfa/6526621/8f9dd58b0ae4/13059_2019_1707_Fig1_HTML.jpg

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