Tangram: a comprehensive toolbox for mobile element insertion detection

BMC Genomics

Gabor T Marth¹  Mark A Batzer³  Miriam K Konkel³  Jerilyn A Walker³  Alistair Ward²  Wan-Ping Lee²  Jiantao Wu² 

[1] Department of Human Genetics and USTAR Center for Genetic Discovery, University of Utah, Salt Lake City, Utah, USA;Department of Biology, Boston College, Chestnut Hill, MA, USA;Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, USA

关键词: High-throughput sequencing;    SVA;    Alu;    L1;    Endogenous retrovirus;    Retrotransposon;    Mobile element insertion;    Structural variation;   

Others  :  1139578
DOI  :  10.1186/1471-2164-15-795

 received in 2014-02-28, accepted in 2014-09-03,  发布年份 2014

【 摘 要 】

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.

【 授权许可】

   
2014 Wu et al.; licensee BioMed Central Ltd.

【 图 表 】

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