.png){kind=icon}
返回
Tangram: a comprehensive toolbox for mobile element insertion detection
.png){kind=icon}
OA
AI
DOI：10.1186/1471-2164-15-795.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Background: Mobile elements (MEs) constitute greater than 50% of the human genome as a result of repeated insertion events during human genome evolution. Although most of these elements are now fixed in the population, some MEs, including ALU, L1, SVA and HERV-K elements, are still actively duplicating. Mobile element insertions (MEIs) have been associated with human genetic disorders, including Crohn's disease, hemophilia, and various types of cancer, motivating the need for accurate MEI detection methods. To comprehensively identify and accurately characterize these variants in whole genome next-generation sequencing (NGS) data, a computationally efficient detection and genotyping method is required. Current computational tools are unable to call MEI polymorphisms with sufficiently high sensitivity and specificity, or call individual genotypes with sufficiently high accuracy. Results: Here we report Tangram, a computationally efficient MEI detection program that integrates read-pair (RP) and split-read (SR) mapping signals to detect MEI events. By utilizing SR mapping in its primary detection module, a feature unique to this software, Tangram is able to pinpoint MEI breakpoints with single-nucleotide precision. To understand the role of MEI events in disease, it is essential to produce accurate individual genotypes in clinical samples. Tangram is able to determine sample genotypes with very high accuracy. Using simulations and experimental datasets, we demonstrate that Tangram has superior sensitivity, specificity, breakpoint resolution and genotyping accuracy, when compared to other, recently developed MEI detection methods. Conclusions: Tangram serves as the primary MEI detection tool in the 1000 Genomes Project, and is implemented as a highly portable, memory-efficient, easy-to-use C++ computer program, built under an open-source development model.
Keyword:
Structural variation
Mobile element insertion
Retrotransposon
Endogenous retrovirus
L1
Alu
SVA
High-throughput sequencing
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.7
论文数:
1.9W
被引数:
5.2W
机构
引用论文
The genetic architecture of Down syndrome phenotypes revealed by high-resolution analysis of human segmental trisomies通过对人类节段性三体性的高分辨率分析揭示的唐氏综合症表型的遗传结构
OAAI
OAAI分享收藏The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data基因组分析工具包: 用于分析下一代DNA测序数据的MapReduce框架.png)
GENOME RESEARCHOAAI
GENOME RESEARCHIF5.5
OAAI分享收藏Discovery and genotyping of genome structural polymorphism by sequencing on a population scaleNATURE GENETICSOAAI
NATURE GENETICSIF31.8
OAAI分享收藏
查看更多内容