Introduction

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.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.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.

Publications

  1. Tangram: a comprehensive toolbox for mobile element insertion detection.
    Cite this
    Wu J, Lee WP, Ward A, Walker JA, Konkel MK, Batzer MA, Marth GT, 2014-09-01 - BMC genomics

Credits

  1. Jiantao Wu
    Developer

  2. Wan-Ping Lee
    Developer

  3. Alistair Ward
    Developer

  4. Jerilyn A Walker
    Developer

  5. Miriam K Konkel
    Developer

  6. Mark A Batzer
    Developer

  7. Gabor T Marth
    Investigator

    Department of Human Genetics and USTAR Center for Genetic Discovery, University of Utah

Community Ratings

UsabilityEfficiencyReliabilityRated By
0 user
Sign in to rate
Summary
AccessionBT005698
Tool TypeApplication
Category
PlatformsLinux/Unix
TechnologiesC, C++
User InterfaceTerminal Command Line
Download Count0
Submitted ByGabor T Marth